T10 Iss4 Hjr Radiology

ISSN 2654-1629

OCTOBER - DECEMBER 2025 | Volume 10, Issue 4

J

90 ΧΡΟΝΙΑ

Quarterly Publication by the Hellenic Radiological Society

Elekta Evo

Adapt at your rhythm

More options for creative control over cancer care. Whether your

practice needs simplicity or your patient’s treatment needs change,

choose from a full spectrum of radiotherapy techniques. Elekta Evo

offers unlimited clinical freedom from a single machine.

LADOX240403 _v1 Elekta Evo requires CE mark and is not available in all markets.

Follow us:

Discover

the DR 600 X-ray

Room with Innovative

SmartGrip Technology!

Innovative, high-productivity features meet cutting edge

technology in the fully automated DR 600. The speed and

intelligence of technologies such as MUSICA ® and SmartXR ® , and the

precision and effortless movement of ZeroForce and SmartGrip,

come together in a complete and integrated solution.

ΑΓΚΦΑ-ΓΚΕΒΕΡΤ Μον/πη ΑΕΒΕ

Αχαρνών 481, 143 43 Ν. Χαλκηδόνα

Τηλ.: 210.5706565 Fax: 210.5706600

e-mail: athanasios.tsegkos@agfa.com

Εμπιστοσύνη

με Κάθε Δόση

100+

χώρες 1

40 χρόνια

κληρονομιάς

Αποδεδειγμένο προφίλ

οφέλους-κινδύνου

• >150.000 ασθενείς σε μελέτες παρατήρησης

2-4

• Χαμηλό ποσοστό HSRs* σε όλες τις ηλικίες

5

Υψηλά πρότυπα παραγωγής

Πιστοποίηση

GMP και ISO 9001

Ολοκληρωμένη προσφορά

με τους εγχυτές και τις λύσεις λογισμικού της Bayer

Μάθετε περισσότερα

*Hypersensitivity reactions=αντιδράσεις υπερευαισθησίας

1. Bayer data reported to Health Authorities. PSUR/PBER Ultravist® (Iopromide) (01 JUL 2023 to 30 JUN 2024), August 2024) 2. Nijssen EC, Rennenberg RJ, Nelemans PJ, et al. Prophylactic hydration to protect renal function from intravascular

iodinated contrast material in patients at high risk of contrast-induced nephropathy (AMACING): a prospective, randomised, phase 3, controlled, open-label, non-inferiority trial. Lancet. 2017 Apr 1;389(10076):1312-1322. 3. Chen JY, Liu Y,

Zhou YL, et al. Safety and tolerability of iopromide in patients undergoing cardiac catheterization: real-world multicenter experience with 17,513 patients from the TRUST trial. Int J Cardiovasc Imaging. 2015;31(7):1281-1291. 4. Palkowitsch

P, Bostelamm S, Lengsfeld P. Safety and tolerability of iopromide intravascular use: a pooled analysis of three no-interventional studies in 132,012 patients. Acta Radiologica 2014;55(6):707-714 5. Endrikat J, Chernova J, Gerlinger C, et

al. Risk of Hypersensitivity Reactions to Iopromide in Children and Elderly: An Analysis of 132,850 Patients From 4 Observational Studies and Pharmacovigilance Covering >288 Million Administrations. Invest Radiol. 2022;57(5):318-326

Πριν τη συνταγογράφηση συμβουλευθείτε την Περίληψη των Χαρακτηριστικών του Προϊόντος.

Τρόπος διάθεσης: Περιορισμένη ιατρική συνταγή: ιατρό ειδικότητας Ακτινοδιαγνώστη.

Κάτοχος Άδειας Κυκλοφορίας στην Ελλάδα και Κύπρο:

Bayer Ελλάς ΑΒΕΕ, Αγησιλάου 6-8, 151 23 Μαρούσι Τηλ.:+ 30 210 6187500

Τοπικός αντιπρόσωπος στην Κύπρο: Novagem Ltd, Τηλ. +357 22483858

Τμήμα Ιατρικής Πληροφόρησης: Τηλ. +30 2106187742, Email: medinfo.gr.cy@bayer.com

ULTRAVIST 300 INJ.SOL 62,34%(30%IODINE) BTX1VIALX50ML, Λ.Τ. €19.66


ULTRAVIST 300 INJ.SOL 62,34%(30%IODINE) BTx1BOTTLEx200ML, Λ.Τ. €71.46

ULTRAVIST 370 INJ.SOL 76,9%(37%IODINE) BTX1VIALX50ML , Λ.Τ. €20.03


ULTRAVIST 370 INJ.SOL 76,9%(37%IODINE) BTX1BOTTLEX200ML, Λ.Τ. €71.29

Πριν τη συνταγογράφηση συμβουλευθείτε την Περίληψη των Χαρακτηριστικών

του Προϊόντος. (Παρακαλούμε όπως ανατρέξετε στις επόμενες σελίδες).

PP-ULT-GR-0080-1 July 2025

ΠΕΡΙΛΗΨΗ ΤΩΝ ΧΑΡΑΚΤΗΡΙΣΤΙΚΩΝ ΤΟΥ ΠΡΟΙΟΝΤΟΣ

1. ΟΝΟΜΑΣΙΑ ΤΟΥ ΦΑΡΜΑΚΕΥΤΙΚΟΥ ΠΡΟΙΟΝΤΟΣ Ultravist® 300, Ενέσιμο διάλυμα, 62,34% (30% ιώδιο). Ultravist® 370, Ενέσιμο διάλυμα,

76,9% (37% ιώδιο). 2. ΠΟΙΟΤΙΚΗ ΚΑΙ ΠΟΣΟΤΙΚΗ ΣΥΝΘΕΣΗ Ultravist 300: 1 ml περιέχει 623,4 mg ιοπρομίδης (αντιστοιχεί σε 300 mg ιωδίου),

Ultravist 370: 1 ml περιέχει 768,86 mg ιοπρομίδης (αντιστοιχεί σε 370 mg ιωδίου), Έκδοχο: Κάθε ml περιέχει 0,01109 mmol (αντιστοιχεί σε

0,2549 mg) νατρίου (βλ. Παράρτημα 1), Για τον πλήρη κατάλογο των εκδόχων βλ. παράγραφο 6.1. 3. ΦΑΡΜΑΚΟΤΕΧΝΙΚΗ ΜΟΡΦΗ Ενέσιμο

διάλυμα, Διαυγές, άχρωμο έως υποκίτρινο διάλυμα. Οι φυσικο-χημικές ιδιότητες του Ultravist στις διαφορετικές συγκεντρώσεις είναι οι εξής:

Συγκέντρωση ιωδίου (mg/ml) 300 370

Ωσμωτική γραμμομοριακή περιεκτικότητα (οsm/kg H2O) σε 37°C 0,59 0,77

Ιξώδες (mPa·S)

σε 20°C

σε 37°C

Πυκνότητα (g/ml)

σε 20°C

σε 37°C

8,9

4,7

1,328

1,322

22,0

10,0

1,409

1,399

Τιμή pH 6,5-8,0 6,5-8,0

4. ΚΛΙΝΙΚΕΣ ΠΛΗΡΟΦΟΡΙΕΣ 4.1 Θεραπευτικές ενδείξεις Αυτό το προϊόν είναι μόνο για διαγνωστική χρήση. Για ενίσχυση της σκιαγραφικής

αντίθεσης. Για ενδοαγγειακή χρήση και χρήση σε κοιλότητες του σώματος. Ultravist 300: Eνδοφλέβια πυελογραφία, Αγγειογραφία σπλάχνων,

Αγγειογραφία εγκεφάλου, Αγγειογραφία άκρων, Αξονική τομογραφία, Ψηφιακή αφαιρετική αγγειογραφία, Σκιαγράφηση κοιλοτήτων (με

εξαίρεση τη μυελογραφία, την κοιλιογραφία και την ακτινογραφία των κοιλιών του εγκεφάλου). Για χρήση σε ενήλικες γυναίκες σε ψηφιακή

μαστογραφία με έγχυση σκιαγραφικού για την αξιολόγηση και την ανίχνευση γνωστών ή ύποπτων βλαβών του μαστού, ως συμπληρωματική

εξέταση στη μαστογραφία (με ή χωρίς υπέρηχο) ή ως εναλλακτική της μαγνητικής τομογραφίας (MRI) όταν η μαγνητική τομογραφία αντενδείκνυται

ή δεν είναι διαθέσιμη. Ultravist 370: Eνδοφλέβια πυελογραφία, Αγγειοκαρδιογραφία, Αξονική τομογραφία, Ψηφιακή αφαιρετική

αγγειογραφία, Σκιαγράφηση κοιλοτήτων (με εξαίρεση τη μυελογραφία, την κοιλιογραφία και την ακτινογραφία των κοιλιών του εγκεφάλου).

Για χρήση σε ενήλικες γυναίκες σε ψηφιακή μαστογραφία με έγχυση σκιαγραφικού για την αξιολόγηση και την ανίχνευση γνωστών ή ύποπτων

βλαβών του μαστού, ως συμπληρωματική εξέταση στη μαστογραφία (με ή χωρίς υπέρηχο) ή ως εναλλακτική της μαγνητικής τομογραφίας

(MRI) όταν η μαγνητική τομογραφία αντενδείκνυται ή δεν είναι διαθέσιμη. Το Ultravist δεν ενδείκνυται για ενδοραχιαία χρήση. 4.3 Αντενδείξεις

Δεν υπάρχουν απόλυτες αντενδείξεις για τη χρήση του Ultravist. 4.4 Ειδικές προειδοποιήσεις και προφυλάξεις κατά τη χρήση Για όλες

τις ενδείξεις • Αντιδράσεις υπερευαισθησίας: Το Ultravist μπορεί να συσχετιστεί με αναφυλακτοειδείς αντιδράσεις / αντιδράσεις υπερευαισθησίας

ή άλλες ιδιοσυγκρασιακές αντιδράσεις χαρακτηριζόμενες από καρδιοαγγειακές, αναπνευστικές και δερματικές εκδηλώσεις. Αντιδράσεις

αλλεργικού τύπου που κυμαίνονται από ήπιες έως σοβαρές, συμπεριλαμβανομένου του σοκ, είναι πιθανές (βλ. παράγραφο 4.8 «Ανεπιθύμητες

Ενέργειες»). Οι περισσότερες από αυτές τις αντιδράσεις εμφανίζονται μέσα σε 30 λεπτά από τη χορήγηση. Ωστόσο, μπορεί να

εμφανιστούν όψιμες αντιδράσεις (μετά από ώρες έως μέρες). Ο κίνδυνος για αντιδράσεις υπερευαισθησίας είναι υψηλότερος στην περίπτωση:

- Προηγούμενης αντίδρασης σε σκιαγραφικό μέσο - Ιστορικό βρογχικού άσθματος ή άλλων αλλεργικών διαταραχών. Ιδιαίτερα σε ασθενείς

με γνωστή υπερευαισθησία στο Ultravist ή σε οποιοδήποτε από τα έκδοχά του ή με ιστορικό προηγούμενης αντίδρασης υπερευαισθησίας

σε οποιοδήποτε άλλο ιωδιούχο σκιαγραφικό μέσο, απαιτείται προσεκτική αξιολόγηση της σχέσης κινδύνου/οφέλους, λόγω του αυξημένου

κινδύνου εμφάνισης αντιδράσεων υπερευαισθησίας (συμπεριλαμβανομένων σοβαρών αντιδράσεων). Ωστόσο, οι αντιδράσεις αυτές δεν εμφανίζονται

με σταθερό ρυθμό και η φύση τους δεν μπορεί να προβλεφθεί. Ασθενείς που εμφανίζουν παρόμοιες αντιδράσεις ενόσω λαμβάνουν

αποκλειστές των β-υποδοχέων μπορεί να παρουσιάσουν ανθεκτικότητα στη θεραπευτική αγωγή με αγωνιστές των β-υποδοχέων (βλ.

επίσης παράγραφο 4.5 «Ειδικές προειδοποιήσεις και προφυλάξεις κατά τη χρήση»). Σε περίπτωση σοβαρής αντίδρασης υπερευαισθησίας, οι

ασθενείς με καρδιαγγειακή νόσο είναι περισσότερο επιρρεπείς σε μια σοβαρή ή ακόμα και θανατηφόρο έκβαση. Λόγω της πιθανότητας εμφάνισης

σοβαρών αντιδράσεων υπερευαισθησίας μετά τη χορήγηση, συνιστάται παρακολούθηση του ασθενούς μετά την εξέταση. Πρέπει

να υπάρχει ετοιμότητα για την εφαρμογή επειγόντων μέτρων για όλους τους ασθενείς. Σε ασθενείς με αυξημένο κίνδυνο οξέων αντιδράσεων

αλλεργικού τύπου, και ασθενείς με ιστορικό μετρίας ή σοβαρής οξείας αντίδρασης, άσθματος ή αλλεργίας που απαίτησε ιατρική αντιμετώπιση,

μπορεί να ληφθεί υπόψη μια προφυλακτική αγωγή με κορτικοστεροειδή. • Δυσλειτουργία του θυρεοειδούς. Ιδιαίτερα προσεκτική εκτίμηση

της σχέσης κινδύνου/οφέλους είναι απαραίτητη σε ασθενείς με γνωστό ή πιθανολογούμενο υπερθυρεοειδισμό ή βρογχοκήλη, καθώς τα

ιωδιούχα σκιαγραφικά μέσα μπορεί να προκαλέσουν υπερθυρεοειδισμό και θυρεοτοξική κρίση σε αυτούς τους ασθενείς. Ο έλεγχος της λειτουργίας

του θυρεοειδή πριν από τη χορήγηση του Ultravist και/ή προληπτική φαρμακευτική αγωγή με αντιθυρεοειδικά μπορεί να ληφθούν

υπόψη στους ασθενείς με γνωστό ή πιθανολογούμενο υπερθυρεοειδισμό. Έχουν αναφερθεί δοκιμές λειτουργίας του θυρεοειδούς ενδεικτικές

του υποθυρεοειδισμού ή παροδικής καταστολής του θυρεοειδούς μετά από χορήγηση ιωδιούχου σκιαγραφικού μέσου σε ενήλικες και παιδιατρικούς

ασθενείς. Αξιολογήστε τον πιθανό κίνδυνο υποθυρεοειδισμού σε ασθενείς με γνωστές ή ύποπτες ασθένειες του θυρεοειδούς πριν

από τη χρήση ιωδιούχων σκιαγραφικών μέσων. Παιδιατρικός πληθυσμός Θυροειδική δυσλειτουργία χαρακτηριζόμενη από υποθυρεοειδισμό

ή παροδική θυρεοειδική καταστολή έχει αναφερθεί τόσο μετά από εφάπαξ όσο και μετά από πολλαπλές εκθέσεις σε ιωδιούχα σκιαγραφικά

μέσα (ICM) σε παιδιατρικούς ασθενείς ηλικίας κάτω των 3 ετών. Η συχνότητα εμφάνισης έχει αναφερθεί μεταξύ 1% και 15% ανάλογα με την

ηλικία των ατόμων και τη δόση του ιωδιούχου σκιαγραφικού και παρατηρείται πιο συχνά σε νεογνά και πρόωρα βρέφη. Τα νεογνά μπορεί

επίσης να εκτεθούν μέσω της μητέρας κατά τη διάρκεια της εγκυμοσύνης. Μικρότερη ηλικία, πολύ χαμηλό βάρος γέννησης, προωρότητα,

υποκείμενες ιατρικές παθήσεις που επηρεάζουν τη λειτουργία του θυρεοειδούς, εισαγωγή σε μονάδες εντατικής θεραπείας νεογνών ή παίδων

και συγγενείς καρδιακές παθήσεις σχετίζονται με αυξημένο κίνδυνο υποθυρεοειδισμού μετά από έκθεση σε ICM. Οι παιδιατρικοί ασθενείς με

συγγενείς καρδιακές παθήσεις ενδέχεται να διατρέχουν τον μεγαλύτερο κίνδυνο, δεδομένου ότι συχνά απαιτούν υψηλές δόσεις σκιαγραφικού

κατά τις επεμβατικές καρδιακές διαδικασίες. Ένας υπολειτουργικός θυρεοειδής κατά την πρώιμη ζωή μπορεί να είναι επιβλαβής για τη

γνωστική και νευρολογική ανάπτυξη και μπορεί να απαιτεί θεραπεία υποκατάστασης θυρεοειδικών ορμονών. • Μετά την έκθεση σε ICM,

εξατομικεύστε την παρακολούθηση της λειτουργίας του θυρεοειδούς με βάση τους υποκείμενους παράγοντες κινδύνου, ειδικά στα τελειόμηνα

και πρόωρα νεογνά. Παθήσεις του ΚΝΣ. Ασθενείς με ιστορικό διαταραχών του ΚΝΣ μπορεί να διατρέχουν αυξημένο κίνδυνο εμφάνισης

νευρολογικών επιπλοκών σχετιζόμενων με τη χορήγηση του Ultravist. Οι νευρολογικές επιπλοκές εμφανίζονται συχνότερα στην εγκεφαλική

αγγειογραφία και ανάλογες διαδικασίες. Έχει αναφερθεί εγκεφαλοπάθεια με τη χρήση ιοπρομίδης (βλ. παράγραφο 4.8). Η εγκεφαλοπάθεια

οφειλόμενη σε σκιαγραφικό μπορεί να εκδηλωθεί με συμπτώματα και σημεία νευρολογικής δυσλειτουργίας όπως κεφαλαλγία, διαταραχή

της όρασης, φλοιώδη τύφλωση, σύγχυση, επιληπτικές κρίσεις, απώλεια συντονισμού, ημιπάρεση, αφασία, απώλεια συνείδησης, κώμα και

εγκεφαλικό οίδημα. Τα συμπτώματα συνήθως εμφανίζονται εντός λεπτών έως ωρών μετά τη χορήγηση ιοπρομίδης και γενικά υποχωρούν

πλήρως εντός ημερών. Θα πρέπει να δίνεται προσοχή σε καταστάσεις κατά τις οποίες μπορεί να υπάρχει μειωμένος ουδός ως προς την εμφάνιση

επιληπτικών κρίσεων, όπως προηγούμενο ιστορικό επιληπτικών κρίσεων και χρήση ορισμένων συγχορηγούμενων φαρμάκων. Παράγοντες

που αυξάνουν τη διαπερατότητα του αιματοεγκεφαλικού φραγμού διευκολύνουν τη δίοδο του σκιαγραφικού μέσου στον εγκεφαλικό

ιστό, γεγονός που πιθανόν να οδηγήσει σε αντιδράσεις από το ΚΝΣ, για παράδειγμα εγκεφαλοπάθεια. Εάν πιθανολογείται εγκεφαλοπάθεια

οφειλόμενη σε σκιαγραφικό, θα πρέπει να αρχίζει κατάλληλη ιατρική διαχείριση και η χορήγηση ιοπρομίδης δεν πρέπει να επαναληφθεί. •

Ενυδάτωση: Πρέπει να διασφαλιστεί επαρκής κατάσταση ενυδάτωσης σε όλους τους ασθενείς, πριν από ενδοαγγειακή χορήγηση του

Ultravist, (βλ. επίσης υποπαράγραφο «Οξεία νεφρική βλάβη»). Αυτό ισχύει ιδιαίτερα για τους ασθενείς με πολλαπλό μυέλωμα, σακχαρώδη

διαβήτη, πολυουρία, ολιγουρία, υπερουριχαιμία, καθώς επίσης και σε νεογνά, βρέφη, μικρά παιδιά και ηλικιωμένους ασθενείς. Η επαρκής

κατάσταση ενυδάτωσης πρέπει να διασφαλίζεται σε ασθενείς με νεφρική δυσλειτουργία. Ωστόσο, η προφυλακτική ενδοφλέβια ενυδάτωση

σε ασθενείς με μέτρια νεφρική δυσλειτουργία (eGFR 30 – 59 ml / min / 1,73 m2) δεν συνιστάται, καθώς δεν έχουν τεκμηριωθεί πρόσθετα

οφέλη για την νεφρική ασφάλεια. Σε ασθενείς με σοβαρή νεφρική δυσλειτουργία (eGFR <30 ml / min / 1,73 m2) και συνοδές καρδιακές παθήσεις,

η προφυλακτική ενδοφλέβια ενυδάτωση μπορεί να οδηγήσει σε αυξημένες σοβαρές καρδιακές επιπλοκές. Ανατρέξτε στις υποπαραγράφους

«Οξεία νεφρική βλάβη», «Καρδιαγγειακή νόσος», «Κατάλογος των ανεπιθύμητων ενεργειών σε μορφή πίνακα». • Ανησυχία: Έντονες

καταστάσεις έξαψης, ανησυχίας και πόνου μπορεί να αυξήσουν τον κίνδυνο ανεπιθύμητων ενεργειών ή να εντείνουν τις αντιδράσεις που

σχετίζονται με τα σκιαγραφικά μέσα. Πρέπει να λαμβάνεται μέριμνα για την ελαχιστοποίηση της κατάσταση άγχους σε αυτούς τους ασθενείς.

• Προκαταρτικός έλεγχος: Δεν συνιστάται η δοκιμή ευαισθησίας χρησιμοποιώντας μία μικρή δοκιμαστική δόση του σκιαγραφικού μέσου

καθώς δεν έχει προγνωστική αξία. Επιπρόσθετα, οι ίδιες οι δοκιμές ευαισθησίας έχουν οδηγήσει περιστασιακά σε σοβαρές ή ακόμα και θανατηφόρες

αντιδράσεις υπερευαισθησίας. • Σοβαρές δερματικές ανεπιθύμητες ενέργειες (SCARs): Σοβαρές δερματικές ανεπιθύμητες ενέργειες

(SCARs) που συμπεριλαμβάνουν σύνδρομο Stevens-Johnson (SJS), τοξική επιδερμική νεκρόλυση (TEN), αντίδραση στο φάρμακο με ηωσινοφιλία

και συστηματικά συμπτώματα (DRESS) και οξεία γενικευμένη εξανθηματική φλυκταίνωση (AGEP), οι οποίες μπορεί να είναι απειλητικές

για τη ζωή ή θανατηφόρες, έχουν αναφερθεί με συχνότητα μη γνωστή σε συνδυασμό με τη χορήγηση ιοπρομίδης. Οι ασθενείς θα πρέπει να

λαμβάνουν συμβουλές σχετικά με τα σημεία και συμπτώματα και να παρακολουθούνται στενά για δερματικές αντιδράσεις. Στα παιδιά, η

αρχική παρουσίαση εξανθήματος μπορεί να εκληφθεί εσφαλμένα ως λοίμωξη, και οι ιατροί θα πρέπει να εξετάζουν την πιθανότητα αντίδρασης

στην ιοπρομίδη στα παιδιά τα οποία αναπτύσσουν σημεία εξανθήματος και πυρετού. Οι περισσότερες από αυτές τις αντιδράσεις εμφανίστηκαν

εντός 8 εβδομάδων (AGEP 1 12 ημέρες, DRESS 2 8 εβδομάδες, SJS/TEN 5 ημέρες έως 8 εβδομάδες). Εάν ο ασθενής έχει αναπτύξει μια

σοβαρή αντίδραση όπως SJS, TEN, AGEP ή DRESS με τη χρήση ιοπρομίδης, δεν πρέπει ποτέ να επαναχορηγηθεί ιοπρομίδη σε αυτόν τον

ασθενή. Ενδοαγγειακή χρήση • Οξεία νεφρική βλάβη. Μετά την ενδοαγγειακή χορήγηση του Ultravist μπορεί να εμφανιστεί οξεία νεφρική

βλάβη που οφείλεται στη χορήγηση του σκιαγραφικού μέσου (Post-Contrast Acute Kidney Injury – PC-AKI), η οποία εμφανίζεται ως παροδική

έκπτωση της νεφρικής λειτουργίας. Σε μερικές περιπτώσεις μπορεί να εμφανιστεί οξεία νεφρική ανεπάρκεια. Στους παράγοντες κινδύνου

περιλαμβάνονται ενδεικτικά: - προϋπάρχουσα μειωμένη νεφρική λειτουργία (βλ. υποπαράγραφο «Ασθενείς με νεφρική ανεπάρκεια»), - αφυδάτωση

(βλ. υποπαράγραφο «Ενυδάτωση»), - σακχαρώδης διαβήτης, - πολλαπλό μυέλωμα/παραπρωτεϊναιμία - επαναλαμβανόμενες και/ή

υψηλές δόσεις Ultravist. Ασθενείς με μέτρια έως σοβαρή (eGFR 44 – 30 ml / min / 1,73 m2) ή σοβαρή νεφρική δυσλειτουργία (eGFR <30 ml /

min / 1,73 m2) διατρέχουν αυξημένο κίνδυνο εμφάνισης οξείας νεφρικής βλάβης που οφείλεται στη χορήγηση του σκιαγραφικού μέσου

(PC-AKI) μετά την ενδοαρτηριακή χορήγηση του μέσου αντίθεσης με νεφρική έκθεση σε πρώτο χρόνο (first pass renal exposure). Οι ασθενείς

με σοβαρή νεφρική δυσλειτουργία (eGFR <30 ml / min / 1,73 m2) διατρέχουν αυξημένο κίνδυνο PC-AKI μετά την ενδοφλέβια ή ενδοαρτηριακή

χορήγηση του μέσου αντίθεσης με νεφρική έκθεση σε δεύτερο χρόνο (second pass renal exposure) (βλ. υποπαράγραφο «Ενυδάτωση»). Οι

ασθενείς που υποβάλλονται σε αιμοκάθαρση, ακόμα και αν δεν παρουσιάζουν υπολειμματική νεφρική λειτουργία, μπορούν να λάβουν

Ultravist για τη διεξαγωγή ακτινολογικών εξετάσεων, διότι τα ιωδιούχα σκιαγραφικά μέσα απεκκρίνονται μέσω της διαδικασίας της αιμοκάθαρσης.

• Καρδιαγγειακή νόσος: Ασθενείς με σημαντική καρδιακή νόσο ή στεφανιαία νόσο βρίσκονται σε αυξημένο κίνδυνο να αναπτύξουν

κλινικά σημαντικές αιμοδυναμικές μεταβολές και αρρυθμία. Η ενδοαγγειακή ένεση Ultravist μπορεί να οδηγήσει σε πνευμονικό οίδημα σε

ασθενείς με καρδιακή ανεπάρκεια. • Φαιοχρωμοκύττωμα: Ασθενείς με φαιοχρωματοκύττωμα μπορεί να βρίσκονται σε αυξημένο κίνδυνο να

αναπτύξουν υπερτασική κρίση. • Μυασθένια Gravis: Η χορήγηση Ultravist μπορεί να επιδεινώσει τα συμπτώματα της μυασθένιας Gravis. •

Θρομβοεμβολικά συμβάματα: Μία από τις ιδιότητες των μη ιονικών σκιαγραφικών μέσων είναι η μικρή επίδρασή τους στις φυσιολογικές

λειτουργίες του οργανισμού. Συνεπώς, τα μη ιονικά σκιαγραφικά μέσα έχουν μικρότερη αντιπηκτική δράση in vitro από τα ιονικά. Πολλοί

παράγοντες επιπρόσθετα στα σκιαγραφικά μέσα, συμπεριλαμβανομένης της διάρκειας της διαδικασίας, του αριθμού των ενέσεων, του υλικού

του καθετήρα και της σύριγγας, της υποκείμενης νόσου, και της ταυτόχρονης φαρμακευτικής αγωγής, μπορούν να συμβάλλουν στην

εμφάνιση θρομβοεμβολικών επεισοδίων. Αυτό πρέπει να λαμβάνεται υπόψη κατά την εφαρμογή τεχνικών με φλεβοκαθετήρα και να δίνεται

ιδιαίτερη προσοχή στην τεχνική της αγγειογραφίας και στο συχνό πλύσιμο των καθετήρων με φυσιολογικό ορό (εφόσον είναι απαραίτητο με

προσθήκη ηπαρίνης) ενώ ο χρόνος της εξέτασης να μειώνεται στο ελάχιστο, ώστε να ελαχιστοποιείται ο κίνδυνος θρομβώσεων και εμβολών

που συνδέονται με τη διαδικασία. Ψηφιακή μαστογραφία με έγχυση σκιαγραφικού (CEM) Η Ψηφιακή μαστογραφία με έγχυση σκιαγραφικού

έχει ως αποτέλεσμα μεγαλύτερη έκθεση του ασθενούς σε ιονίζουσα ακτινοβολία από την τυπική μαστογραφία. Η δόση ακτινοβολίας

εξαρτάται από το πάχος του μαστού, τον τύπο και τις ρυθμίσεις συστήματος της συσκευής. Η συνολική δόση ακτινοβολίας CEM παραμένει

κάτω από το όριο που ορίζεται από τις διεθνείς οδηγίες για τη μαστογραφία (κάτω από 3 mGy). 4.8 Ανεπιθύμητες ενέργειες Περίληψη του

προφίλ ασφαλείας Το συνολικό προφίλ ασφαλείας του Ultravist βασίζεται σε δεδομένα που λήφθηκαν σε μελέτες πριν από την κυκλοφορία

του προϊόντος σε περισσότερους από 3.900 ασθενείς και σε μελέτες μετά την κυκλοφορία του προϊόντος σε περισσότερους από 74.000

ασθενείς, καθώς επίσης και σε δεδομένα από αυθόρμητες αναφορές και τη βιβλιογραφία. Οι πιο συχνά αναφερόμενες ανεπιθύμητες ενέργειες

(≥4 %) σε ασθενείς που λαμβάνουν Ultravist είναι κεφαλαλγία, ναυτία και αγγειοδιαστολή. Οι πιο σοβαρές ανεπιθύμητες ενέργειες σε

ασθενείς που λαμβάνουν Ultravist είναι αναφυλακτικό σοκ, αναπνευστική ανακοπή, βρογχoσπασμός, λαρυγγικό οίδημα, φαρυγγικό οίδημα,

άσθμα, κώμα, εγκεφαλικό έμφρακτο, εγκεφαλικό επεισόδιο, εγκεφαλικό οίδημα, σπασμοί, αρρυθμία, καρδιακή ανακοπή, μυοκαρδιακή ισχαιμία,

έμφραγμα του μυοκαρδίου, καρδιακή ανεπάρκεια, βραδυκαρδία, κυάνωση, υπόταση, καταπληξία, δύσπνοια, πνευμονικό οίδημα, αναπνευστική

ανεπάρκεια και αναρρόφηση. Κατάλογος των ανεπιθύμητων ενεργειών σε μορφή πίνακα Οι ανεπιθύμητες ενέργειες που παρατηρήθηκαν

με το Ultravist παρουσιάζονται στον παρακάτω πίνακα. Κατηγοριοποιούνται σύμφωνα με το Οργανικό Σύστημα κατά MedDRA

(έκδοση 13.0). Ο πιο κατάλληλος όρος MedDRΑ χρησιμοποιείται για να περιγράψει μία συγκεκριμένη κατάσταση καθώς επίσης και τα συνώνυμά

της και σχετιζόμενες καταστάσεις. Οι ανεπιθύμητες ενέργειες από τις κλινικές μελέτες κατηγοριοποιούνται σύμφωνα με τις συχνότητές

τους. Οι ομαδοποιήσεις των συχνοτήτων ορίζονται σύμφωνα με την εξής σύμβαση: Συχνές (≥1/100 έως <1/10), Όχι συχνές (≥1/1.000 έως

<1/100), Σπάνιες (≥1/10.000 έως <1/1.000). Οι ανεπιθύμητες ενέργειες που αναγνωρίστηκαν μόνο κατά τη διάρκεια της παρακολούθησης του

προϊόντος μετά την κυκλοφορία του και για τις οποίες η συχνότητα δεν μπορούσε να εκτιμηθεί, παρατίθενται στην κατηγορία «μη γνωστές».

Πίνακας 1: Ανεπιθύμητες ενέργειες που αναφέρθηκαν σε κλινικές μελέτες ή κατά την παρακολούθηση του προϊόντος μετά την κυκλοφορία

του σε ασθενείς που έλαβαν Ultravist. Οργανικό σύστημα: Διαταραχές του ανοσοποιητικού συστήματος, Όχι συχνές: ΑΑντιδράσεις

υπερευαισθησίας/ αναφυλακτοειδείς αντιδράσεις (αναφυλακτικό σοκ §) *), αναπνευστική ανακοπή §) *), βρογχοσπασμός *), λαρυγγικό

*)/ φαρυγγικό*) οίδημα, οίδημα προσώπου, οίδημα γλώσσας §), λαρυγγικός /φαρυγγικός σπασμός §), άσθμα §) *),, επιπεφυκίτιδα §),

δακρύρροια §), πταρμός, βήχας, οίδημα βλεννογόνων, ρινίτιδα §), βράγχος §), ερεθισμός λαιμού §), κνίδωση, κνησμός, αγγειοοίδημα).

Οργανικό σύστημα: Διαταραχές του ενδοκρινικού συστήματος, Μη γνωστές: Θυρεοτοξική κρίση, Διαταραχή της θυρεοειδικής λειτουργίας.

Οργανικό σύστημα: Ψυχιατρικές διαταραχές, Σπάνιες: Ανησυχία. Οργανικό σύστημα: Διαταραχές του νευρικού συστήματος, Συχνές:

Ζάλη, Πονοκέφαλος, Δυσγευσία, Όχι συχνές: Βαγοτονία, Κατάσταση σύγχυσης, Νευρικότητα, Παραισθησία/Υπαισθησία, Αϋπνία,

Μη γνωστές: Κώμα*), Εγκεφαλική ισχαιμία/ έμφρακτο *), Εγκεφαλικό επεισόδιο *), Εγκεφαλικό οίδημα α) *), Σπασμοί *), Παροδική

φλοιώδης τύφλωση α), Απώλεια συνείδησης, Διέγερση, Αμνησία, Τρόμος, Διαταραχές ομιλίας, Πάρεση/παράλυσηΕγκεφαλοπάθεια

οφειλόμενη σε σκιαγραφικό. Οργανικό σύστημα: Οφθαλμικές διαταραχές, Συχνές: Θολή/ διαταραγμένη όραση Οργανικό σύστημα:

Διαταραχές του ωτός και του λαβυρίνθο, Μη γνωστές: Διαταραχές της ακοής. Οργανικό σύστημα: Καρδιακές διαταραχές, Συχνές:

Πόνος / στο στήθος/δυσφορία, Όχι συχνές: Αρρυθμία *) , Σπάνιες: Καρδιακή ανακοπή *) , Μυοκαρδιακή ισχαιμία, Αίσθημα παλμών, Μη

γνωστές: Έμφραγμα του μυοκαρδίου *) , Καρδιακή ανεπάρκεια *) , Βραδυκαρδία *) , Ταχυκαρδία, Κυάνωση *) Οργανικό σύστημα: Αγγειακές

διαταραχές Συχνές: Υπέρταση Αγγειοδιαστολή Όχι συχνές: Υπόταση *) Μη γνωστές: Καταπληξία *) , Θρομβοεμβολικά συμβάντα α) , Αγγειόσπασμος

α) Οργανικό σύστημα: Διαταραχές του αναπνευστικού συστήματος, του θώρακα και του μεσοθωράκιου Όχι συχνές: Δύσπνοια

*)

Μη γνωστές: Πνευμονικό οίδημα *) , Αναπνευστική ανεπάρκεια *) , Αναρρόφηση *) Οργανικό σύστημα: Διαταραχές του γαστρεντερικού,

Συχνές: Έμετος, Ναυτία, Όχι συχνές: Κοιλιακό άλγος, Μη γνωστές: Δυσφαγία, Μεγέθυνση των σιελογόνων αδένων, Διάρροια. Οργανικό

σύστημα: Διαταραχές του δέρματος και του υποδόριου ιστού Μη γνωστές: Πομφολυγώδεις δερματοπάθειες (π.χ. σύνδρομο Stevens-

Johnson ή σύνδρομο Lyell), Εξάνθημα, Ερύθημα, Υπερίδρωση, Οξεία γενικευμένη εξανθηματική φλυκταίνωση, Αντίδραση στο φάρμακο

με ηωσινοφιλία και συστηματικά συμπτώματα. Οργανικό σύστημα: Διαταραχές του μυοσκελετικού συστήματος και του συνδετικού

ιστού, Μη γνωστές: Σύνδρομο διαμερισματοποίησης σε περίπτωση εξαγγείωσης α) Οργανικό σύστημα: Διαταραχές των νεφρών και των

ουροφόρων οδών, Μη γνωστές: Μειωμένη νεφρική λειτουργία α) , Οξεία νεφρική ανεπάρκεια α) Οργανικό σύστημα: Γενικές διαταραχές

και καταστάσεις της οδού χορήγησης Συχνές: ΠόνοςΑντίδραση στο σημείο της ένεσης (ποικίλλων ειδών, π.χ. πόνος, θερμότητα §) , οίδημα

§)

, φλεγμονή §) και κάκωση των μαλακών μορίων §) σε περίπτωση εξαγγείωσης), Αίσθημα θερμότητας Όχι συχνές: Οίδημα, Μη γνωστές:

Αδιαθεσία, Ρίγη, Ωχρότητα. Οργανικό σύστημα: Παρακλινικές εξετάσεις, Μη γνωστές: Διακυμάνσεις στη θερμοκρασία του σώματος. *)

έχουν αναφερθεί απειλητικές για τη ζωή και/ή μοιραίες περιπτώσεις, α) μόνο σε ενδοαγγειακή χρήση, §) αναγνωρίστηκαν μόνο κατά την παρακολούθηση

του προϊόντος μετά την κυκλοφορία του (συχνότητα μη γνωστή). Η πλειοψηφία των αντιδράσεων μετά από μυελογραφία ή χρήση

σε σωματικές κοιλότητες, εμφανίζονται μερικές ώρες μετά τη χορήγηση. Εκτός από τις προαναφερθείσες ανεπιθύμητες ενέργειες, μπορεί να

εμφανιστούν και οι ακόλουθες με τη χρήση σε ERCP: αύξηση των επιπέδων των ενζύμων του παγκρέατος και παγκρεατίτιδα σε μη γνωστή

συχνότητα. Αναφορά πιθανολογούμενων ανεπιθύμητων ενεργειών: Η αναφορά πιθανολογούμενων ανεπιθύμητων ενεργειών μετά από τη

χορήγηση άδειας κυκλοφορίας του φαρμακευτικού προϊόντος είναι σημαντική. Επιτρέπει τη συνεχή παρακολούθηση της σχέσης οφέλους-κινδύνου

του φαρμακευτικού προϊόντος. Ζητείται από τους επαγγελματίες υγείας να αναφέρουν οποιεσδήποτε πιθανολογούμενες ανεπιθύμητες

ενέργειες μέσω: Ελλάδα: Εθνικός Οργανισμός Φαρμάκων, Μεσογείων 284, GR-15562 Χολαργός, Αθήνα, Τηλ: + 30 21 32040337, Ιστότοπος:

http://www.eof.gr, http://www.kitrinikarta.gr, Κύπρος: Φαρμακευτικές Υπηρεσίες, Υπουργείο Υγείας, CY-1475 Λευκωσία, Τηλ: +357 22608607,

Φαξ: + 357 22608669, Ιστότοπος: www.moh.gov.cy/phs 6. ΦΑΡΜΑΚΕΥΤΙΚΕΣ ΠΛΗΡΟΦΟΡΙΕΣ 6.1 Κατάλογος εκδόχων Νατριούχο εδετικό

ασβέστιο, τρομεταμόλη, υδροχλωρικό οξύ 10% (για ρύθμιση του pH), υδροξείδιο του νατρίου (για ρύθμιση του pH), ενέσιμο ύδωρ. 6.2 Ασυμβατότητες

To Ultravist δεν πρέπει να αναμειγνύεται με άλλα φάρμακα, ώστε να αποφευχθεί ο κίνδυνος πιθανής ασυμβατότητας. 6.3 Διάρκεια

ζωής 3 χρόνια. Μετά το άνοιγμα του περιέκτη, το Ultravist συνιστάται να χρησιμοποιείται εντός 10 ωρών. 6.4 Ιδιαίτερες προφυλάξεις κατά

τη φύλαξη του προϊόντος Να φυλάσσεται σε θερμοκρασία έως 25° C και να μην εκτίθεται στο φως και την ακτινοβολία. Διατηρείτε τα φάρμακα

προσεκτικά και μακριά από τα παιδιά. 6.5 Φύση και συστατικά του περιέκτη Φιάλες από γυαλί τύπου ΙΙ, Φιαλίδια από γυαλί τύπου Ι,

Πώμα από καουτσούκ: ελαστομερές χλωροβουτύλιο ή βρωμοβουτύλιο, γκρι. Ultravist 300 Ελλάδα: φιαλίδια των 20 ml, 50 ml, 100 ml και

φιάλες των 200 ml, 500 ml, 1000 ml, Κύπρος: φιαλίδια των 50 ml, 100 ml και φιάλες των 200 ml Ultravist 370

Ελλάδα: φιαλίδια των 50 ml, 100 ml και φιάλες των 150 ml, 200 ml και 500 ml, 1000 ml, Κύπρος: φιαλίδια των 50 ml, 100 ml και φιάλες των

150 ml, 200 ml και 500 ml, 1000 ml, Μπορεί να μη κυκλοφορούν όλες οι συσκευασίες. 6.6 Ιδιαίτερες προφυλάξεις απόρριψης και άλλος

χειρισμός Οπτικός έλεγχος. Πριν τη χρήση των σκιαγραφικών μέσων πρέπει να διενεργείται οπτικός έλεγχος και δεν θα πρέπει αυτά να χρησιμοποιούνται

σε περίπτωση αποχρωματισμού, ούτε επί παρουσίας σωματιδίων (περιλαμβανομένων των κρυστάλλων) ή ελαττωματικού περιέκτη.

Επειδή το Ultravist είναι ένα υψηλής συγκέντρωσης διάλυμα, πολύ σπάνια μπορεί να εμφανισθεί κρυσταλλοποίηση (γαλακτώδης – θολερή

εμφάνιση και/ή ίζημα στον πυθμένα ή αιωρούμενοι κρύσταλλοι). • Φιαλίδια: Το διάλυμα του σκιαγραφικού δεν πρέπει να αναρροφάται στη

σύριγγα ή στον ορό της συσκευής έγχυσης, παρά μόνο αμέσως πριν τη χορήγησή του. Το ελαστικό πώμα πρέπει να διατρυπάται μία μόνο φορά,

ώστε να αποφεύγεται η εισροή μικροσωματιδίων από το πώμα στο διάλυμα. Συνιστάται για τη διάτρηση του ελαστικού πώματος και για την

αναρρόφηση του σκιαγραφικού να χρησιμοποιούνται βελόνες με μακρά λοξή κοπή και με διάμετρο κατά μέγιστο 18G (ιδιαίτερα κατάλληλες

είναι γνήσιες βελόνες παρακέντησης με πλάγιo άνοιγμα π.χ. βελόνες Nocore-Admix). Διάλυμα σκιαγραφικού που δεν καταναλώθηκε σε μια

εξέταση για ένα συγκεκριμένο ασθενή πρέπει να απορρίπτεται. • Περιέκτες μεγάλου όγκου (μόνο για ενδοαγγειακή χορήγηση): Οι ακόλουθες

οδηγίες πρέπει να ακολουθούνται για την πολλαπλή αφαίρεση σκιαγραφικού από τις συσκευασίες των 200 ml και άνω: Η πολλαπλή αφαίρεση

σκιαγραφικού πρέπει να γίνεται με τη χρήση ιατροτεχνολογικών βοηθημάτων, τα οποία έχουν εγκριθεί για πολλαπλή χρήση. Το ελαστικό πώμα

πρέπει να διατρυπάται μία μόνο φορά, ώστε να αποφεύγεται η εισροή μικροσωματιδίων από το πώμα στο διάλυμα. Το σκιαγραφικό μέσο

πρέπει να χορηγείται με τη βοήθεια ενός αυτόματου εγχυτή ή οποιασδήποτε άλλης διαδικασίας, η οποία μπορεί να εξασφαλίσει τη στειρότητα

του σκιαγραφικού μέσου. Ο σωλήνας που οδηγεί από τον εγχυτή στον ασθενή (σωλήνας του ασθενούς) πρέπει να αλλάζει μετά από κάθε

εξέταση, διότι μολύνεται με το αίμα του ασθενούς. Οι σωλήνες και όλα τα εξαρτήματα μίας χρήσης του εγχυτή πρέπει να απορρίπτονται, όταν

αδειάζει η φιάλη ή 10 ώρες μετά το πρώτο άνοιγμα του περιέκτη. Το σκιαγραφικό που μένει μέσα σε ανοιγμένη φιάλη, πρέπει να απορρίπτεται

δέκα ώρες μετά το πρώτο άνοιγμα του περιέκτη. Επίσης, πρέπει να ακολουθούνται πιστά οι οποιεσδήποτε πρόσθετες οδηγίες που έχουν δοθεί

από τον παρασκευαστή του αντίστοιχου εξοπλισμού. 7. ΚΑΤΟΧΟΣ ΑΔΕΙΑΣ ΚΥΚΛΟΦΟΡΙΑΣ ΚΑΤΟΧΟΣ ΑΔΕΙΑΣ ΚΥΚΛΟΦΟΡΙΑΣ ΣΤΗΝ ΕΛΛΑΔΑ ΚΑΙ

ΣΤΗΝ ΚΥΠΡΟ Bayer Ελλάς ΑΒΕΕ, Αγησιλάου 6-8, 151 23 Μαρούσι, Αττική, Ελλάδα, Τηλ: +30 210 6187500 Τοπικός αντιπρόσωπος στην Κύπρο

Novagem Ltd Τηλ: +357 22483858. 8. ΑΡΙΘΜΟΣ ΑΔΕΙΑΣ ΚΥΚΛΟΦΟΡΙΑΣ ΕΛΛΑΔΑ: Ultravist 300: 39944/4-11-2009, Ultravist 370: 39946/4-

11-2009 ΚΥΠΡΟΣ: Ultravist 300: 023211, Ultravist 370: 18966 9. ΗΜΕΡΟΜΗΝΙΑ ΤΗΣ ΠΡΩΤΗΣ ΕΓΚΡΙΣΗΣ/ΑΝΑΝΕΩΣΗΣ ΤΗΣ ΑΔΕΙΑΣ ΕΛΛΑΔΑ:

Ultravist® 300: 06.02.1989 / 4.11.2009 (επ’ αόριστον), Ultravist® 370: 06.02.1989 / 4.11.2009 (επ’ αόριστον) ΚΥΠΡΟΣ: Ultravist® 300: 07.10.2020

/ 13 Μαρτίου 2025, Ultravist® 370: 11.07.2000 / 14.9.2011 (επ’ αόριστον). 10. ΗΜΕΡΟΜΗΝΙΑ ΑΝΑΘΕΩΡΗΣΗΣ ΤΟΥ ΚΕΙΜΕΝΟΥ Ελλάδα: Μάρτιος

2025, Κύπρος: 13/03/2025.

Για περισσότερες συνταγογραφικές πληροφορίες απευθυνθείτε στον Κάτοχο Άδειας Κυκλοφορίας

H JR

Hellenic Journal of Radiology

Editorial Board

Editor-in-Chief

Athanasios Chalazonitis, Athens/GR

Associate Editor

Anastasios Gyftopoulos, Athens/GR

Assistant Editors

Konstantinos Stefanidis, Athens/GR

Aikaterini Tavernaraki, Athens/GR

Junior Assistant Editors

Alexandros Letsos, Athens/GR

Antigoni Logotheti, Athens/GR

Kalliopi Parlamenti, Athens/GR

Ioannis Raftopoulos, Athens/GR

Stavroula Tzamouri, Athens/GR

Section Editors

Neuro/Head and Neck Radiology

Petros Zampakis, Patras/GR

Georgios Karas, Amsterdam/NL

Spyridon Kollias, Athens/GR

Stavroula Lyra, Athens/GR

Ekaterini Solomou, Patras/GR

Thoracic and Cardiovascular Imaging

Alexandros Kalifatidis, Thessaloniki/GR

Theodoros Kratimenos, Athens/GR

Renata Mastorakou, Athens/GR

Marousa Ntouskou, Liverpool/UK

Abdominal Imaging

Evaggelos Alexiou, Larissa/GR

Anastasios Leukopoulos, Thessaloniki/GR

Chrysovalantis Vergadis, Athens/GR

Konstantinos Revenas, Athens/GR

Oncologic Imaging

Efthymios Andriotis, Athens/GR

Christina Kalogeropoulou, Patras/GR

Myrsini Stassinopoulou, Athens/GR

Kostas Tsilikas, Athens/GR

Dimitris Tsitsimelis, Athens/GR

Paediatric Radiology

Ioannis Nikas, Athens/GR

Marina Papadaki, Athens/GR

Marina Vakaki, Athens/GR

Musculoskeletal Imaging

Alexia Balanika, Athens/GR

Georgios Delimpasis, Bern/CH

Marianna Vlychou, Larissa/GR

Interventional Radiology

Ioannis Ioannidis, Larissa/GR

Georgios Karydas, Athens/GR

Konstantinos Papadopoulos, Athens/GR

Christos Rountas, Larissa/GR

Breast Imaging

Irini Georgiou, Athens/GR

Polytimi Leonardou, Athens/GR

Vasilis Tataridas, Athens/GR

Aikaterini Vassiou, Larissa/GR

Molecular and Hybrid Imaging

Maria Gavra, Athens/GR

Ioannis Pantazis, Athens/GR

Konstantinos Stefanidis, Athens/GR

Cited in: • Scopus • Index Copernicus International • Google Scholar

Visit the journal website www.hjradiology.org

Published by: SPEG Consulting

Ippokratous 44-46, 106 80, Athens, Greece

Tel.: +30 210 5238777, E - mail: info@speg.gr

H JR


Editorial Board

Editorial Board Members

Andreas Adam, London/UK - Interventional Radiology

Gina Allen, Oxford/UK - Musculoskeletal Radiology

Luis Marti Bonmati, Valencia/ES - Neuroradiology

Efstathios Boviatsis, Athens/GR - Neurosurgery

Roberto Cannella, Palermo/IT - Abdominal and Gastrointestinal Imaging

Elias Brountzos, Athens/GR - Interventional Radiology

Achilles Chatziioannou, Athens/GR - Interventional Radiology

Sofia Chatziioannou, Athens/GR - Nuclear Medicine

Ioannis Datseris, Athens/GR - Nuclear Medicine

Athanasios Gouliamos, Athens/GR - Neuroradiology

Giuseppe Guglielmi, Foggia/IT - Musculoskeletal Radiology

Thomas Helmberger, Munich/DE - Abdominal Imaging

Vasiliki Kamenopoulou, Vienna/AT - Radiation Physics

Dimitrios Kardamakis, Patras/GR - Radiation Oncology

Dimitrios Karnabatidis, Patras/GR - Interventional Radiology

Ioannis Koutsikos, Athens/GR - Nuclear Medicine

Andrea Laghi, Rome/IT - Abdominal and Gastrointestinal Imaging

Paul Nikolaidis, Chicago/USA - Body Imaging

Sotirios Oikonomidis, Athens/GR - Radiation Physics

Nikos Papanicolaou, Philadelphia/USA - Genitourinary Imaging

Georgios Pissakas, Athens/GR - Radiation Oncology

Spyridon Pneumatikos, Athens/GR - Orthopedics

Marios Psychogios, Basel/CH - Interventional Neuroradiolgy

Vassilios Raptopoulos, Boston/USA -Abdominal Radiology

Hans Peter Schlemmer, Heidelberg/DE - Oncologic Imaging

Nikolaos Tentolouris, Athens/GR - Internal Medicine

David Wilson, Oxford/UK - Musculoskeletal Radiology

Giulia Zamboni, Verona/IT - Abdominal Imaging

Anastasia Zikou, Ioannina/GR - Neuroradiology

H JR


90 ΧΡΟΝΙΑ

Official Journal of the

HELLENIC RADIOLOGICAL

SOCIETY

Board of the Society

President

Athanasios Chalazonitis

Honorary President

Prof. Kyriakos Strigaris

Vice President A'

Stylianos Benakis

Vice President B'

Antonio Tsanis

Secretary General

Kyriaki Tavernaraki

Treasurer

Konstantinos Tsilikas

Members

Orestis Kavvadas

Penelope Lampropoulou

Christos Mpaltas

Fotios Takis

Secretary Specialists

Vassiliki Bizimi

Ioanna Staikidou

The Hellenic Radiological Society is the formal scientific and educational Society of Greek Radiolgists. It was founded in 1933

and aims to develop the highest Radiological standards, as well as to exchange scientific informationin all fields of Imaging

through training and research.

Hellenic Radiological Society, 21, P. Kiriakou str., 11521 Athens, Greece

E-mail: info@helrad.org, Tel.: (+30) 210 6451489, Fax: (+30) 210 6453092

H JR


Contents

original article

Anatomical Variability of Foramen Vesalius Using Cone Beam Computed Tomography

Karthikeya Patil, Sanjay C.J., Eswari Solayappan, Varusha Sharon Christopher,

Monica Mirnalini Mannar Nandagopalan 26-32

Plug-assisted Retrograde Transvenous Obliteration (PARTO) for Gastric Variceal Bleeding in Left-sided

Portal Hypertension: An Institutional Case Series

Vishal Nandkishor Bakare, Aniketh Davangere Hiremath, Ritesh Kumar Sahu, Rohan Rahul Thakur 34-43

Future of Diagnosis: Impact and Rise of Artificial Intelligence in Radiology

Aarzoo Tehlan, Anna T. Haimbodi, Abdullahi Abdullahi Idris, Airemy Taning,

Sanjhana Shree Tallabathulla, Yusuf Muhammad 44-54

Urinary bladder herniation: CT evaluation

Dimitrios Kourdakis, Maria-Michailia, Eleni Makridou,

Elena Hadjichristou, Ouroumidou Kristina, Manavi Aikaterini 56-63

Review

Recent advances and future perspectives of artificial intelligence in medical imaging: a review

Akhil Raj.P, Kamalesh.C, Renisha Divina Dsouza, Vashist Baburao Mhalsekar 64-76

Clinical Case - Test Yourself

Popliteal Artery Pathology: An Uncommon Yet Critical Clinical Challenge

Andrioti Petropoulou Nefeli, Papatheodorou Athanasios, Tsanis Antonios 78-83

A case of aberrant central venous catheter position on chest X-ray:

Ectopic placement or benign finding?

Belivanis Michail, Samaras Vaios, Roubanis Dimitrios 84-87

GUIDELINES FOR AUTHORS 88-91

25

H R J

Anatomical Variability of Foramen Vesalius Using Cone Beam Computed Tomography, p. 26-32

VOLUME 10 | ISSUE 4

Original Article

Neuroradiology

Anatomical Variability of

Foramen Vesalius Using Cone Beam

Computed Tomography


Monica Mirnalini Mannar Nandagopalan

Department of Oral Medicine and Radiology, JSS Dental College and Hospital

JSS Academy of Higher Education and Research

Mysuru, Karnataka, India

SUBMISSION: 16/08/2024 | ACCEPTANCE: 03/02/2025

Abstract

Purpose: This study aims to provide a detailed morphometric

analysis of the foramen Vesalius (FV) using

Cone Beam Computed Tomography (CBCT) to understand

its anatomical variations and clinical implications.

Material and Methods: A retrospective study was

conducted at JSS Dental College and Hospital, analyzing

140 CBCT scans (68 males, 72 females) of subjects aged

11-70 years. Inclusion criteria were diagnostic-quality

images showing the skull base with clear FV views.

Exclusion criteria included partial images, artifacts,

pathologies, or previous surgeries. Prevalence of FV,

unilateral/bilateral presentation, and distances to foramen

Ovale (FO) and foramen Spinosum (FS) were meas-

ured using Planmeca Romexis 5.3 software. Statistical

analysis was performed using SPSS version 23.

Results: AFV prevalence varied significantly by

sex, with bilateral occurrences being more common

(44.64%) than unilateral (18.72%) or absent (37.44%).

Males had higher bilateral FV (25.92%) and lower absent

FV (17.28%) compared to females (18.72% and

20.16%, respectively).

Mean distances between FV and FO were 3.176 mm

(right) and 4.689 mm (left) in females, and 3.922 mm

(right) and 4.699 mm (left) in males. Distances between

FV and FS were 11.966 mm (right) and 14.028 mm (left)

in females, and 13.063 mm (right) and 14.206 mm (left)

in males.

Corresponding

Author,

Guarantor

Dr. Karthikeya Patil, Professor and Head of Department, Department of Oral

Medicine and Radiology, JSS Dental College and Hospital, JSS Academy of Higher

Education and Research, Mysuru - 570015, Karnataka, India

Contact Number: +91 94498 22498

Email: dr.karthikeyapatil@jssuni.edu.in

26


VOLUME 10 | ISSUE 4

H R J

Age-related variations were observed, with the highest

bilateral FV prevalence in the 21-30 and 51-60 age

groups.

Conclusions: The morphometric analysis of FV highlights

significant anatomical variations by sex and age,

emphasizing the need for precise surgical planning.

Understanding these variations can minimize procedural

risks and optimize treatment outcomes in neurosurgical

and radiological interventions.

Keywords: : Foramen vesalius, Cone beam computed

tomography, Foramen ovale, Foramen spinosum, Anatomical

variation

Introduction

The skull base, divided into the anterior, middle, and

posterior fossae, contains numerous foramina, with the

middle fossa housing critical neurovascular structures.

Within the intricate structure of the sphenoid bone, the

foramen rotundum, foramen ovale (FO), and foramen

spinosum (FS) serve as permanent apertures. Additionally,

two foramina are non-permanent: the meningo orbital

(Hyrtl's channel) and the Vesalius foramina, also

known as sphenoidal emissary foramen, foramen venosus,

or canaliculus sphenoidal. These foramina, with

their diverse functions and characteristics, contribute

significantly to the intricate network of passages and

channels within the skull base, facilitating crucial neuro-vascular

interactions and pathways [1].

The foramen of Vesalius is a tiny, variable foramen in

the middle cranial fossa, posterolateral to the foramen

rotundum and anteromedial to the foramen ovale (FO),

and it transmits emissary veins that connect the pterygoid

plexus and the cavernous sinus. The anatomist Andreas

Vesalius was the one who initially described and

depicted the FV. The FV may be bilateral, unilateral, or

absent at times [2].

Regarded as a promising modality for the analysis of

the foramen Vesalius, CBCT provides three-dimensional,

high-resolution imaging. CBCT facilitates precise

evaluation of the dimensions, configuration, and fluctuations

of the foramen Vesalius through the provision

of distinct and accurate visualisations of anatomical

structures.

This technology allows for non-invasive evaluation,

aiding in the diagnosis of anomalies or pathologies associated

with the foramen Vesalius. Furthermore, CBCT

facilitates an enhanced understanding of the spatial re-

lationships between the foramen Vesalius and neighboring

structures, offering valuable insights for surgical

planning and interventional procedures. Its ability

to provide comprehensive imaging data makes CBCT a

valuable tool in the analysis of foramen Vesalius morphology

and pathology [3].

By employing CBCT technology, this study aimed to

provide precise measurements and detailed three-dimensional

reconstructions of the foramen Vesalius,

elucidating any population-specific anatomical variations.

This study was also aimed to contribute to the

understanding of cranial anatomy, potentially informing

clinical practice, surgical interventions, and diagnostic

approaches related to neuro-vascular conditions

involving the foramen Vesalius.

Material and Methods

This monocentric, descriptive retrospective study

was conducted at JSS Dental College and Hospital and

approved by the institutional ethics committee (reference

number 22/2023). A convenient sampling technique

was used, assuming an absolute precision of 5%

and a confidence level of 95%. 140 CBCT scans, 68 males

and 72 females, were estimated for the sample size and

were utilized between June 2022 and June 2023, which

fulfilled the following criteria.

Inclusion Criteria

CBCT images of male and female subjects aged between

11 and 70 years.

CBCT images of diagnostic quality.

Exclusion Criteria

CBCT images with artifacts or subpar diagnostic

quality.

CBCT images that do not reveal the base of the skull

with a clear view of the expected location of the foramen

Vesalius (FV).

Partial images or the presence of artifacts in the

skull's base.

CBCT images with any pathology or developmental

defects of the base of the skull.

Images with any evidence of previous surgery, fracture,

or healed fracture in the base of the skull.

Radiographic images satisfying the inclusion criteria

were subjected to analysis for the following landmarks

in the axial section in 3D-rendered images of Planmeca

Romexis 5.3 (3D software) for the prevalence of FV, uni-

27

H R J


VOLUME 10 | ISSUE 4

(CBCT of axial images; Fig A- Showing absence of FV, B- Showing unilateral presence of FV,

C- Showing distance between FV to FO, D- Showing distance between FV to FS, E- showing bilateral presentation of FV).

(Note: red circle- denotes Foramen Vesalius, Yellow circle- denotes Foramen Spinosum, Asterisk denotes Foramen Ovale)

lateral (Fig. B) or bilateral (Fig. E) presentation among

different age groups and genders. If it exists, the distance

between FV to FO (Fig. C) and FV to Foramen Spinosum

(FS) (Fig. D) was evaluated.

Statistical Analysis

Descriptive statistics were done to establish the mean

and median for all the demographic and quantitative

data. Mean differences between the left and right sides

were evaluated using a paired sample t-test. Age and

gender were also analyzed qualitatively with the chisquare

test to determine the presence of FV.

A p-value of less than 0.05 was considered significant.

The statistical analysis software employed was SPSS

version 23.

Results

There was a notable disparity in the distribution of foramen

Vesalius variations between males and females.

While bilateral occurrences were more prevalent in

both sexes compared to unilateral or absent occurrences,

there were distinct differences in their frequencies.

Females demonstrated a slightly higher incidence of

absent foramen Vesalius (Fig. A), but lower bilateral occurrences

compared to males. Conversely, males exhibited

a higher prevalence of bilateral occurrences and

lower rates of absent occurrences. Additionally, unilateral

occurrences were substantially higher in males

than in females. Although the p-value of 0.059 indicated

a trend towards significance, further investigation with

a larger sample size is warranted to confirm these findings

and elucidate any potential sex-based differences

in foramen Vesalius morphology (Table 1).

Table 1. Prevalence of Unilateral or Bilateral

Foramen Vesalius.

Sex

Absent

(in %)

Bilateral

(in %)

Unilateral

(in %)

Female 20.16 18.72 12.96

Male 17.28 25.92 5.76

Total 37.44 44.64 18.72

p-value

0.059

The analysis of the frequency distribution of foramen

Vesalius variations among various age groups revealed

that bilateral occurrences of foramen Vesalius are

more prevalent than unilateral or absent occurrences

across all age groups. The highest prevalence of bilateral

occurrences was observed in the 21-30 and 51-60

age groups, while absent occurrences are most prominent

in the 11-20 age group. Unilateral occurrences

28


VOLUME 10 | ISSUE 4

H R J

varied across age groups, with notable percentages in

the 21-30 and 11-20 age groups. Although the result

approaches statistical significance, it is not statistically

significant under the conventional threshold of p <

0.05. (Table 2).

Table 2. Prevalence of Foramen Vesalius Among

Different Age Groups.

Age group

(in years)

Absent

(in %)

Bilateral

(in %)

Unilateral

(in %)

p-value

Morphometric measurements related to foramen

Vesalius morphology across age groups revealed that

the mean distances between foramen Vesalius and foramen

Ovale (FV-FO) and foramen Spinosum (FV-FS)

ranged from approximately 3.218 mm to 5.345 mm and

11.851 mm to 15.285 mm, respectively. Standard deviations

indicate variability around these means, with values

ranging from 0.239 mm to 3.844 mm. Minimum and

maximum distances vary across age groups, reflecting

morphological differences (Table 4).

11-20 1.44 2.88 4.32

21-30 11.52 14.92 5.76

31-40 8.64 8.64 2.88

41-50 5.76 5.76 4.32

0.091

Table 4. Descriptive statistics of Foramen

Vesalius based on different age groups.

Parameters

Age

(in years)

Mean

(in mm)

Standard

Deviation

(in mm)

Minimum

(in mm)

Maximum

(in mm)

51-60 4.32 10.72 1.44

61-70 5.76 5.76 0

Bilateral prevalence of FV relative to nearby structures

by gender and side is summarized in Table 3. Females

generally have slightly smaller mean distances

compared to males. For instance, between FV and FO,

females average 3.176 mm (right) and 4.689 mm (left),

while males average 3.922 mm (right) and 4.699 mm

(left). Standard deviations suggest variability, providing

insights into gender and side-based anatomical differences.

Table 3. Bilateral Prevalence of Foramen

Vesalius Based on Gender and Side.

Right

FV-FO

Left

FV-FO

11-20 3.755 1.241 2.680 4.830

21-30 3.218 1.699 1.700 7.690

31-40 3.263 0.884 2.150 4.560

41-50 3.192 1.011 2.560 4.820

51-60 4.946 2.301 2.530 7.850

61-70 3.777 1.379 2.680 5.910

11-20 3.690 1.951 2.00 5.380

21-30 4.218 1.880 2.00 8.410

31-40 4.600 0.883 3.690 6.250

41-50 6.002 1.761 4.120 8.540

51-60 4.598 0.239 4.250 4.820

61-70 5.345 1.580 3.770 7.770

11-20 12.520 1.062 11.600 13.440

Parameters

Right

FV-FO

Sex

Mean

(in mm)

Standard

Deviation

(in mm)

Minimum

(in mm)

Maximum

(in mm)

Female 3.176 1.114 2.00 5.90

Male 3.922 1.858 1.70 7.9

Right

FV-FS

21-30 11.851 2.902 6.510 18.420

31-40 12.420 1.242 11.030 14.700

41-50 13.295 1.052 11.760 14.340

51-60 14.032 3.844 8.780 18.970

Left

FV-FO

Right

FV-FS

Left

FV-FS

Female 4.689 1.390 2.04 7.8

Male 4.699 1.727 2.00 8.5

Female 11.966 2.500 6.50 16.4

Male 13.063 2.573 8.80 18.9

Female 14.028 1.654 11.60 17.8

Male 14.206 2.400 10.90 18.9

Left

FV-FS

61-70 12.323 2.628 10.000 16.420

11-20 15.285 2.950 12.730 17.840

21-30 14.261 1.803 11.560 18.180

31-40 13.097 1.582 10.920 14.750

41-50 14.755 2.878 12.320 18.930

51-60 13.828 1.991 12.270 17.550

61-70 14.476 2.334 11.560 17.760

29

H R J


VOLUME 10 | ISSUE 4

The results indicate a statistically significant difference

between the distances of the right and left sides

for both the foramen Vesalius to foramen Ovale (FV-

FO) and foramen Vesalius to foramen Spinosum (FV-FS)

measurements. The t-values of -4.823 and -5.345, with

degrees of freedom (df) at 61 for both comparisons, suggest

a substantial difference between the mean distances

of the right and left sides. Additionally, the p-values

of less than 0.001 signify a highly significant finding,

indicating that the observed differences are unlikely to

be due to random chance (Table 5).

Table 5. Paired t-test for Bilateral Prevalence.

mm (SD 1.995 mm). FV-FS distances for females average

13.429 mm (SD 1.913 mm), males 14.107 mm (SD 1.727

mm). Minimum and maximum distances show comparable

ranges across sexes for both measurements (Table

6).

Table 6. Descriptive Statistics of Unilateral

Prevalence of FV Based on Gender.

Parameters

FV-FO

Sex

Mean

(in mm)

Standard

Deviation

(in mm)

Minimum

(in mm)

Maximum

(in mm)

Female 4.362 1.916 2.680 8.770

Male 4.777 1.995 2.830 7.770

Parameters t df p-value

Right-Left

FV-FO

-4.823 61 <0.001

FV-FS

Female 13.429 1.913 10.810 17.120

Male 14.107 1.727 12.350 16.560

Right-Left

FV-FS

-5.345 61 <0.001

Distances between the foramen Vesalius (FV) and

neighboring structures, foramen Ovale (FO) and foramen

Spinosum (FS), by sex. Females exhibit a mean

FV-FO distance of 4.362 mm (SD 1.916 mm), males 4.777

Unilateral FV measurements across age groups are

presented in Table 7. Mean FV-FO measurements rise

from 3.777 mm (11-20 age group) to 6.995 mm (31-40),

then decrease to 2.830 mm (51-60). Similarly, mean FV-

FS measurements peak at 13.897 mm (11-20) and decline

to 12.350 mm (51-60), indicating age-related variations.

Table 7. Descriptive statistics of unilateral Foramen Vesalius based on different age groups.

Parameters

Age

(in years)

Mean

(in mm)

Standard

Deviation

(in mm)

Minimum

(in mm)

Maximum

(in mm)

P Value

11-20 3.777 1.547 2.680 5.770

21-30 4.790 1.873 3.390 7.770

FV-FO

31-40 6.995 2.050 5.220 8.770

0.619

41-50 3.687 0.878 3.120 4.820

51-60 2.830 0.000 2.830 2.830

11-20 13.897 2.824 10.810 17.120

21-30 14.055 1.891 11.890 16.560

FV-FS

31-40 13.635 1.819 12.060 15.210

0.399

41-50 13.253 0.979 12.450 14.500

51-60 12.350 0.000 12.350 12.350

30


VOLUME 10 | ISSUE 4

H R J

Discussion

The greater wing of the sphenoid bone has an inconstant

aperture known as the FV. An emissary vein travels

through the foramen and interacts with the cavernous

sinus with the help of the pterygoid plexus. The embryonic

development of these foramina begins with the alisphenoid

cartilage and obturator membrane. During the

formation of the cranial base, nerves and arteries create

orifices at the intersection of several embryonic components.

Hence, radiologic imaging is a crucial diagnostic

tool for presurgical, intrasurgical, and postoperative evaluations,

focusing on anatomical features, variations, and

neurovascular structures [4].

The FV carries a small emissary vein that connects the

cavernous sinus to the pterygoid plexus, which helps to

regulate intracranial and extracranial pressure. The emissary

veins have minimal blood flow during normal physiological

settings, but when intracranial pressure rises,

these veins play a significant role in blood drainage [5].

This vein may transfer an infected thrombus from the

extracranial region to the cavernous sinus. Because this

foramen is inconstant, it can complicate a surgical procedure

in this area if the practitioner lacks solid anatomical

conceptualization [6,7].

Maletin et al. reviewed 500 CT images of adults and observed

that the FV was present in 67.7% of instances [8];

similarly, our study shows 63.36%. These findings are consistent

with those reported by Görürgöz et al. [9], Lanzieri

et al. [10], and Raval et al. [11], who found FV of about

73.1%, 61.54% and 64% of those surveyed, respectively. In

contrast to our study, Shinohara et al. [12] and Shaik et al.

[13] reported that foramen prevalence is 33.75% and 36%,

respectively, which is considerably lower than the current

study’s results. They claimed that the lower incidence rate

could be due to the different methodology, where they

evaluated the dry skull; another reason suggested was that

impairment in the development of venous drainage organization

would lead to the existence of FV [12]. As a result,

we could anticipate some degree of heterogeneity in the

information provided by numerous authors.

Maletin found that bilateral FV was significantly more

common in males and unilateral in females [8], which is

in accordance with the current study, where bilateral FV

in males shows 25.92% and unilateral FV in females shows

12.96%. When different age groups were analyzed, the

second and third decades show higher prevalence rates,

yet there were no statistically significant differences between

age groups and genders.

The FV is situated anteromedially to the FO at a mean

distance of 3.54±1.48mm on the right side and 4.69±1.55mm

on the left side, which shows statistical significance between

the right and left sides. Similarly, the study by Rossi

et al. showed that the FV-FO distance on the right side of

the skull was less than that on the left side [14]. However,

the study by Shinohara et al. showed no significant difference

in the average FV-FO distance between the left

and right sides [12]. It could be due to the low prevalence

rate of FV observed in their study, as well as the ethnic

variances. In such cases larger sample size was advised to

analyze between the right and left sides.

The foramen ovale serves as the entry point for the surgical

treatment of trigeminal neuralgia. However, when

approaching this foramen, one may misplace the needle

for microvascular decompression inside the foramen Vesalius.

This proximity can lead to a puncture of the cavernous

sinus, potentially causing serious complications

such as intracranial bleeding [15]. Thus, the distance between

FV and FO plays a crucial role.

The other structure in close proximity to the FV is the

foramen spinosum, located posterolateral to the FO and

anteromedial to the spine of the sphenoid bone. Crucial

blood vessels that permeate the dura mater emerge from

the FS [1]. In the current study, the average distance between

FV-FS in males is 13.63±2.48mm, and in females it

is 12.51±2.07mm, which is statistically insignificant between

sexes. Shinohara found 11.52mm on the right side

and 10.95mm on the left side; contrarily, our study shows

12.51±2.5mm on the right side and 14.11±2.02mm on the

left side, showing statistically significant differences

among sides (P<0.001) [12]. This can be explained by the

fact that the higher values in men are due to their comparatively

bigger craniocaudal dimensions than those in

women.

Conclusion

The morphometric analysis of the foramen Vesalius reveals

clinically critical findings, shedding light on its anatomical

variations and potential implications. Variations

in distances between the foramen Vesalius and adjacent

structures, such as the foramen Ovale and the foramen

Spinosum, underscore the importance of precise surgical

planning and intervention. Understanding these morphometric

characteristics aids in minimizing procedural

risks and optimizing treatment outcomes, particularly in

31

H R J


VOLUME 10 | ISSUE 4

neurosurgical and radiological interventions. Moreover,

recognizing sex-based differences in these measurements

emphasizes the need for tailored approaches in patient

care. Overall, this study emphasizes the critical role of foramen

Vesalius morphology in clinical practice, guiding

more effective patient management strategies. R

References

1. Standring S. Gray’s Anatomy: The Anatomical Basis of

Clinical Practice. Elsevier Limited; 2016. 1562 p.

2. Vesalius A, Richardson WF, Carman JB. On the Fabric

of the Human Body: A Translation of De Humana

Corporis Fabrica Libri Septem. The veins and arteries.

The nerves. Norman Pub.; 2002. 286 p.

3. Kamburoğlu K, Kolsuz E, Kurt H, Kiliç C, Özen T, Paksoy

CS. Accuracy of CBCT measurements of a human

skull. J Digit Imaging. 2011 Oct;24(5):787–93.

4. do Nascimento JJC, da Silva Neto EJ. Foramen Venosum

in macerated skulls from the North-East of Brazil: morphometric

study. Eur J Anat [Internet]. 2018; Available

from: https://eurjanat.com/data/pdf/eja.170136na.pdf

5. Reis CVC, Deshmukh V, Zabramski JM, Crusius M,

Desmukh P, Spetzler RF, et al. Anatomy of the mastoid

emissary vein and venous system of the posterior

neck region: neurosurgical implications. Neurosurgery.

2007 Nov;61(5 Suppl 2):193–200; discussion 200–1.

6. Gusmão S, Magaldi M, Arantes A. Trigeminal radiofrequency

rhizotomy for the treatment of trigeminal

neuralgia: results and technical modification. Arq

Neuropsiquiatr. 2003 Jun;61(2B):434–40.

7. Laslo JM CLM. Bilateral duplication of the sphenoidal

emissary foramen: A case report with implications

for surgeries using transovale cannulation.

Anat Physiol [Internet]. 2014;04(04). Available from:

https://www.academia.edu/download/67048924/bilateral-duplication-of-the-sphenoidal-emissary-foramen-a-case-report-with-implications-for-surgeries-using-transovale-cannulation-2161.pdf

8. Maletin M, Vuković M, Sekulić M. Morphological

characteristics of foramen Vesalius in dry

adult human skulls. Medicinski [Internet]. 2019;

Available from: https://doiserbia.nb.rs/Article.

aspx?ID=0025-81051912357M

9. Görürgöz C, Paksoy CS. Morphology and morphometry

of the foramen venosum: a radiographic study of

CBCT images and literature review. Surg Radiol Anat.

2020 Jul;42(7):779–90.

10. Lanzieri CF, Duchesneau PM, Rosenbloom SA, Smith

AS, Rosenbaum AE. The significance of asymmetry of

the foramen of Vesalius. AJNR Am J Neuroradiol. 1988

Nov-Dec;9(6):1201–4.

11. Raval BB, Singh PR, Rajguru J. A morphologic and

morphometric study of foramen vesalius in dry adult

human skulls of gujarat region. J Clin Diagn Res. 2015

Feb;9(2):AC04–7.

12. Shinohara AL, de Souza Melo CG, Silveira EMV, Lauris

JRP, Andreo JC, de Castro Rodrigues A. Incidence,

morphology and morphometry of the foramen of

Vesalius: complementary study for a safer planning

and execution of the trigeminal rhizotomy technique.

Surg Radiol Anat. 2010 Feb;32(2):159–64.

13. Shaik HS, Shepur MP, Desai SD, Thomas ST, Maavishettar

GF, Haseena S. Study of foramen Vesalius in

South Indian Skulls. IJMH. 2012 Apr 1;22–4.

14. Rossi AC, Freire A, Prado F, Caria PH, Botacin PR. Morphological

characteristics of foramen of Vesalius and

its relationship with clinical implications. Journal of

Morphological Sciences. 2010 Dec 1;26–9.

15. del Cráneo Humano FE. Emissary Foramens of the Human

Skull: Anatomical Characteristics and its Relations

with Clinical Neurosurgery. Int J Morphol [Internet].

2013; Available from: https://www.academia.

edu/download/79285926/86d7a9f10a58f0681c7ecc7558b60b66418f.pdf

Ready - Made

Citation


Monica Mirnalini Mannar Nandagopalan. Anatomical Variability of Foramen

Vesalius Using Cone Beam Computed Tomography, Hell J Radiol 2025; 10(4): 26-32.

32

H R J

Plug-assisted Retrograde Transvenous Obliteration (Parto) for Gastric Variceal Bleeding

in Left-sided Portal Hypertension : An Institutional Case Series, p. 34-43

VOLUME 10 | ISSUE 4

Original Article

Gastrointestinal Imaging

Plug-assisted Retrograde Transvenous

Obliteration (PARTO) for Gastric

Variceal Bleeding in Left-sided

Portal Hypertension:

An Institutional Case Series

Vishal Nandkishor Bakare, Aniketh Davangere Hiremath,

Ritesh Kumar Sahu, Rohan Rahul Thakur

Dr. D. Y. Patil Hospital & Research Centre


Abstract

Purpose: This case series aims to evaluate the procedural

safety and clinical outcomes of plug-assisted retrograde

transvenous obliteration (PARTO) in patients

presenting with severe upper gastrointestinal bleed secondary

to left sided portal hypertension (LSPH) following

unsuccessful conservative and endoscopic management.

Material and Methods: The study includes 4 patients

presenting with acute upper gastrointestinal bleed and

LSPH. Pre-procedural imaging workup was performed

to confirm the presence of gastro-renal shunt and rule

out high-risk anatomical factors. After failure of endoscopic

management, PARTO was performed with subsequent

gel-foam embolization of the gastric varices.

Post-procedural outcomes were noted at 1 month and

3 months.

Results: All four patients showed significant clinical

improvement post procedure with no recurrent bleeding.

On 1 month and 3 months follow up, one patient

experienced progression in ascites and another patient

progression in esophageal varices requiring endoscopic

banding at 2 months. All patients had reduction in

size of gastric varices at 3 months follow up endoscopy.

Overall PARTO demonstrated high clinical success rate

with minimal complications.

Conclusions: PARTO has emerged as a safe and promising

solution treating gastro-variceal bleed secondary

Corresponding

Author,

Guarantor

Dr. Aniketh Davangere Hiremath University, Gurugram

Department of Interventional Radiology. Dr. D. Y. Patil Hospital & Research Centre,

Sant Tukaram Nagar, Pimpri Colony, Pimpri-Chinchwad, Maharashtra 411018

Email: kyathadh@gmail.com

34


in Left-sided Portal Hypertension : An Institutional Case Series, p. 34-43.

VOLUME 10 | ISSUE 4

H R J

to left-sided portal hypertension and gastro-renal shunt

draining anatomy, particularly after failed endoscopic

management. The technique has high clinical success

rate, good short term clinical outcome and associated

with minimal complications.

Keywords: Gastric varices, Left-sided portal hypertension,

Vascular plug, Embolization, Gastro-renal

shunt.

Introduction

Left-sided portal hypertension (LSPH), also known as

sinistral hypertension, is a rare condition that is often

misdiagnosed as generalized (right-sided) portal hypertension.

Most patients are asymptomatic or have

vague abdominal pain, whereas others present with

life-threatening upper gastrointestinal (UGI) bleeding.

They are detected incidentally on imaging or while

investigating for unexplained UGI bleeding or splenomegaly.

The overall incidence of LSPH is less than 5%

in all patients with portal hypertension [1]. As most patients

are non-cirrhotic (80%), ascites is rarely seen unless

they have concomitant dilutional hypoalbuminemia

of any etiology. Spleno-portal venous obstruction

(secondary to thrombosis, chronic pancreatitis, pancreatic

pseudocysts, and pancreatic neoplasms) is often

the main pathophysiological factor in the development

of gastric varices. A progressive increase in left-sided

portal pressure leads to shunting of blood from the

spleen to the short gastric veins (posterior gastric, left

gastric, and superior gastric veins). Gastric varices that

develop in the fundal region are more lethal at presentation

than esophageal varices, and have a higher

tendency to bleed with poor patient outcomes [2,3,4].

These patients often face anatomical limitations that

make traditional endoscopic interventions difficult and

less effective, particularly in cases with large varices or

poor visibility during severe bleeding.

Vascular plug-assisted retrograde transvenous obliteration

(PARTO) is emerging as safe, minimally invasive

alternative for patients in whom conservative and

endoscopic management has failed. The technique involves

deploying a vascular plug in the gastro-renal

shunt, followed by gel foam embolization of gastric

varices. This is a case series of 4 patients presenting

with gastro-variceal bleeding secondary to left-sided

portal hypertension, all of whom underwent successful

PARTO after unsuccessful endoscopic management.


Patient selection: This is a retrospective study on 4

patients conducted between May 2023 and May 2024.

All patients underwent pre- and post-procedural imaging

with contrast-enhanced computed tomography

(CT).

Pre-procedural evaluation: A thorough anatomical

evaluation of gastro-renal shunt using computed tomography

is recommended with importance given to

the following parameters (Fig 1): (a) minimum and (b)

maximum diameter of GRS proper, (c) diameter of common

stump of adrenal vein and GRS proper, (d) shunt

distance from the left renal vein origin (e) Left renal

vein diameter, (f) Left renal vein and shunt orientation

and angulation.

Fig. 1: Illustrative diagram of gastro-renal shunt: (A)

minimum and (B) maximum diameter of GRS proper,

(C) diameter of common stump of adrenal vein and GRS

proper, (D) shunt distance from the left renal vein origin

(E) Left renal vein diameter.

Aneurysmal dilatation of the left renal vein (diameter

greater than 50% of the normal calibre or an unaffected

segment), unfavourable shunt angulation (either too

acute <60 or obtuse >120), and out-of-plane orientation

of shunt origin with LRV (anterior/posterior directed)

were identified as significant risk factors for procedural

failure [5].

This evaluation minimizes procedural risks, improves

technical success, and enhances the likelihood

of achieving effective variceal obliteration.

35

H R J



VOLUME 10 | ISSUE 4

PARTO technique

The procedures were performed under local anesthesia,

and all four patients underwent right femoral venous

access. Gastro-renal shunt was accessed via the left

renal vein and common stump (with adrenal vein) using

5Fr Cobra or Simmons Angio catheter with 180cm 0.035’

guide wire (Terumo, Tokyo, Japan). An appropriately

sized 7-10Fr Long Vascular sheath (Flexor Check-Flo or

shuttle sheath; Cook, Bloomington, IN, USA) is selected

based on the size of the vascular plug for deployment.

The size of the vascular plug device was determined after

appropriately upsizing it by 30-50% relative to the narrowest

part (waist) of the gastro-renal shunt. Long vascular

sheath was wedged in the common adrenal vein or

GRS over a 260cm, 0.035-inch Amplatz Extra-Stiff guidewire

(Cook, Inc) for vascular plug deployment and a 2.7Fr

microcatheter (Direxion, Boston Scientific, Natick, MA)

was placed distal to the plug for variceal embolization.

In cases of difficult or tortuous anatomy, the sheath can

be advanced into the GRS using two 0.035 guidewires.

Amplatzer Vascular Plug Type II (AGA Medical, Golden

Valley, MN, USA) was used in all the patients (Fig 2). Retrograde

venography is performed using the long sheath

placed inside the GRS after occluding the common adrenal

vein/GRS with a vascular plug. Anatomy of GRS,

dominant collateral veins, efferent and afferent pathways

were delineated on venography. After confirming

appropriate occlusion of the GRS by microcatheter venography,

gastric variceal embolization was performed

using a combination of contrast and gel-foam pledgets

injected through the same microcatheter. The endpoint

of embolization was complete opacification of gastric

varices on fluoroscopy with slow opacification and visualization

of the left gastric vein or posterior gastric vein

(Fig 3). In situations where a number of collateral veins,

including the pericardiophrenic, inferior diaphragmatic,

and intercostal veins, are efferent feeders, gel foam can

be slowly injected intermittently with contrast to ensure

stasis and occlusion of these veins.

Case 1:

A 47-year-old female patient presented to the emergency

department with a 5-day history of giddiness and

vomiting along with massive hematemesis that occurred

few hours back. Hematemesis was likely secondary to

severe retching, leading to upper gastrointestinal bleed-

Fig. 2: Illustrative diagram of vascular plug deployment

(red) across the waist of the gastro-renal shunt proper

using long sheath (orange). A jailed microcatheter (green)

is placed distal to the plug for gastric variceal gel-foam

embolization.

Fig. 3: Illustrative diagram of PARTO demonstrating

vascular plug deployment at the gastro-renal shunt with

gel-foam embolization of gastric varices (GV). The end

point variceal embolization (PARTO/BRTO) is gradual

visualisation of left gastric/ posterior gastric veins. (PV:

Portal vein; SV: splenic vein; IMV: inferior mesenteric vein;

SMV: superior mesenteric vein; LGV: left gastric vein: PGV:

posterior gastric vein: SGV: superior gastric vein; LRV: left

renal vein; IVC: inferior venacava).

36



VOLUME 10 | ISSUE 4

H R J

ing. The patient was severely anemic (hemoglobin: 6 g%),

with a heart rate of 110bpm and blood pressure of 90/56

mmHg. In addition, there was a history of severe mitral

regurgitation and anemia, which may have contributed

to the patient's symptoms. The patient’s vital signs were

initially stabilized with blood transfusions and emergency

endoscopy was planned. After failure of endoscopic

management due to a difficult anatomy and poor field

of vision due to massive hemorrhage, the patient was

referred to the interventional radiology department for

the modified BRTO/PARTO procedure. Preprocedural

computed tomography was performed to confirm and

delineate the anatomy of the gastro-renal shunt. On CT

images, isolated gastric varices were noted predominantly

draining into the left renal vein with a waist size

of 9.2 +/- 0.5 mm. A 14 mm Amplatzer Vascular Plug Type

II (AGA Medical, Golden Valley, MN, USA) was deployed

across the waist (narrowest part) of the GRS. Retrograde

venography was performed to confirm adequate occlusion

of the gastro-renal shunt, and gastric variceal embolization

was performed using a combination of contrast

and gel foam pledgets. (Fig 4). The patient’s vital

signs were monitored. The patient remained asymptomatic

during the rest of the hospital stay with improving

Fig. 4: A. Post contrast CT coronal section showing isolated gastric varices with splenomegaly (leftsided

portal hypertension).

B. Placement of 4Fr diagnostic catheter in the gastro-renal shunt and its position confirmed by

a check venogram.

C. Placement of Long sheath (arrow) at the origin of gastro-renal shunt. Common stump with

adrenal vein tributaries are seen in profile (arrow head).

D. Deployment of vascular plug type II across the waist of the gastro-renal shunt (arrow) and

check venogram demonstrating complete occlusion the gastro-renal shunt. Microcatheter

placement inside the gastric varices.

E. Slow embolization of gastric varices a the microcatheter with gel-foam and contrast.

F. Complete visualization of gastric varices with slow opacification of posterior gastric vein.

Procedure was concluded at this point.

37

H R J



VOLUME 10 | ISSUE 4

haemoglobin levels. Follow-up endoscopy and computed

tomography were performed at 1 month, which showed

a significant decrease in gastric varices with no recurrence

of symptoms or gastrointestinal bleeding.

Case 2:

A 45-year-old male patient with history of decompensated

chronic liver disease secondary to chronic alcoholism

presented to the emergency department with recurrent

episodes of hematemesis which increased from

the last 2 days. The patient’s haemoglobin was 8.9 g%,

had mild tachycardia (82 bpm) but otherwise vitally stable

(blood pressure 110/80mmHg). The patient had undergone

failed endoscopic glue embolization of gastric

varices on two separate occasions in the last 3months.

On CT images, a large isolated gastric varix was noted

draining via the gastro-renal shunt into left renal vein

with waist size of 8.6 +/- 0.5mm. The patient was referred

to interventional radiology for endovascular management.

In the interventional suite, gastro-renal shunt was

cannulated via the right femoral venous access. A 12mm

sized Amplatzer vascular plug Type II was deployed

across the shunt and subsequent gastric variceal embolization

was done using gel-foam and contrast (Fig 5). Post

procedure the patient developed low grade fever which

was alleviated with antipyretic medications. The patient

recovered well the next day with no fresh episodes of

bleeding in the remainder of the hospital stay.

Fig. 5: A. Post contrast CT coronal sections showing isolated gastric varices with splenomegaly (leftsided

portal hypertension).

B. Placement of 4Fr diagnostic catheter in the gastro-renal shunt and its position confirmed by

a check venogram.

C. Deployment of vascular plug type II across the waist of the gastro-renal shunt and check

venogram to confirm complete occlusion the gastro-renal shunt.

D. Left oblique view of gastric varices with gel-foam and contrast.

E. AP view of gastric varices with gel-foam and contrast.

F. Faint visualization of left gastric vein (red arrow) with stasis of contrast in the gastric

varices.

38



VOLUME 10 | ISSUE 4

H R J

Case 3:

A 29-year-old young male patient presented to the

outpatient department with 2-day history of hematemesis,

melena and decreased bowel movements. Ultrasonography

was performed which revealed shrunken

liver with portal hypertension, splenomegaly and mild

ascites. CT revealed large tortuous varices in the gastric

fundus region.

In view of difficult anatomy for endoscopic management,

patient was referred to interventional radiology.

A 14mm sized Amplatzer vascular plug Type II was deployed

across the shunt, relative to GRS waist size of 9.0

+/- 0.5mm. Gastric variceal gel-foam embolization was

performed (Fig 6). Post procedure was uneventful and

patient recovered well.

Case 4:

A 56-year-old male patient presented to the emergency

department with intermittent hematemesis since

the last 2days. The patient was a known case of chronic

parenchymal liver disease and had undergone endoscopic

esophageal banding twice in the last one year.

Emergency endoscopy was performed and large gastro-esophageal

varices were visualised. Subsequently

glue embolization was performed. The patient recovered

well for two days but presented again with he-

Fig. 6: A. Post contrast CT coronal images showing isolated gastric varices with splenomegaly (leftsided

portal hypertension)

B. The GRS commonly does not empty directly into the left renal vein (LRV) but actually

empties in the LRV via a common stump with the left adrenal vein (black arrow).

C. Identification of GRS by rapid washoff of contrast along the lateral wall of the common

stump (asterisk).

D. Deployment of vascular plug type II across the waist of the gastro-renal shunt (red

arrow) and check venogram demonstrating complete occlusion the gastro-renal shunt.

Microcatheter placement inside the gastric varices (yellow arrow)

E. Slow embolzation of gastric varices a the microcatheter with gel-foam and contrast.

F. Complete visualization of gastric varices with stasis of contrast,

39

H R J



VOLUME 10 | ISSUE 4

matemesis. The patient was referred to interventional

radiology for further management. CECT abdomen

was performed which revealed large gastro-esophageal

varices (GOV type 2) with gastro-renal shunt as dominant

drainage pathway. Patient was immediately transferred

to interventional suite. A 14mm sized Amplatzer

vascular plug device Type II was deployed across the

shunt, relative to GRS waist size of 10 +/- 0.5mm. Gastric

variceal gel-foam embolization was performed. Patient

recovered well after the procedure.

Post Procedure Follow Up

All 4 patients were followed up at 1 month and 3

months to evaluate gastro-renal shunt occlusion and for

any procedure related complications such as hematoma

or systemic thrombosis. Progression in size of esophageal

varices or appearance of any other ectopic varices

were evaluated with endoscopy and computed tomography,

upon the discretion of the treating physician. Median

interval of endoscopy from the procedure was at

1 month. All 4 patients had symptomatic improvement

and significant reduction in size of the gastric varices

on endoscopy and computed tomography. Two patients

had complete resolution and other two patients had significant

reduction in size of gastric varices. One patient

underwent banding for esophageal varices at 2 month

follow up period. One patient had progression in ascites

at 1 month follow up. Medical tests were done at 1 month

and 3 months to look for changes in liver function tests

or signs of recurrent bleeding, which revealed no significant

interval changes.

Discussion

Variceal bleeding is fairly common and dreaded complication

of portal hypertension. Gastric varices appear

in 20-30% of patients with cirrhosis and portal hypertension.

While the exact mortality rates vary across

studies, gastric variceal bleeding is associated with

significant mortality and morbidity ranging between

8% and 35% depending on the treatment approach and

follow-up period [6,7]. Gastric varices are less common

than esophageal varices, but they are more severe at

presentation which can rupture and result in massive

gastrointestinal bleeding requiring timely and effective

interventions for the survival of the patient.

Gastric varices can be classified based on their location,

into gastroesophageal varices (GOV) and isolated

gastric varices (IGV). Sarin et al.'s classification is the

most commonly used to classify gastric varices. Gastroesophageal

varices are extensions of esophageal varices

and are termed GOV type 1(GOV1) when they extend

below the gastroesophageal junction along the lesser

curvature, and GOV type 2(GOV2) when they extend

into the fundus of the stomach. Isolated gastric varices

(IGV) located in the fundus of the stomach are called

IGV type 1(IGV1) or commonly referred to as fundal

varices. IGV type 2(IGV2) is an ectopic varix located anywhere

in the stomach. GOV1 represents almost 75% of

all gastric varices, followed by GOV2 (21%), IGV1 less

than 2%, and IGV2, which comprises 4% [8].

First line of management of gastric variceal bleed is

medical therapy and endoscopic N-cyanoacrylate glue

embolization [9,10,11]. Interventional radiology techniques

are employed when endoscopy fails, which includes

transjugular intrahepatic portosystemic shunt

(TIPS) and balloon retrograde transvenous obliteration

(BRTO). Of the two, BRTO is preferred in East Asian

countries because it is less invasive, cost effective and

has a better survival rate.

Plug-assisted retrograde transvenous obliteration

(PARTO) is an emerging modification of the BRTO technique

for the treatment of gastric variceal bleeding. It

involves occlusion of the gastro-renal shunt using a vascular

plug device and subsequent embolization of the

varices using gel foam or sclerosant. Compared to BRTO,

PARTO does not require prolonged overnight balloon

inflation and can be performed in a wider range of patients,

including those lacking a gastro-renal shunt, and

is associated with a lower risk of exacerbation of portal

hypertension [12,13,14]. PARTO also mitigates the devastating

risk of systemic embolization of sclerosant as seen

in conventional BRTO in up to 8.7% of cases [15].

The choice of access can be transfemoral or transjugular

approach. If the origin of the shunt is close to the

origin of the LRV, it is preferable to use transfemoral

approach as it has a steeper angle with the LRV (more

perpendicular). If the origin of the shunt is far from LRV

origin, a transjugular approach is easier as it has shallower

angle with the LRV (more parallel) [16] (Fig 7).

GRS commonly does not drain directly into the LRV,

but joins the left adrenal vein to form a common stump

before draining to the LRV. It is important to check for

a web-like narrowing usually seen at the junction of

GRS proper with the common stump. A reverse-shaped

40



VOLUME 10 | ISSUE 4

H R J

gastric varices, with a technical success rate of 100%

and clinical success ranging from 97.3% to 100% [20].

Complications are generally minor and manageable,

with reported rates of adverse events being relatively

low compared with other endoscopic or interventional

techniques (TIPS). Clinical outcomes have been favorable,

with studies reporting high rates of complete

variceal obliteration, reduced rebleeding rates, and improved

overall survival in patients undergoing PARTO

[21, 22, 23].

Fig. 7: Illustrative diagram for access route preference:

If angulation of IVC and LRV is between 60 – 1200 , either

transfemoral and transjugular route can be used. Use

transfemoral approach if it has a steeper angle with the

LRV (more perpendicular; > 1200). If the origin of the shunt

is far from LRV origin, a transjugular approach is easier as

it has shallower angle with the LRV (more parallel; < 600).

catheter is especially helpful in selection if the common

stump or the GRS proper points medially in the 9 to 11

o’clock position. Occasionally, there is little discernible

difference between the common stump and the GRS

proper.

Aggravation of ascites and esophageal varices are

the main drawbacks of PARTO/BRTO and is attributed

to increased portal flow [6,17]. A study by Saad et al.

reported the protective role of combining TIPS with

BRTO in the development of hydrothorax, ascites, and

upper gastrointestinal bleeding; however, the concomitant

protective effect on the progression of esophageal

varices was not evaluated, although theoretically very

valid [18]. Modified techniques, such as selective and

super-selective BRTO/PARTO, have been employed and

have shown a decreased risk of exacerbating ascites and

esophageal varices [19]. Shunt occlusion with preexisting

partial/complete portal vein thrombosis was a risk

factor for aggravation of esophageal varices, whereas

the amount of sclerosant employed was found to be a

major risk factor for ascites aggravation [19].

PARTO has shown promising results in terms of

both short-term and long-term efficacy in treating

Conclusion

Gastrovariceal bleed poses a significant clinical challenge

owing to its severity and high associated mortality.

Plug-assisted retrograde transvenous obliteration

(PARTO) has emerged as a safe and effective alternative

in failed endoscopic management due to its high technical

success rate and favourable outcomes in variceal

obliteration. A thorough pre-operative anatomical

evaluation helps in planning and selecting appropriate

hardware there by enhancing procedural success and

patient prognosis. However, a meticulous patient selection

with close monitoring and future comparative

studies are needed to stand out in clinical practice. R

Acknowledgements: We acknowledge the constructive

feedback provided by the anonymous reviewers,

which greatly improved the quality of this publication.

Additionally, we would like to thank our families for

their unwavering support during the entire process.

This project did not receive any specific funding.

Conflict of interest: The authors declare that they

have no conflict of interest.

Ethical Approval: The study was performed in accordance

with the principles of the Declaration of Helsinki

and the Institutional Review Board approved this

retrospective study.

Informed Consent: The Institutional Review Board

approved this retrospective study and waived the requirement

of informed consent.

Consent for Publication: Consent for publication

was obtained for every individual person’s data included

in the study.

41

H R J



VOLUME 10 | ISSUE 4

References

1. Pereira P, Peixoto A. Left-Sided Portal Hypertension:

A Clinical Challenge. GE Port J Gastroenterol.

2015;22(6):231-233. Published 2015 Nov 23.

doi:10.1016/j.jpge.2015.10.001

2. Goral V, Yılmaz N. Current Approaches to the Treatment

of Gastric Varices: Glue, Coil Application, TIPS,

and BRTO. Medicina (Kaunas). 2019;55(7):335. Published

2019 Jul 3. doi:10.3390/medicina55070335

3. Garcia-Tsao G, Abraldes JG, Berzigotti A, et al. Portal

hypertensive bleeding in cirrhosis: Risk stratification,

diagnosis, and management: 2016 practice

guidance by the American Association for the

study of liver diseases [published correction appears

in Hepatology. 2017 Jul;66(1):304. doi: 10.1002/

hep.29169.]. Hepatology. 2017;65(1):310-335.

doi:10.1002/hep.28906

4. Lee EW, Shahrouki P, Alanis L, et al. Management

Options for Gastric Variceal Hemorrhage.

JAMA Surg. 2019;154(6):540-548. doi:10.1001/

jamasurg.2019.0407

5. Mukund A, Anandpara KM, Ramalingam R, et

al. Plug-Assisted Retrograde Transvenous Obliteration

(PARTO): Anatomical Factors Determining

Procedure Outcome. Cardiovasc Intervent

Radiol. 2020;43(10):1548-1556. doi:10.1007/

s00270-020-02580-9

6. Saad WE, Sabri SS. Balloon-occluded Retrograde

Transvenous Obliteration (BRTO): Technical

Results and Outcomes. Semin Intervent Radiol.

2011;28(3):333-338. doi:10.1055/s-0031-1284460

7. Ibrahim A, Kalil E, Habeeb M. Predictors of early

re-bleeding and mortality in patients with

first attack of gastric variceal hemorrhage. Med

J Viral Hepatitis. 2021;5(3):14–21. doi:10.21608/

mjvh.2021.193435.

8. Sarin SK, Kumar A. Gastric varices: profile, classification,

and management. Am J Gastroenterol.

1989;84(10):1244-1249.

9. Kochar R, Dupont AW. Primary and secondary

prophylaxis of gastric variceal bleeding. F1000 Med

Rep. 2010;2:26. Published 2010 Apr 12. doi:10.3410/

M2-26

10. Bazarbashi AN, Ryou M. Gastric variceal bleeding.

Curr Opin Gastroenterol. 2019;35(6):524-534.

doi:10.1097/MOG.0000000000000581

11. Saad WE. Balloon-occluded retrograde transvenous

obliteration of gastric varices: concept, basic techniques,

and outcomes. Semin Intervent Radiol.

2012;29(2):118-128. doi:10.1055/s-0032-1312573

12. Tatsumi A, Maekawa S, Osawa L, et al. Spontaneous

portosystemic shunt diameter predicts liver function

after balloon-occluded retrograde transvenous

obliteration. JGH Open. 2022;6(2):139-147. Published

2022 Jan 28. doi:10.1002/jgh3.12712

13. Maruno M, Kiyosue H, Tanoue S, et al. Balloon-Occluded

Retrograde Transvenous Obliteration for Isolated

Gastric Varices Draining through Pericardiophrenic

Vein without Gastrorenal or Gastrocaval

Shunts. J Vasc Interv Radiol. 2016;27(2):203-207.

doi:10.1016/j.jvir.2015.11.028

14. Kim E, Gwon D. Plug-assisted retrograde transvenous

obliteration. Digestive Disease Interventions.

2017;1(4):293–301. doi:10.1055/s-0038-1632364.

15. Park SJ, Chung JW, Kim HC, et al. The prevalence,

risk factors, and clinical outcome of balloon rupture

in balloon-occluded retrograde transvenous

obliteration of gastric varices. J Vasc Interv Radiol.

2010;21(4):503-507. doi:10.1016/j.jvir.2009.11.023

16. Saad WE, Kitanosono T, Koizumi J, et al. The conventional

balloon-occluded retrograde transvenous

obliteration procedure: indications, contraindications,

and technical applications. Tech

Vasc Interv Radiol. 2013;16(2):101-151. doi:10.1053/j.

tvir.2013.02.003

17. Tanihata H, Minamiguchi H, Sato M, et al. Changes in

portal systemic pressure gradient after balloon-occluded

retrograde transvenous obliteration of gastric

varices and aggravation of esophageal varices.

Cardiovasc Intervent Radiol. 2009;32(6):1209-1216.

doi:10.1007/s00270-009-9679-3

18. Saad WE, Wagner CC, Lippert A, et al. Protective value

of TIPS against the development of hydrothorax/

ascites and upper gastrointestinal bleeding after

balloon-occluded retrograde transvenous obliteration

(BRTO). Am J Gastroenterol. 2013;108(10):1612-

1619. doi:10.1038/ajg.2013.232

19. Ahmed R, Kiyosue H, Mori H, et al. Conventional versus

selective balloon-occluded retrograde transvenous

obliteration of gastric varices. Egypt J Radiol Nucl

Med. 2020;51:101. doi:10.1186/s43055-020-00228-9.

42



VOLUME 10 | ISSUE 4

H R J

20. Gwon DI, Ko GY, Kwon YB, et al. Plug-Assisted Retrograde

Transvenous Obliteration for the Treatment

of Gastric Varices: The Role of Intra-Procedural

Cone-Beam Computed Tomography. Korean J Radiol.

2018;19(2):223-229. doi:10.3348/kjr.2018.19.2.223

21. Kim GH, Gwon DI, Ko GY, et al. Short-Term Results of

Plug-Assisted Retrograde Transvenous Obliteration for

Portal Steal from Complicated Portosystemic Shunts in

Living-Donor Liver Transplantation. J Vasc Interv Radiol.

2023;34(4):645-652. doi:10.1016/j.jvir.2022.12.023

22. Shim J, Lee JM, Cho Y, et al. Efficacy and Technical

Feasibility of Plug-Assisted Retrograde Transvenous

Obliteration of Gastric Varices via Pathways

Other than the Gastrorenal Shunt. Cardiovasc

Intervent Radiol. 2023;46(5):664-669. doi:10.1007/

s00270-023-03416-y

23. Kochar R, Dupont AW. Primary and secondary

prophylaxis of gastric variceal bleeding. F1000 Med

Rep. 2010;2:26. Published 2010 Apr 12. doi:10.3410/

M2-26

Key words

Gastric varices, Left-sided portal hypertension, Vascular plug, Embolization,

Gastro-renal shunt

Ready - Made

Citation

Vishal Nandkishor Bakare, Aniketh Davangere Hiremath, Ritesh Kumar Sahu,

Rohan Rahul Thakur. Plug-assisted Retrograde Transvenous Obliteration

(Parto) for Gastric Variceal Bleeding in Left-sided Portal Hypertension : An

Institutional Case Series, Hell J Radiol 2025; 10(4): 34-43.

43

H R J

Future Of Diagnosis: Impact and Rise of Artificial Intelligence in Radiology, p. 44-54

VOLUME 10 | ISSUE 4

Original Article

Future Diagnosis

Future of Diagnosis: Impact and Rise

of Artificial Intelligence in Radiology


Sanjhana Shree Tallabathulla, Yusuf Muhammad

Department of Radiology, GD Goenka University, Gurugram


Abstract

Introduction and purpose: This study explores the

impact, awareness, and prognosis of Artificial Intelligence

(AI) within the domain of Radiology. With objectives

focusing on assessing AI’s advantages and disadvantages,

understanding its awareness within the radiology

department, and estimating its future prospects.

Methodology: This study targets a group of 50 individuals

including students and faculties from GD

Goenka University along with 25 staff members of the

radiology department from five hospitals. Using both

online and offline questionnaires, data is collected via

Google Forms and printed copies of questionnaires.

The questions focus on the impact of AI and awareness

levels within the individuals relating to radiology background,

AI’s potential in diagnosis improvement, and

its capability to replace radiological staff.

Results: Results indicate a significant belief in

AI's impact on radiology from both the participants’

groups, with the majority expressing disbelief

regarding its potential to replace radiographers and

radiologists. The research findings highlight a strong

agreement on AI’s transformative potential in radiology,

with 80% of online and 78% of offline respondents

acknowledging its considerable impact. Most of the radiology

staff from the hospitals shared that they did not

receive any information about the use of AI in radiology

prior our interviews. 90% of them believed that AI can

have good impact on radiological practices. However,

disbelief remains regarding AI’s ability to replace human

professionals.

Conclusion: In conclusion, the study emphasize on

the pivotal role of AI in the future of radiology, including

its potential to enhance diagnostic accuracy, streamline

workflows, and improve patient care. As AI continues

to integrate into radiological practice, it presents both

opportunities and challenges, indicating a new era of

informed and efficient diagnoses facilitated by machine

learning algorithms and deep learning techniques.

Corresponding

Author,

Guarantor

Aarzoo Tehlan, Assistant Professor, Department of Radiology, GD Goenka

University, Gurugram

E mail: aarzootehlan30@gmail.com

44


VOLUME 10 | ISSUE 4

H R J

Introduction

The field of radiography has undergone transformations

driven by technological advancements, transitioning

from manual film processing to automatic processing

and eventually adopting digital image processing.

This evolution has revolutionized clinical radiography,

particularly with the introduction of cross-sectional

imaging modalities like CT and MRI. In the context of

healthcare infrastructure inadequacies in low-resource

settings, a crucial discussion about the future impact

of these innovations on clinical radiography practice is

warranted [2, 3, 6, 7].

AI, a powerful technology utilizing computer programs

to analyze complex data, has proven to be

promising in diagnostic imaging, demonstrating high

accuracy in detecting small abnormalities in medical

images [1, 5, 10]. However, concerns arise regarding

the current focus of AI studies on lesion detection without

considering the nature or aggressiveness of abnormalities,

potentially leading to biased evaluations.

Improvements are required by consistently using clinically

meaningful endpoints, such as patient survival,

symptoms, and the need for treatment, to provide a

more comprehensive evaluation of AI's effectiveness in

medical imaging [15].

While improved sensitivity is advantageous, it comes

with the challenge of detecting subtle changes of indeterminate

significance. For instance, in screening

mammograms, artificial neural networks exhibit higher

sensitivity for pathological findings, including subtle

lesions, though not surpassing radiologists' overall accuracy

[1, 5]. Incorporating outcome variables like new

diagnoses of advanced disease, disease requiring treatment,

or conditions likely to affect long-term survival

in AI imaging studies to enhance relevance is important

[15]. In recent years, the integration of AI technology

into healthcare has sparked significant interest and debate

among researchers, practitioners, and policymakers.

This paper reviews the impact of artificial intelligence

on the current healthcare scenario in radiology,

highlighting key findings, trends, and future research

areas [10, 11, 12].

While AI demonstrates effectiveness in specific tasks,

the global replacement of radiology staff is far from

possible. AI can be utilized as a supportive tool, emphasizing

the importance of communication and collaboration

with professionals like engineers and computer

scientists [18]. Despite the growing need for AI education

for students, residents, and medical specialists,

only a limited number of studies have addressed this

need in recent years. The consensus advocates for continuous

training, starting from the university phase,

consolidating during residency or training, and persisting

throughout one's professional career [14, 16].

While numerous training programs are available,

they often lack integration into the overall learning

path. The emerging nature of AI training creates a significant

gap between program offerings and the actual

needs of radiology staff. It is evident that comprehensive

training encompassing the use, benefits, challenges,

and implementation issues of AI in clinical departments

is essential. This ensures increased confidence

among clinicians interested in incorporating AI into

their careers. Practical exercises with real AI applications

should engage students, teaching them effective

and critical usage [4, 8, 9, 13].

Literature Review

S. No. Title of study Authors Methodology Conclusion

1 AI IN MACHINE

LEARNING IN

RADIOLOGY,

CRITERIA FOR

SUCCESS

Thrall, J. H,

et al.(10)

Involves a comprehensive literature review

approach. The authors likely conducted a

systematic search of relevant databases and

sources to identify pertinent literature on AI

and ML in radiology. They then extracted key

insights and synthesized findings from selected

studies to explore opportunities, challenges,

pitfalls, and success criteria in the field. The

methodology likely entailed qualitative analysis

techniques to identify common themes and

patterns across the literature.

Worldwide interest in AI

applications, including

imaging, is high and

growing rapidly. - The

large amount of image

and report data now in

digital form ("big data")

provides a substrate

for development of AI

Applications.

45

H R J


VOLUME 10 | ISSUE 4

Literature Review


2 RESEARCH:

ARTIFICIAL

INTELLIGENCE

IN MEDICAL

IMAGING

PRACTICE:

LOOKING TO THE

FUTURE

Lewis, K, et al.(11)

The commentary presents

a comprehensive review of

the current landscape of AI

in medical imaging, drawing

on existing research and

industry developments. The

authors analyze the potential

applications of AI, including

machine learning and natural

language processing, in various

aspects of medical imaging

practice. Additionally, they

discuss the implications of AI

for healthcare professionals,

emphasizing the need for

education and training to adapt

to this technological shift.

The commentary provides

valuable insights into the

transformative potential of AI

in medical imaging practice.

It underscores the need for

healthcare professionals to

embrace AI as a collaborative

tool rather than a replacement

for human expertise. The

authors advocate for ongoing

education and professional

development to empower

medical imaging practitioners

to leverage AI effectively while

upholding ethical standards

and prioritizing patient care.

3 AI IN DIAGNOSTIC

APPLICATIONS

OF ARTIFICIAL

RADIOLOGY: A

TECHNOGRAPHY

STUDY

Rezazade

Mehrizi, et al.(12)

Review based

The article provides a

comprehensive analysis of the

current state of AI applications

in diagnostic radiology and

suggests possible ways to further

develop them and integrate

them into clinical practice.

4 THERAPY:

INNOVATIONS,

AI IN IMAGING

AND ETHICS, AND

IMPACT:REVIEW

ARTICLE

Drabiak, et al.(13) Review based AI in healthcare has significant

potential to improve patient

care, clinical outcomes and

medical research. However,

legalities, ethics and

regulations must be addressed.

Interdisciplinary collaboration

and further research are

needed to ensure the long-term

benefits of the technology.

5 IMPACT OF

THE RISE OF

ARTIFICIAL

INTELLIGENCE

IN RADIOLOGY:

WHAT DO

RADIOLOGISTS

THINK?

Waymel, Q., et

al.(14)

A general data protection

regulation-compliant electronic

survey was sent by e-mail to

the 617 radiologists registered

in the French departments

of Nord and Pas De-Calais (93

radiology residents and 524

senior radiologists)

Most radiologists lack adequate

prior knowledge about AI

but express a willingness

to participate in additional

courses to enhance their

understanding and technical

expertise in the field. Despite

limited information, the

majority of radiologists are

optimistic about the positive

impact of AI on their future

practice. Their primary

expectations revolve around

improved patient care quality

and time efficiency in their

interactions with patients.

46


VOLUME 10 | ISSUE 4

H R J

Literature Review


6 ARTIFICIAL

INTELLIGENCE

IN MEDICAL

IMAGING:

SWITCHING

FROM

RADIOGRAPHIC

PATHOLOGICAL

DATA TO

CLINICALLY

MEANINGFUL

ENDPOINTS

Oren, O., et al.(15) Review based Unless AI algorithms are trained

to distinguish between benign

abnormalities and clinically

meaningful lesions, better imaging

sensitivity might come at the cost

of increased false positives, as well

as perplexing scenarios whereby

AI findings are not associated with

outcomes. To facilitate the study of

AI in Medical image interpretation,

it is paramount to assess the effects

on clinically meaningful endpoints

to improve applicability and allow

effective deployment into clinical.

7 RESHAPING THE

PRACTICE OF

RADIOLOGICAL

SCIENCES IN THE

21ST CENTURY

El Naqa, I., et al.

(16)

Clearly define the

objectives of the study,

such as assessing the

impact of AI on diagnostic

accuracy, workflow

efficiency, and patient

outcomes in radiology

practice.

The past few years have witnessed a

tremendous rise in AI applications

to a wide range of areas in

radiological sciences (diagnostic

and therapy) including automation

of segmentation, improving image

quality, and developing decisionsupport

systems for personalization

of detection and treatment.

8 IMPACT OF

ARTIFICIAL

INTELLIGENCE

ON CLINICAL

RADIOGRAPHY

PRACTICE:

FUTURISTIC

PROSPECTS IN A

LOW RESOURCE

SETTING

Wuni, A. R., et

al.(17)

Researchers can

systematically investigate

the impact of AI on

clinical radiography

practice in low-resource

settings, providing

valuable insights for

healthcare professionals,

policymakers, and

technology developers.

Artificial intelligence has come to

stay, radiographers equipped with

clinical, analytical and research

skills should be harnessed to ensure

the safe and ethical use of AI. As

professionals we must accept AI,

embrace it, learn it and own it.

9 IMPACT OF

ARTIFICIAL

INTELLIGENCE

ON RADIOLOGY

AUTHOR;

EUROPEAN

SOCIETY OF

RADIOLOGY (ESR)

Becker Christoph

D, et al.(18)

Review based

The integration of artificial

intelligence into radiology has

the potential to revolutionize

diagnostic imaging practices,

offering opportunities for increased

efficiency, accuracy, and patient

care. While challenges such as data

privacy and algorithm transparency

remain, collaboration between

radiologists and AI developers can

help overcome these hurdles and

ensure the responsible and effective

deployment of AI technologies in

radiology departments worldwide.

47

H R J


VOLUME 10 | ISSUE 4

Methodology

Target population:

The intended group comprises a minimum of 50 individuals,

encompassing students and faculty with expertise

in radiology from GD Goenka University. The goal

is to encompass four hospitals and gather data from

various members of the radiological staff, such as radiologists,

radiological residents, radiographers, and radiological

nurses, across these healthcare institutions.

Method of data collection:

We have opted for two data collection methods, utilizing

both online and offline approaches. The online

method involves a questionnaire distributed via Google

Forms, incorporating questions derived from the

existing survey for faculty and students at GD Goenka

University. In contrast, the offline mode entails printed

copies of the same questionnaire, to be completed by

the radiological staff in various hospitals.

Results

OFFLINE DATA:

A survey was conducted among 5 hospitals in Gurgaon;

Medanta, Artemis, Polaris, Park and Veriezon

hospital and Gd goenka university sohna among radiology

students from 6th semester.

25 questionnaires were distributed among the medical

staff including Nurses, Technicians and Doctors

across all 5 hospitals, 5 questionnaire was given to each

hospital.

The following are responses received after distribution

from respondents:

Medanta hospital, 4 out of 5 respondents heard about

AI through workshop and 1 heard through social media 3

believes AI has a great impact in medical imaging modalities

and 2 reverse the case, 2 respondents have 9 years of

working experience and 3 have 5 years, 2 believes AI can

replaced radiographers and 3 so not believe.

Artemis hospital, 3 heard about AI through workshop

and 2 heard through professional conference, 2 believes

AI have great impact in radiology and 3 3 respondents

do not believed, 5 believes AI cannot replace radiographers,4

respondents have 8years and 1 has 4years of

working experience.

Park hospital, 4 respondents heard about AI through

social media and 1 heard through conference, 5 believed

AI have a great impact in radiology, 5 believed AI

cannot replace radiographers, 2 have 4years and 3 have

4 years of working experience.

Veriezon hospital,5 heard about AI through workshop,4

believed AI have great impact in radiology and

1 not, 3 respondents have 4 years working experience

and 2 have 2 years’ experience, 5 believed AI cannot replace

radiographers.

Polaris hospital, 2 heard about AI through professional

conference, and 1 through workshop and 3 through

scientific journals, 5 believed AI have a great impact in

radiology, 5 believed AI can replace radiographers, 3

have 3 years working experience 2 have 1 year experience.

Table 1: Tally responses received from all the 5 hospitals after distribution of questionnaire from

respondents

Age

Under 18 18-29 30 and above

0 20 5

Have you receive any information about artificial intelligence in radiology prior to this study?

Yes No Unsure

25 0 0

Please, specify how you became perceive of Artificial intelligence?

professional

conference

Scientific journal workshop/seminars Online resources

6 2 17 0

48


VOLUME 10 | ISSUE 4

H R J

Table 1: Tally responses received from all the 5 hospitals after distribution of questionnaire from

respondents

Do you believe that artificial intelligence will have a positive impact in future of radiology?

Yes No Unsure

22 4 0

Can Artificial intelligence replace radiological staff or healthcare professionals?

Yes No Unsure

7 19 0

How do think training and education in radiology should adapt to incorporate AI technology

Formal training programs

on AI integration

Continuous professional

development on AI

Application

Revision to radiology

curriculum

Others

2 8 2 14

DATA PRESENTATION

Fig 1:

Sources of prior

information

of AI

Fig 2:

Views on

replacement

of healthcare

professionals

by AI

49

H R J


VOLUME 10 | ISSUE 4

Fig 3:

Methods to

adapt to AI

technology

ONLINE DATA

A survey from online survey was conducted and all

the 35 responses are all from 18-29 age and for their

gender categories, 20(57.1%) responses are male while

15(42.9%) are female. From the survey, 24(70.6%) have

not been exposed from to AI while 10(29.4%) have been

exposed to AI in hospital. 29(82.9%) responses feel that

AI will have an impact in patient care while 6(17.2%) responses

did not feel AI will have impact in patient care.

The responses were asked whether they receive any

information about AI in radiology prior to these studies

and 19(54.3%) responses are aware, 10(28.6%) are not

aware and 6(17.1%) responses are unsure. 20(57.1%) responses

became perceive of AI in online resources while

6(17.1%) through workshop/seminar, also 3(8.6%) perceive

through scientific journal and 5(14.3%) have become

perceive of AI in radiology through professional

conference. 28(80%) of the responses believe that AI

will have a positive impact in the future of radiology,

6(17.1%) responses are unsure and 1(2.9%) responses

says no at all.

Also, 15(42.9%) of the responses did not believe AI can

replace radiological staff in healthcare system, 12(34.3)

responses have believe AI can replace radiological staff

while 8(22.9%) are unsure of the idea of replacement.

Also 21(60%) responses think training and education in

radiology of AI can be adapt through formal training

programs of AI integration while 10(28.6%) responses

believe through continuous professional development

on AI Application and 3(8.6%) says through revision to

radiology curriculum and 1(2.9%) is unspecified.

Table 2: Tally responses received from Radiology students

Age

Under 18 18-29 30 and above

0 35 0

Gender

Male

female

20 15

Have you ever been exposed to AI in hospitals?

Yes No Unsure

24 10 0

50


VOLUME 10 | ISSUE 4

H R J

Table 2: Tally responses received from Radiology students

Do you feel AI in radiology will impact patient care?

Yes No Unsure

29 6 0

Have you receive any information about artificial intelligence in radiology prior to this study?

Yes No Unsure

19 10 6

Please, specify how you became perceive of Artificial intelligence?

professional

conference

Scientific journal

workshop/seminars

5 3 6 20

Do you believe that artificial intelligence will have a positive impact in future of radiology?

Yes No Unsure

28 1 6

What did you perceive as the potential benefit of artificial intelligence in radiology?

Online resources/

network

Lowering of imaging-related

medical errors

Lowering the interpretation

time of examination

Increase in the time spent

with patients

Others

18 12 4 1

Can Artificial intelligence replace radiological staff or healthcare professionals?

Yes No Unsure

12 15 8

How do think training and education in radiology should adapt to incorporate AI technology

formal training programs

on AI integration

continuous professional

development on AI

Application

revision to radiology

curriculum

Others

21(60%) 10(28.6%) 3(8.6%) 1(2.9%)

Fig 4:

Exposure to AI

in hospitals

51

H R J


VOLUME 10 | ISSUE 4

Fig 5:

Views on imapct

of AI on patient

care

Fig 6:

Sources of prior

information

of AI

Fig 7:

Views on impact

of AI in future of

Radiology

52


VOLUME 10 | ISSUE 4

H R J

Fig 8:

Views on

replacement

of healthcare

professionals

by AI

Conclusion

From the study conducted both offline (using survey

questionnaire) and Online (using Google forms), 80%

from online respondents and 78% from offline respondents

believed that AI have a great impact in radiology,

42% online respondents and 93% offline respondents do

not believe AI can replace radiographers, 57% received

AI through online source from online survey and 87%

through workshop from offline survey. The future of

diagnosis in radiology is undeniably intertwined with

the rapid rise of artificial intelligence (AI). AI technologies

have already begun to revolutionize the field,

offering unprecedented opportunities to enhance diagnostic

accuracy, streamline workflows, and improve

patient outcomes.

The integration of machine learning algorithms and

deep learning techniques into radiological practice has

led to significant advancements in image interpretation,

enabling radiologists and radiographers to make

more informed and efficient diagnoses. R

References

1. X. Liu et al. A comparison of deep learning performance

against health-care professionals in detecting

diseases from medical imaging: a systematic review

and meta-analysis Lancet Digital Health (2019)

2. A.D. Piersson et al. Assessment of availability, accessibility

and affordability of magnetic resonance imaging

services in Ghana Radiography (2017)

3. B. Botwe et al. An investigation into the infrastructure

and management of computerized tomography

units in Ghana J Med Imag Radiat Sc (2020)

4. B.O. Botwe et al. The integration of artificial intelligence

in medical imaging practice: perspectives of

African radiographers

5. X. Liu et al. A comparison of deep learning performance

against health-care professionals in detecting

diseases from medical imaging: a systematic review

and meta-analysis Lancet Digital Health (2019)

6. A.D. Piersson et al. Assessment of availability, accessibility

and affordability of magnetic resonance imaging

services in Ghana Radiography (2017)

7. B. Botwe et al. An investigation into the infrastructure

and management of computerized tomography

units in Ghana J Med Imag Radiat Sci (2020)

8. B.O. Botwe et al. The integration of artificial intelligence

in medical imaging practice: perspectives of

African radiographers Radiography (2021)

9. C.M. Hayre et al. Is image interpretation a sustainable

form of advanced practice in medical imaging? J

Med Imag Radiat Sci (2019)

10. Thrall, J. H., Li, X., Li, Q., Cruz, C., Do, S., Dreyer, K., &

Brink, J. (2018). Artificial intelligence and machine

learning in radiology: opportunities, challenges, pit-

53

H R J


VOLUME 10 | ISSUE 4

falls, and criteria for success. Journal of the American

College of Radiology, 15(3), 504-508.

11. Lewis, S. J., Gandomkar, Z., & Brennan, P. C. (2019).

Artificial Intelligence in medical imaging practice:

looking to the future. Journal of Medical radiation

sciences, 66(4), 292-295.

12. Rezazade Mehrizi, M. H., van Ooijen, P., & Homan,

M. (2021). Applications of artificial intelligence (AI)

in diagnostic radiology: a technography study. European

radiology, 31, 1805-1811.

13. Drabiak, K., Kyzer, S., Nemov, V., & El Naqa, I. (2023).

AI and machine learning ethics, law, diversity, and

global impact. The British journal of radiology,

96(1150), 20220934.

14. Waymel, Q., Badr, S., Demondion, X., Cotten, A., &

Jacques, T. (2019). Impact of the rise of artificial intelligence

in radiology: what do radiologists think?.

Diagnostic and interventional imaging, 100(6),

327-336.

15. Oren, O., Gersh, B. J., & Bhatt, D. L. (2020). Artificial

intelligence in medical imaging: switching from radiographic

pathological data to clinically meaningful

endpoints. The Lancet Digital Health, 2(9),

e486-e488.

16. El Naqa, I., Haider, M. A., Giger, M. L., & Ten Haken,

R. K. (2020). Artificial intelligence: reshaping the

practice of radiological sciences in the 21st century.

The British journal of radiology, 93(1106), 20190855.

17. Wuni, A. R., Botwe, B. O., & Akudjedu, T. N. (2021).

Impact of artificial intelligence on clinical radiography

practice: futuristic prospects in a low resource

setting. Radiography, 27, S69-S73.

18. European Society of Radiology (ESR) communications@

myesr. org Becker Christoph D. Kotter Elmar

Fournier Laure Martí-Bonmatí Luis. (2022). Current

practical experience with artificial intelligence

in clinical radiology: a survey of the European Society

of Radiology. Insights into imaging, 13(1), 107.

Key words

Radiology, Artificial Intelligence, Impact, Rise

Ready - Made

Citation


Sanjhana Shree Tallabathulla, Yusuf Muhammad. Future Of Diagnosis: Impact

and Rise of Artificial Intelligence in Radiology, Hell J Radiol 2025; 10(4): 44-54.

54

H R J

Urinary bladder herniation: CT evaluation, p. 56-63

VOLUME 10 | ISSUE 4

Original Article

Abdominal Imaging

Urinary bladder herniation:

CT evaluation

Dimitrios Kourdakis MD 1 , Maria-Michailia MD 1 , Eleni Makridou MD 1 ,

Elena Hadjichristou MD 1 , Ouroumidou Kristina MD 2 , Manavi Aikaterini MD 1

1

Agios Dimitrios General Hospital, Department of Radiology

2

G. Gennimatas General Hospital Thessaloniki, Department of Radiology


Abstract

Purpose: To evaluate the role of computed tomography

(CT) in the diagnosis and characterization of

urinary bladder herniation, an uncommon and often

underdiagnosed condition, with emphasis on its anatomical

patterns and imaging features essential for accurate

diagnosis and preoperative planning.

Material and Methods: A retrospective review was

conducted over a six-year period, from January 2019

to May 2025. Inclusion criteria were (1) imaging-confirmed

urinary bladder herniation and (2) availability

of complete clinical history, including presenting

symptoms and physical examination findings. Imaging

protocols included pre-contrast, post-contrast, and

delayed-phase scans with a focus on the lower pelvis.

Multiplanar reconstructions were routinely performed.

Results: Urinary bladder herniation was identified

in six patients, in which four of them the herniated

bladder extended into the scrotum, consistent with inguinoscrotal

herniation. Four of the patients were diagnosed

incidentally on CT scans for other purposes and

two presented with symptoms. All hernias were unilateral,

with five occurring on the right side and one on

the left. The cohort included four male and two female

patients.

Key words

Inguinal hernia, Urinary bladder, CT, Urinary bladder hernia

Corresponding

Author,

Guarantor

Dimitrios Kourdakis, MD

Agios Dimitrios General Hospital, Department of Radiology

Thessaloniki, Greece, Elenis Zografou 2, 54634

E-mail: dimitriskourd1@gmail.com

Tel: +306980385351

56


VOLUME 10 | ISSUE 4

H R J

Conclusions: Urinary bladder herniation is a rare

condition but should always be considered in the differential

diagnosis of inguinal hernias, particularly in

older male patients. CT imaging plays a pivotal role in

the detection and evaluation of urinary bladder herniation,

providing crucial information for diagnosis and

surgical planning.

Introduction

Urinary bladder hernia is an uncommon clinical entity.

It was first recorded in the Middle Ages by Plater

in 1550. Most bladder hernias involve the inguinal and

femoral canals (75% and 23% respectively), with a predisposition/

tendency for the right side been reported.

Rarely, herniations through ischiorectal, obturator,

rectus abdominus (Gironcoli hernia) and abdominal

wall openings have also been described (2%).[1],[2] The

incidence rate of bladder involvement is ranging between

1-4% of all inguinal hernias and around 10% in

men older than 50 years.[3]

The majority of patients present with no symptoms

and bladder herniation is found incidentally on diagnostic

imaging examinations or intraoperatively at the

operation theatre. In symptomatic cases, patients usually

present with atypical symptoms such as dysuria,

urinary urgency, inguinal swelling and left or lower

quadrant abdominal pain. The standard treatment of

bladder hernias is the surgical repair with a mesh to

prevent recurrence.


We searched for patients with herniation of the urinary

bladder in a period of 6 years, from January 2019

to May 2025. Inclusion criteria were (1) imaging confirmation

of urinary bladder herniation and (2) availability

of complete clinical history, including symptoms

and physical examination findings. Patients who did

not meet these criteria were excluded from the study.

All patients underwent computed tomography (CT) imaging

using Siemens Somatom 32-slice CT scanner.

The CT protocol included scans before and after intravenous

contrast administration, as well as delayed

phase images focused on the lower abdomen and pelvis

to better assess the extent of bladder involvement.

Multiplanar reconstructions were systematically performed

in sagittal and coronal planes to enhance visualization

of the herniated bladder component, assess its

anatomical relationships, and confirm continuity with

the orthotopic portion of the urinary bladder.

Results

A total of six adult patients (four males and two females),

aged between 40 and 76 years (mean age: 61.3

years), were included in the study. All patients had imaging-confirmed

urinary bladder herniation identified

on CT scans. One patient was excluded due to lack of

clinical information. None of the patients had a prior

diagnosis of bladder herniation before the CT examination.

The majority of patients (n=5, 83,3%) presented with

right-sided inguinal herniation of the bladder. (Figure

1A, 1B, 2) Only one case (n=1, 16.7%) involved the left

inguinal canal. (Figure 3A, 3B) In four of the patients,

the herniated bladder extended into the scrotum, consistent

with inguinoscrotal herniation.

Clinical presentation varied among patients. Two individuals

reported lower urinary tract symptoms such

as dysuria, urinary frequency, or a sensation of incomplete

emptying. The remaining four cases were asymptomatic

and diagnosed incidentally during CT scans

performed for unrelated reasons. No signs of bladder

wall ischemia, diverticula, stones or urinary bladder

cancer were identified in any of the cases.

Delayed-phase imaging was essential in clearly delineating

the extent of bladder involvement and confirming

the communication between the herniated segment

and the orthotopic portion of the urinary bladder.

Discussion

Epidemiology

As mentioned above, inguinal bladder hernias account

for 1-4% of all inguinal hernias and mostly occur

in male patients over the age of 50, usually on the right

side, in contrast to femoral bladder hernias which are

more commonly observed in women. The incidence in

the pediatric population is extremely low, with only

two cases identified in a cohort of 6,361 inguinal hernia

patients (0.03%) reported by a single author.[4] Becker

et al. classified inguinal hernias in regard to their relation

to the peritoneum in three categories:

1) Paraperitoneal hernias: the bladder remains extraperitoneal

and is medial to the peritoneal herniation

(most common category). This can be seen in either direct

or indirect inguinal hernias,

57

H R J


VOLUME 10 | ISSUE 4

2) Intraperitoneal hernias: the bladder is completely

covered with peritoneum in the hernial sac and

3) Extraperitoneal hernias, where the bladder herniates

alone without the peritoneum. The degree of herniation

can vary from a small protrusion to whole bladder

herniation, always containing all segments of the bladder

wall.[1] Anatomically, inguinal bladder hernias can

be classified as direct or indirect, depending on whether

they protrude medially through Hesselbach’s triangle or

laterally through the internal inguinal ring.[5]

Pathophysiology and Risk Factors

Bladder herniation is thought to occur due to persistent

elevation of the intravesical pressure and weakening

of the bladder’s musculature leading to loss of bladder

tone, in combination with an increased abdominal

wall compliance. The aforementioned create a weak

spot in the abdominal wall, allowing the “stretched”

urinary bladder to protrude. Risk factors associated

with bladder herniation are increased age, obesity,

male gender (especially for inguinal bladder hernia),

bladder outflow obstruction (due to benign prostatic

hyperplasia, prostate cancer, bladder neck strictures,

direct inguinal hernia, pelvic masses) causing urinary

bladder distention and movement proximal to the hernia

orifices[6], pericystitis and perivesical bladder fat

protrusion[5], [positive family history, chronic obstructive

pulmonary disease, smoking, increased intraabdominal

pressure, weak pelvic musculature, collagen

vascular disease].[2]

Clinical Image and Physical Examination

Patients with urinary bladder hernia are typically

asymptomatic. In asymptomatic patients, diagnosis is

made as an incidental finding in imaging examination

or intraoperatively. According to research, less than

7% of bladder hernias are diagnosed preoperatively,

around 16% are diagnosed postoperatively because of

existing complications and the rest percentage perioperatively.[3]

In our opinion, due to recent technological

advances and vast increasing number of imaging examinations,

these percentages have changed in favor of

preoperative finding in imaging both in asymptomatic

and symptomatic patients. Our opinion is also supported

by Branchu et al. [7]. When patients are symptomatic,

they usually present with non-specific symptoms

such as urinary frequency, inguinal swelling, left or

right lower quadrant abdominal pain, urinary urgency,

hematuria, nocturia. In cases with large hernias, when

the herniated bladder gets trapped within the inguinal

canal, it could make its way to the scrotum, condition

referred as scrotal cystocele. (first description was

made in 1951 by Levine)[8].

This entity can present as intermittent scrotum

swelling, urinary tract infection or in more advanced

cases as two-stage micturition, a situation in which initially

the patient empties the normally located bladder

and then voids again after manual compression of the

hernia sac in the scrotal region. After that, a reduction

in the size of the hernia is observed. In literature this

is known as Mery’s sign.[7] The two-stage urination

symptom or the reduction in size of the hernia mass

after micturition appears to be pathognomonic of inguinal-scrotal

bladder hernia.

On physical examination especially in large hernias,

when maneuvers are attempted to reduce the hernia,

the patient may complain of urinary urgency. In

a review of 190 cases, Oruç et al. found that 23.5% of

bladder hernias were associated with various complications.[9]

Larger hernias are particularly prone to adverse

outcomes such as bladder incarceration or necrosis,

bladder hemorrhage, ischemia or infarction (due

to strangulation)[10], bladder rupture, obstructive or

neurogenic bladder dysfunction, urinary tract infections,

epididymitis, scrotal abscesses, vesicoureteric

reflux, cystolithiasis (due to poor drainage), hydronephrosis

and renal failure (especially in cases when

a ureter herniates into the sac along with the bladder

or independently[11]).[12],[9] Another interesting

complication is malignancy. Oruç et al revealed 11%

(13/116 cases) incidence of genitourinary malignancy

in patients with inguinal bladder hernia. (9/116 were

reported as bladder carcinoma, 3/116 as prostate carcinoma

and 1/116 as a neoplasm).[9]

Pediatric Population

Bladder herniation is a very rare entity in pediatric

population. In young infants less than 6 months of age,

is thought to represent a variation of normal development.[5]

In infants and young toddles, the bladder

is positioned more superficially within the abdomen

compared to adults. Due to anatomical differences—including

a relatively large inguinal ring and the more

anterior and caudal position of the bladder—the lateral

58


VOLUME 10 | ISSUE 4

H R J

Figure 1 A(Axial) and B (Sagittal): CT images which reveal right inguinoscrotal herniation of the urinary bladder.

59

H R J


VOLUME 10 | ISSUE 4

Figure 2: (Axial): CT images which reveal right inguinoscrotal herniation of the urinary bladder.

bladder wall lies closer to the internal inguinal ring, allowing

greater lateral mobility. This can result in transient

bladder protrusions, known as bladder ears. Allen

et al. also observed that these bladder ears occurred

when the bladder was only partially filled and disappeared

with complete filling of the bladder or with the

onset of micturition.[13]

As in adults, in children it is more common in males

and appears more often on the right side. However, unlike

adults, children usually don’t present with typical

symptoms or cannot articulate them due to their age

limitations. The incidence of bladder hernias is more

prevalent in premature infants, where increased intra-abdominal

pressure due to CPAP ventilation and repeated

squeezing of the immature intestine appear as

contributing factors. Diagnosis is usually made post-operatively

from the complications, such as urinary leakage,

fluid seeping from the incision, abdominal distention,

anuria, peritonitis or swelling in the inguinal and

scrotal region or intraoperatively.[14]

This differentiates from the adults, because of the

limited number of diagnostic exams in small age

groups, as well as the increased movement ability of the

bladder in small ages —frequently shifting depending

on its filling status— (protrusions disappearing with

full-filled or full empty bladder). To avoid inadvertent

injury, Aloi et al. suggests the emptying of the bladder

before any surgery on the inguinal canal in infants and

young children.[15]

Imaging

Incidental detection of urinary bladder herniation

most commonly occurs via ultrasound (US) or Computed

Tomography (CT), which are frequently employed

in the evaluation of groin-related symptoms or other

abdominal pathologies. It may also be identified on

Magnetic Resonance Imaging (MRI), especially during

staging procedures for prostate cancer or for the assessment

of lower abdominal masses. In symptomatic

cases, US and CT are also the first line imaging modality.

Male, obese patients over 50 years old with urinary

tract symptoms, with or without history of inguinal

hernia should raise high clinical suspicion for containing

part of the urinary bladder in the hernia sac. The

high clinical index could avoid surgical complications

including bladder injury, which occurs in 12% of hernia

repairs.[16] That is the reason why a correct preoperative

management is necessary to prevent any iatrogenic

injury to the bladder.

Ultrasound: is usually helpful in large blader hernias

which protrude in the scrotal region. US is requested

for initial characterization of a scrotal mass. Diagnostic

criteria can be a hypoechogenic “mass like

lesion” protruding from the bladder through the inguinal

canal[11], a fluid-filled lesion at the scrotum

that can often be followed cranially to join the intraabdominal

portion of the bladder. Changing in the

position, the volume and the thickening of the lesion

after urination is highly diagnostic.[5]

60


VOLUME 10 | ISSUE 4

H R J

Figure 3 A(Axial) and B (Sagittal): CT images which reveal left inguinoscrotal herniation of the urinary bladder.

61

H R J


VOLUME 10 | ISSUE 4

CT: CT scan can be more beneficial than US in cases

of obese patients, patients with inguinal swelling and

patients with multiple bladder diverticula. In small

bladder hernias, the sign pointing at bladder herniation

is direction/bending/expanding of the bladder

towards the side of the hernia. In larger bladder hernias,

it is possible to locate part of the bladder into

the inguinal canal or in the scrotal area with mild

thickening and irregular border of the hernial part.

In cases when contrast medium cannot be administered,

identification of bladder’s thick wall surrounding

unopacified urine can suggest the diagnosis.[5]

Coronal and sagittal reconstruction can be also helpful,

especially in complexed cases. Furthermore, CT

allows simultaneous comprehensive assessment of

potential complications, surrounding pelvic anatomy,

as well as in the identification of other possible

coexisting hernias.

MRI: Bladder herniation is usually an incidental finding

on MRI examinations performed for other purposes,

most commonly for prostate gland or pelvic

mass evaluation. Nevertheless, it can be used to clarify

the sonographic or CT findings. MRI’s high resolution

can provide useful information about the rest

of the structures in the inguinal or femoral canal and

about the relation of the hernia to the vascular landmarks,

such as the inferior epigastric vessels.

Retrograde Cystography: It is considered to be the

gold standard technique to image a bladder hernia.

Filling phase of cystography may not reveal the bladder

herniation, which could be visible only in voiding

or post-voiding phases. This could be explained

due to the high intravesical pressure during voiding

which allows the contrast medium to slide through

the herniated portion of the bladder.[5] The defining

feature is a “dog-ear” or “dumbbell” shape of

the bladder, found in the voiding phase.[2] Voiding

cystourethrography is recommended in cases where

the suspicion of inguinal bladder hernia is high, or in

cases where initial imaging with US or CT is inconclusive.[17]

Excretory Urography: Bladder herniation may be

suspected when asymmetry of the bladder wall is seen

in the pelvis.[1] Possible findings may be a rounded

protrusion of the bladder wall directed downward

in anteroposterior projections and protruding areas

laterally and inferiorly in oblique projections. Moreover,

patient’s position plays a very important role,

as bladder hernias can be reliably identified in 100%

of cases only when erect imaging is performed. Comparably,

only 30% of hernias can be detected on supine

films and more than 50% on prone position.[5]

Cystoscopy: is usually used to confirm diagnosis after

an inconclusive CT scan and to rule out additional

pathology of the bladder.[17]

Conclusion

Although urinary bladder herniation remains a rare

and usually incidental finding, increased awareness

among radiologists and clinicians is essential in order

to prevent misdiagnosis and avoid potential intraoperative

bladder injury. CT imaging represents a highly

valuable diagnostic tool for urinary bladder herniation

due to its wide availability, short examination time and

capability to simultaneously evaluate surrounding pelvic

and abdominal structures, making it an indispensable

tool in preoperative assessment. R

Acknowledgements, sponsorships and grants:


62


VOLUME 10 | ISSUE 4

H R J

References

1. Becker JA. A Hernia of the Urinary Bladder. Radiology.

1965 Feb;84(2):270–3.

2. Isaac D, Haris F, Panos D, Diab J, Clement Z. Inguinal

bladder hernia: differentials for a male groin mass. J

Surg Case Rep [Internet]. 2022 Nov 1 [cited 2025 July

7];2022(11). Available from: https://academic.oup.

com/jscr/article/doi/10.1093/jscr/rjac493/6794860

3. Kraft KH, Sweeney S, Fink AS, Ritenour CWM, Issa

MM. Inguinoscrotal bladder hernias: report of a series

and review of the literature. Can Urol Assoc J.

2008 Dec 1;2(6):619.

4. Ein SH, Njere I, Ein A. Six thousand three hundred

sixty-one pediatric inguinal hernias: a 35-year review.

J Pediatr Surg. 2006 May;41(5):980–6.

5. Bacigalupo LE, Bertolotto M, Barbiera F, Pavlica

P, Lagalla R, Mucelli RSP, et al. Imaging of Urinary

Bladder Hernias. Am J Roentgenol. 2005

Feb;184(2):546–51.

6. McLain S, Cecire J, Mekisic A. Incarcerated inguinal

hernia containing urinary bladder and bladder

calculi. J Surg Case Rep [Internet]. 2023 Jan 1 [cited

2025 July 7];2023(1). Available from: https://academic.oup.com/jscr/article/doi/10.1093/jscr/

rjad010/7000322

7. Academic Department of Urology, Chu Reims,

Reims, France, Branchu B, Renard Y, Academic Department

of Digestive Surgery, Chu Reims, Reims,

France, Larre S, Academic Department of Urology,

Chu Reims, Reims, France, et al. Diagnosis and treatment

of inguinal hernia of the bladder: a systematic

review of the past 10 years. Türk Ürol DergisiTurkish

J Urol. 2018 Sept 7;44(5):384–8.

8. Levine B. SCROTAL CYSTOCELE. J Am Med Assoc.

1951 Dec 8;147(15):1439.

9. Coskun F, Oru MT, Akbulut Z, Zozan. Urological findings

in inguinal hernias: A case report and review of

the literature. Hernia. 2004 Feb 1;8(1):76–9.

10. Bisharat M, O’Donnell ME, Thompson T, MacKenzie

N, Kirkpatrick D, Spence RAJ, et al. Complications of

inguinoscrotal bladder hernias: a case series. Hernia.

2009 Feb;13(1):81–4.

11. Taskovska M, Janež J. Inguinal hernia containing

urinary bladder—A case report. Int J Surg Case Rep.

2017;40:36–8.

12. Wang P, Huang Y, Ye J, Gao G, Zhang F, Wu H. Large

sliding inguino-scrotal hernia of the urinary bladder:

A case report and literature review. Medicine

(Baltimore). 2018 Mar;97(13):e9998.

13. Allen RP, Condon VR. Transitory Extraperitoneal

Hernia of the Bladder in Infants (Bladder Ears). Radiology.

1961 Dec;77(6):979–83.

14. Kapisiz A, Karabulut R, Kaya C, Eryilmaz S, Turkyilmaz

Z, Atan A, et al. Our Cases and Literature Review

for Presence of Bladder Hernias in the Inguinal Region

in Children. Diagnostics. 2023 Apr 24;13(9):1533.

15. Aloi IP, Lais A, Caione P. Bladder injuries following

inguinal canal surgery in infants. Pediatr Surg Int.

2010 Dec;26(12):1207–10.

16. Khan A, Beckley I, Dobbins B, Rogawski K. Laparoscopic

repair of massive inguinal hernia containing

the urinary bladder. Urol Ann. 2014;6(2):159.

17. Elkbuli A, Narvel RI, McKenney M, Boneva D. Inguinal

bladder hernia: A case report and literature review.

Int J Surg Case Rep. 2019;58:208–11.

Ready - Made

Citation

Dimitrios Kourdakis MD, Maria-Michailia MD, Eleni Makridou MD,

Elena Hadjichristou MD, Ouroumidou Kristina MD, Manavi Aikaterini MD.

Urinary bladder herniation: CT evaluation, Hell J Radiol 2025; 10(4): 56-63.

63

H R J

Recent advances and future perspectives of artificial intelligence in medical imaging: a review, p. 64-76

VOLUME 10 | ISSUE 4

Review

Physics

Recent advances and future perspectives

of artificial intelligence in medical

imaging: a review

Akhil Raj.P, Kamalesh.C, Renisha Divina Dsouza, Vashist Baburao Mhalsekar

Unit of Medical Imaging Technology, Yenepoya School of Allied Health Sciences,

Yenepoya (Deemed to be University), Mangalore, Karnataka India


Abstract

Radiology can benefit significantly from various new

Artificial Intelligence (AI) tools. The application of machine

learning algorithms and deep learning techniques

to help radiologists interpret medical images more accurately

and efficiently and to lower radiographers'

human errors is known as AI in radiology. The goal of

this review article is to provide an overview of AI applications

in radiology for precise diagnosis and treatment,

optimizing workflow and image quality while reducing

radiation dose. Articles published related to the study

were searched and evaluated from indexed journals on

PubMed, Springer, and Elsevier databases from January

2000 to December 2023. With automation and process

optimization for data collecting, including patient positioning

and acquisition parameter settings. To enhance

various aspects of image quality, such as reducing image

noise and utilizing lower radiation doses for data

collection, it is necessary to take steps such as collecting

data, using advanced reconstruction algorithms,

employing image denoising techniques, and optimizing

image reconstruction parameters. In this review article,

we discussed the current developments of AI in the field

of medical imaging technology. We concluded that AI enhances

diagnosis and treatment planning; it has the potential

to transform the medical imaging and healthcare

sector completely.

Key words

Artificial Intelligence (AI), Computer Assisted Diagnosis (CAD), Deep Learning,

Deep Convolutional Neural Network (DCNN), Reconstruction

Corresponding

Author,

Guarantor

Renisha Divina Dsouza, Assistant Professor, Unit of Medical Imaging Technology,

Yenepoya School of Allied Health Sciences, Yenepoya (Deemed to be University),

Mangalore, Karnataka India

E mail: renishamdb98@gmail.com

64


VOLUME 10 | ISSUE 4

H R J

Introduction

Building computer programs with intelligent behaviour

is the focus of the large field of Artificial Intelligence

(AI) [1]. AI is becoming more prevalent in

medicine, with the most applicability being within the

radiology field [2]. AI has advanced perception, enabling

computers to depict and comprehend complicated

input [3]. Clinical AI is integrated into clinical

practice. To improve quality, manage resources, and

guarantee patient safety, governance structures supervise

the adoption, upkeep, and observation of clinical

AI algorithms [4]. AI and machine learning technologies

help radiologists to analyze medical images [5]. AI

in radiology promises to better patient care by expediting

the interpretation process and increasing diagnosis

accuracy. However, to guarantee that the technology is

used efficiently and securely, collaboration with radiologists

is crucial [6]. AI is anticipated to bring about significant

changes. Machine learning evaluates doctors'

performance in diagnosing and treating patients and

improving their expertise [7]. The use of AI in clinical

radiography is widely acknowledged [8]. AI is predicted

to alter clinical work procedures significantly, necessitating

the development of radiologist-specific additional

abilities [9]. The study aims to conduct a literature

review on recent advancements in the field of medical

imaging. Using AI can help with treatment planning,

outcome prediction, image analysis, detection, and

diagnosis. The primary aim of this review article is to

present a comprehensive survey of AI applications in

radiology. These applications aim to enhance accuracy

by providing precise diagnosis and treatment, optimizing

workflow and image quality while reducing radiation

dose.

Data Source:

Articles published related to the study were searched

and evaluated from indexed journals on PubMed,

Springer, and Elsevier databases from January 2000 to

December 2023

AI Interpretation in Imaging:

AI software applications are developed using convolutional

networks based on deep learning. Through an

automated procedure, these algorithms acquire picture

attributes from training datasets derived from radiologic

images. After that, they divide and examine the

structure seen in the pictures, summarising their findings

[10]. Early automated software, such as computer-assisted

diagnosis (CAD), was employed in different

studies with differing degrees of effectiveness.

Computed Assisted Diagnosis:

CAD, an automated processing system, has the potential

to aid radiologists in detecting and diagnosing

the study. Computer-assisted diagnosis (CADx) systems

and computer-assisted detection (CADe) systems are

the two categories under which CAD systems are divided.

While CADx systems aim to characterize the lesions,

CADe systems are primarily used to locate lesions in

medical imaging [11,12]. CADx systems use a classifier

to assess the risk of malignancy after extracting some

specified criteria from the images. However, with the

advent of deep learning techniques, significant advancements

in research and clinical AI applications

have been made possible.

AI-rad Companion:

AI-rad companion is a support platform that has been

approved by the FDA. It uses artificial intelligence algorithms

to handle Computed Tomography (CT) image

files. The program offers automatic image processing,

evaluation, and display of structures on CT scans. The

chest CT scan comprises all components.

1. The program identifies and differentiates lung

abnormalities, determines the precise position of

the abnormality, and provides measures of the lesion's

size, including the maximum and lowest linear

dimensions and multiplanar reformation.

2. Segmentation of the heart and detection of calcium.

3. Segmentation and measuring of the diameter of

the aorta.

4. Vertebra body segmentation and density measurements

[13].

Progress in both hardware and software has enabled

the establishment of a platform that can evaluate complete

datasets. Innovative research in this field mostly

concentrated on representational learning, where the

computer aimed to identify the fundamental features

of the data [14]. Deep learning models treat a dataset

as an ordered collection of features, with the number

of levels in the hierarchy corresponding to the depth of

the computational evaluation [15]. These novel meth-

65

H R J


VOLUME 10 | ISSUE 4

odologies have completely transformed the area of artificial

intelligence and sparked a significant interest in

the use of AI in medical applications.

Fig 1: Shows a representative of the summary page from

the AI platform. Source:[16]

Fig 1 represents that all the algorithms are combined

into a full multiorgan AI software solution that offers

automatically generated summary results to be shown

with AI-annotated photos to help with image interpretation

[16]. Many of these algorithms were created to

yield outcomes for certain anatomical structures. Some

of these have been combined into multiorgan AI software

systems that offer automatically generated summary

data, which are presented with AI-annotated pictures

to assist with interpreting the images [17,18].

Integrating AI into the therapeutic process has been

proposed as a highly effective way to increase workflow

efficiency, for example, by reducing report turnaround

times and enabling faster scan interpretation.

AI in Computed Tomography

AI and Deep Learning in Reconstruction:

With the implementation of AI techniques, image

reconstruction in the radiology field has considerably

advanced. The optimization of image quality, reduction

of artifacts, and improvement of diagnostic accuracy

are achieved by the application of machine learning

algorithms. Better visualization of anatomical features

is made possible by the potential for high-quality picture

reconstruction from low-dose scans through deep

learning models, such as convolutional neural networks

(CNNS) [19].

Filtered-back projection (FBP) is widely used as the

preferred technique for reconstructing CT images because

of its fast reconstruction time and efficient computational

ability, allowing real-time image reconstruction

while patients are being scanned. The primary

disadvantage of FBP is that it deteriorates image quality

in low-dose parameters and in patients with larger

size of body because of increased image noise and beam

hardening errors [20].

Iterative reconstruction is developed to get around

the drawbacks of FBP-based image reconstruction. Iterative

reconstruction is a computer technique that

improves image quality by improving the reconstruction

process. In contrast to classical FBP, iterative reconstruction

reduces artifacts and improves spatial

resolution by iterative optimization of images through

mathematical frameworks. In low-dose CT scans, iterative

image reconstruction techniques are useful

for maintaining image quality while reducing patient

radiation exposure [21]. Additionally, iterative reconstruction

has certain drawbacks. The primary one is

computational complexity; compared to conventional

methods, it necessitates significant processing power

and time, slowing down the reconstruction process. To

properly control the reconstruction process and optimize

settings, operators may need to possess a greater

level of skill [22].

AI-based reconstruction techniques are introduced

to eliminate the shortcomings of earlier systems. The

primary objective of the AI-based reconstruction algorithm

is to enhance the resolution of the image in

a short amount of time with low dosage exposure [23].

Deep Learning Reconstruction (DLR) techniques

that are sold commercially identify patterns of noise

in images and eliminate them from raw data by utilizing

a Deep Convolutional Neural Network (DCNN) [24].

Theoretically, artifacts like motion, truncation, cone

beam, and other image reconstruction issues can all be

resolved with DLR [25]. The training data sets used in

a DCNN are essential to its performance because they

teach the algorithm how to distinguish between anatomical

structures and noise. DCNN training has been

approached differently by each DLR developer, but the

66


VOLUME 10 | ISSUE 4

H R J

final reconstructed DLR picture is directly impacted by

the type and quality of training data [26].

Low-noise picture pairing data is used to train DCNN

algorithms. To optimize DLR accuracy and consistency,

node weights and hyperparameters are chosen. Pretrained

DCNNs offer reliable results and rapid reconstruction

[27].

The two main techniques that are available for purchase

are vendor-specific, which operates in image

space along with projection space, and vendor-independent,

which operates the DLR algorithm in image

space [28].

The vendor-independent method is mostly provided

by third-party solutions positioned between PACS

scanning and CT scanners. The CT scanner is used,

and the reconstruction algorithm is used to initially

reconstruct the data. After scanning, the picture is

transferred to a separate server that handles cleaning

up CT reconstructions [29,30]. Utilizing a vendor-specific

strategy, which has access to projection space

data, reduces picture noise mainly by improving object

edge definition and spatial resolution [31]. In addition

to methods like non-breath-hold scans and pre-scan

breath-hold instructions, motion artifacts can degrade

image quality. AI reconstruction algorithms can reduce

4D-CT artifacts and improve the geometric accuracy of

structures [32].

AI in Dose Reduction:

An essential objective in radiology is to lower the radiation

exposure during computed tomography imaging

without sacrificing diagnostic quality [34].

Artificial intelligence-powered automatic positioning

systems have surfaced as viable options for reducing

radiation exposure while maintaining high-quality

images [35]. Manually adjusting the scanner’s parameters

and the patient’s position during traditional CT

imaging might result in inconsistent image quality and

increased radiation exposure [36]. By utilizing cutting-edge

algorithms to optimize patient placement

and scanner settings depending on numerous criteria

such as patient anatomy, imaging protocol, and clinical

indication, AI-driven autonomous positioning systems

seek to address these issues [37].

During CT scans, technologists take great care to precisely

choose the anatomic scan range and correctly

center the patient to get high diagnostic image quality

at a lower radiation dose. The method of manually centering

and placing takes a lot of time, is technician-reliant,

inconsistent, and is not optimal [38].

AI auto placement makes use of a fixed, off-the-shelf,

2D/3D video camera positioned on the ceiling that can

measure distances to objects inside its range of vision.

The gantry-mounted touchscreens of the CT systems

show conventional RGB (Red, Green, Blue) video images.

Details regarding the gantry and table installation

geometry of each CT system, to ascertain the scout

scans' starting and finishing points, and the locations

of anatomical landmarks [39].

With the use of AI algorithms and a 3D infrared camera,

precise landmarks on the patient's body were identified,

and all anatomical areas were recorded, ensuring

that no pathology was missed in the area of interest.

Additionally, the system automatically moves the

Fig 2: Comparative analysis of CT image quality between iterative reconstruction and deep

learning-based reconstruction. Source: [33]

67

H R J


VOLUME 10 | ISSUE 4

Fig 3: The flexibility of the camera workflow, both on the console and at the gantry, allows for fast and consistent

positioning. Source:[44]

couch vertically so that the isocenter is where the majority

of the anatomical regions of interest are found.

By preventing incorrect patient centering, it lowers the

radiation doses that the patient receives [40].

The AI positioning algorithm identifies anatomical

landmarks and reference points required for precise

placement after it has analyzed the scout photos. The

AI algorithm determines how to modify the patient's

posture within the CT scanner to maximize its efficiency

based on the examination of the scout images and

predetermined placement criteria. These adjustments

could involve tilting, rotating, and translating the patient

table. Using input from the CT scanner's imaging

equipment, the AI program continuously tracks the

patient's location in real-time while the modifications

are being performed. To guarantee ideal alignment

throughout the scan, the algorithm can dynamically

modify the placement parameters [41,42].

When the X-ray tube is positioned underneath the

patient's table, the system can perceive the patient as

being either extremely thick or thin, depending on how

they are positioned about the isocenter. This is because

the isocenter is where a CT system's spatial calibration

occurs [43].

Although manufacturers have implemented AEC

(Automated Exposure Control) systems in somewhat

diverse ways in their industrial equipment, the fundamental

concepts remain the same. Thus, the number of

photons required for each projection through the patient

is automatically determined by the system. This

suggests that for the system to achieve the desired examination

quality and finish the assigned clinical task,

it will need to use an additional number of photons for

very large individuals. The patients are not homogeneous

cylinders; therefore, during a single gantry rotation,

the tube current typically oscillates up and down,

increasing through thicker body portions and reducing

over thinner body regions on average [45].

To ensure proper operation of these computational

methods, the patient has to be at the center of the isocenter

of the system. However, due to the wide variations

in patient anatomy, this can be difficult to do [46].

Fig 4: Automated planning of preview start and end

positions for a head (left) and abdomen (right)

scan. Source: [44]

Parameter Selection:

Several factors need to be carefully selected for the

particular type of imaging technique and diagnostic

indication to maximize the CT examination. These features

are related to the radiation therapy the patient is

68


VOLUME 10 | ISSUE 4

H R J

receiving, the way the X-ray tube and patient table are

moved, and the use of additional specialized methods

(such as cardiac gating) throughout the data collection

procedure. Currently, some AEC systems use simple

machine-learning algorithms to determine the optimal

tube current and potential. One of the most difficult

decisions is setting up the contrast administration and

image acquisition time such that, throughout data collection,

the iodine enhancement is maximal over the

area of interest [47].

To accomplish this, data were gathered on a sizable

number of patients at different times when the contrast

was injected and circulated throughout their bodies.

With the use of this data, an algorithm may predict the

ultimate dimensions of the aortic contrast enhancement

curve. The system can forecast the whole contrast

enhancement curve in patients who follow once the

training data is exceeded. Using only a few data points

on the ascending portion of the curve, it is possible to

estimate the best time to perform the scan while the

contrast is traveling through the patient's body [48].

Research has shown that there is a decrease in the

amount of iodinated contrast medium needed in conjunction

with improved consistency of contrast enhancement

throughout the scan range. Slashing the

injection rate lowers the chance of damaging the vein

that the material is injected into, hence reducing the

amount of iodine [49].

AI in MRI:

Magnetic resonance imaging (MRI) is a valuable tool

in clinical medicine since it can visualize human organs

and tissues to aid in follow-up diagnosis. However,

MRI has always faced the difficulty of long scan times.

Before the emergence of deep learning, two common

methods for accelerating MRI were used: compressed

sensing (CS), which uses picture compressibility, and

parallel imaging, which uses redundant information

between coils. Although these methods have made significant

accomplishments, they still confront the challenges

of extended iteration time and low acceleration

rate [50].

Deep learning has recently emerged as an MRI acceleration

approach. When compared to previous approaches,

it not only enhances image quality but also

provides the benefits of real-time imaging. The peak

signal-to-noise ratio (PSNR) and mean structure similarity

index measure (MSSIM) are used to thoroughly

evaluate image quality. Higher PSNR indicates less

noise, while higher MSSIM indicates better structure

resemblance to the ground truth. Meanwhile, real-time

imaging is vital for some clinic applications, such as

deep learning, which can enable real-time adaptive

magnetic resonance imaging (MRI)-guided radiation

by obtaining larger acceleration factors to reduce total

delays [51].

AI can impact all phases of the Magnetic Resonance

Imaging (MRI) process, including image acquisition and

reconstruction, image analysis and interpretation, and

diagnosis and prognosis. If realized, the intelligent imaging

revolution will result in faster acquisition times,

less effort for physicians, lower healthcare costs, and

more personalized treatment decisions for patients

[52].

Acquisition and Reconstruction

Artificial intelligence is being integrated into the

MRI acquisition process, which normally entails the

collecting of raw data from the MRI scanner. AI can

address difficulties like acquisition time, SNR, balancing

spatial and temporal resolutions, and artifacts. The

acquisition is improved by optimizing scanning parameters,

lowering scan duration, and motion correction,

and by providing real-time feedback [53].

AI algorithms can automatically modify scanning parameters

(such as magnetic field intensity, gradient settings,

and pulse sequences) to optimize image quality

for each patient and clinical task. This lowers the time

and effort required for manual corrections, resulting

in a faster and higher-quality scan. AI can accelerate

data collection by employing techniques such as compressed

sensing (CS) and parallel imaging. These technologies

reduce the number of measurements required

for high-quality photos, allowing patients to spend less

time in the scanner. Furthermore, AI-driven algorithms

can aid in the recovery of missing data, hence enhancing

image quality [54-56].

Patient movement during an MRI scan can cause image

artifacts. AI systems can detect and adjust for these

motions in real time, increasing the quality of the collected

data. AI can help technologists provide real-time

feedback, enabling dynamic modifications during scans

to ensure optimal data gathering, particularly in difficult

circumstances (e.g., pediatric or elderly patients).

69

H R J


VOLUME 10 | ISSUE 4

Once the raw data has been gathered, it must be rebuilt

into meaningful images that radiologists can analyze.

Traditional reconstruction approaches, such as the

Fourier transformation, use complicated mathematical

techniques to convert data into images. AI is increasingly

being utilized to improve or perhaps replace traditional

approaches [57,58].

Compressed sensing is a technique that allows for

the reconstruction of high-quality images from fewer

data points, resulting in shorter scan durations. Artificial

intelligence models, particularly deep learning

techniques like convolutional neural networks (CNNs),

can significantly enhance the quality of reconstructed

images by learning how to retrieve missing information

and decrease noise. AI can help improve the usage of

multi-coil arrays (many receiver coils) and parallel imaging

techniques, which are intended to speed up image

acquisition and increase image quality by minimizing

the number of data points acquired from each coil.

AI algorithms can speed up and improve the quality of

these reconstructions [59,60].

Image Analysis and Interpretation

AI can automatically separate and delineate regions

of interest in MRI images. This makes it possible to quantify

and track illness progression more precisely. Objectively

measure factors such as tissue density, volume,

and lesion progression, providing information that is

sometimes difficult to get manually. AI algorithms are

being developed to help detect abnormalities such as

tumors, plaques, cysts, and other pathological diseases.

AI can identify regions of interest in MRI data, allowing

radiologists to concentrate on potentially troublesome

areas. AI may be trained on massive datasets to recognize

patterns linked with various diseases. For example,

it can assist in detecting early signs of Alzheimer's disease

by analyzing brain scans for typical abnormalities,

such as atrophy, that may not be immediately obvious

to the naked eye. AI can use MRI scans in conjunction

with clinical data to forecast the evolution of diseases

like cancer or neurodegenerative disorders. For example,

AI models can monitor tumor growth over time or

assess how a patient's health is reacting to treatment.

By analyzing longitudinal MRI data, AI can anticipate

how patients will respond to various medications, allowing

for personalized treatment plans that optimize

patient outcomes [61,62].

Clinical Workflow Optimization

AI models can provide early reports based on MRI

data, identifying important abnormalities and summarising

the images. This not only reduces radiologists'

workloads but also speeds up the reporting process,

especially in busy hospitals or radiology departments.

AI systems can reduce human error in interpretation,

particularly when dealing with enormous datasets or

complicated imagery. For example, AI systems might

help radiologists by highlighting tiny indications of

disease that might otherwise go unnoticed, thereby enhancing

diagnostic accuracy.

AI models can be incorporated into existing healthcare

systems, adding a layer of decision support. AI can

suggest diagnoses or notify healthcare practitioners of

important issues by merging MRI data with other patient

information contained in Integration with Electronic

Health Records (HER) systems.

AI has improved the value of functional MRI (fMRI),

which monitors brain activity by detecting blood flow.

AI can help identify brain activity patterns associated

with specific tasks or cognitive processes, which can

benefit in the research of neurological disorders such

as epilepsy, Alzheimer's, and Parkinson's disease [63-

65].

Future Prospects

AI in MRI has huge potential for personalized medicine,

allowing tailored treatment plans based on individual

patient data. By evaluating MRI scans and other

medical data, AI can predict how a patient's illness will

proceed and how they will respond to various therapies.

AI will most likely be used to integrate MRI with other

imaging modalities to provide a more complete picture

of a patient's condition. This could increase diagnostic

accuracy and allow for more targeted treatment

delivery. The combination of AI and robotic technology

could improve MRI operations. For example, AI-powered

robots could help with patient placement or guide

MRI scanners to appropriate locations, boosting operation

accuracy and comfort [66].

AI in Ultrasound:

Ultrasonography (USG) is the most widely used radiologic

method because it is widely accessible, reasonably

priced, and radiation-free. The global USG surveil-

70


VOLUME 10 | ISSUE 4

H R J

lance rate is still low despite several benefits [67]. The

availability of qualified personnel, such as inadequate

radiologists and ultrasoundographers who can effectively

serve for cancer surveillance, is one of the main

obstacles to surveillance, especially in rural areas. Because

USG depends on the operator, the results of the

examination may be interpreted incorrectly. However,

these obstacles might be addressed by creating an assisted

ultrasound system that aids in the identification

and categorization of focal liver lesions (FLLs) during a

real-time USG scan [68].

AI has become more widely used in healthcare, particularly

to improve the sensitivity and accuracy of

medical picture interpretation, as a result of the quick

development of deep learning algorithms and their

great capacity to analyze complicated data. Healthcare

workers and non-radiologist physicians would

greatly benefit from an AI-assisted USG image analysis

system to improve the precision of USG inspection

and interpretation. One possible tactic to raise overall

liver cancer surveillance rates is this system. Despite

the limitations of the detection, artificial intelligence

(AI) models that were recently developed for diagnosing

and detecting FLLs in ultrasound images showed

promising performance with increased sensitivity and

specificity for detecting and classifying FLLs typical in

clinical practice. For example, a lack of differentiation

and characterization skills resulted in incorrect categorization

[69-71].

AI in deep learning and detection:

Real-time object detection in photos is better suited

for a more recent class of AI models called "YOLO,"

which has demonstrated superior performance over

Convolution neural network models in object detection

tasks. Using the model as a framework, distinct kinds

of FLLs in USG still images may be identified and distinguished.

AI Model

The AI model used for the study was YOLOv5 framework.

Because of its tiny size and quick processing performance,

YOLO has gained popularity as a real-time

object identification method since its initial release in

2015. The most recent version at the time of the investigation

was YOLOv5, which had far better capabilities

than the earlier YOLOv4; that is, it had a lighter model

size, more adaptability, and a significantly faster training

pace. It offers data augmentation features that effectively

identify small objects, which was the previous

YOLO mode's most troublesome drawback [72,73].

In order to obtain a high-level feature map that represents

the input image, YOLO's methodology entails

processing the entire input image through a deep neural

network.

After that, this feature map is divided into an N×N

grid, where N is a number that the user defines. The

job assigned to each cell in this grid is to identify objects

whose centers lie inside its borders. Each cell is

projected to have numerous bounding boxes to accommodate

objects with different dimensions and aspect

ratios [74].

YOLO makes a lot of bounding box predictions during

the inference stage. Bounding boxes with no liver lesions,

however, receive poor confidence scores in every

class and are eventually removed. Be aware that a single

FLL may have several bounding boxes created by YOLO.

A post-processing method called "non-maximum suppression"

(NMS) is used to lessen this redundancy. This

approach uses the intersection over union (IoU) metric

to estimate the similarity of groups of proximal bounding

boxes. Only the bounding box with the greatest confidence

score within each group is kept.

The NMS algorithm's confidence threshold and IoU

threshold are essential for regulating the YOLOv5

model's performance. An IoU threshold of 0.3 and a

confidence threshold of 0.25 were used as criteria for

accurate detection in order to assess detection rates

and maximize model performance. The class posterior

probabilities were created by aggregating and normalizing

the confidence scores from each of the seven

differential diagnoses in order to replicate real-world

clinical situations that can arise during a USG examination.

These odds were then shown for each FLL in

descending order, resulting in a prioritized list of possible

diagnoses that closely mimics the clinical practice

decision-making process.

With this new AI model, YOLOv5, we can demonstrate

a good performance in detecting and diagnosing malignant

focal external liver lesions, including Hepatocellular

carcinoma and Cholangiocarcinoma, and benign

FLLs on Ultrasound still images. More external validation

and real-time clinical performance are required to

enhance its applicability [75].

71

H R J


VOLUME 10 | ISSUE 4

AI in Positron Emission Tomography (PET):

One of the initial implementations of AI in PET imaging

has been to facilitate picture reconstructions, utilizing

either the individual counts captured by the PET

detectors in the form of a sinogram or an existing reconstruction

generated by conventional methods. The

most comprehensible technique for PET reconstruction

is filtered back-projection; however, this approach has

predominantly been replaced by iterative reconstruction

approaches such as ordered subset optimization

maximization (OSEM) [76]. The initial presentation of

direct sensor-to-image translation was conducted by

Zhu et al., who termed it AUTOMAP [77]. In this model,

a neural network that is completely linked is positioned

between the intended reconstruction and the sensor

signals. Haggstrom et al. [78]. introduced a technique

they refer to as "Deep PET," which had several benefits,

such as shorter processing time and noise reduction.

The technique was trained on simulated data, where

it was comparable to an ideal reconstruction, and it was

implemented to real human data, but it seemed to produce

images that resembled OSEM but with less noise.

Since DL models frequently take an extended time to

train but are relatively quick to apply once trained (a

procedure called “inference"), it would be expected

that such a technique could facilitate quick image creation

at the scanner and enhance patient workflow.

Discussion:

The benefits of technological advancements in radiology

and the newly emerging domain of radiomics

parallel those experienced in other sectors that have

transitioned to digital systems. However, challenges

persist, particularly regarding the assumption that

machines and computers displace human employment,

often viewed as a limitation to the widespread utilization

of AI in radiology [79]. It has been expected that a

significant portion of the duties performed by anatomic

pathologists and radiologists would be achievable by

machine learning in the future, affecting human employment

in these fields. Furthermore, machine learning

methodologies will probably advance significantly

in the next 5–10 years, thereby affecting radiology as a

thriving human profession [80]. Approval of AI results

may lead to numerous consequences. AI algorithms are

supposed to improve the results, rather than substitute

for radiologists. The next risk is that dependence on

technology generally affects human intelligence and

may lead to declining human capabilities [81].

Advantages of artificial intelligence for radiologists

Radiomics analysis is significantly more powerful

than the human eye or brain since it can identify more

than 400 characteristics from CT, MRI, or PET scans and

connect these features with other data. These characteristics

might be utilized to forecast treatment response

and prognosis [82,83].

AI can facilitate the standard reporting process, correlate

words, photos, and quantitative data, and ultimately

provide the most likely diagnosis. CDE (Common Data

Elements) consists of AI software programs like computer-assisted

reporting systems, and radiology case

reports for clinical research. CDE can serve as an AI language

for creating a customized, organized report for a

patient [84]. AI can compare the present and past examinations,

particularly in the oncologic follow-up that collects

the data and will help the radiologist to prepare the

final report [85]. It can rapidly identify normal studies

which will benefit radiologists and they can report only

the abnormal studies and save time [86].

Deep learning algorithms can recognize pictures to

identify the presence of a fracture and subsequently

employ segmentation to evaluate the pattern of fracture

[87]. AI has demonstrated comparable or superior

performance to experienced radiologists in identifying

fractures on radiographs and serves as a possible

resource for healthcare professionals to reduce faults

and alleviate burnout [88]. Another benefit of AI in radiography

includes computerized measurements, such

as implant placement, joint direction, leg length, or leg

alignment [89-92].

One significant advantage of AI in clinical practice is

its ability to handle at emergency situations.AI can identify

a fracture before radiologists report it. Prioritizing

reading studies with possibly favourable findings allows

radiologists to minimize the time between initial nonexpert

reading and final report, thus enhancing patient

care. Another possible advantage of AI is reduced examination

times. Radiologists may read 200–300 radiographs

a day, thus even a small decrease in reading time, even

by a few seconds for each radiographic examination, can

add up to significant time savings. AI-assisted fracture

detection also holds promise for improving radiologists'

and non-radiologists' diagnostic skills by avoiding errors

brought on by human exhaustion or satisfaction

72


VOLUME 10 | ISSUE 4

H R J

bias in image reporting in addition to identifying subtle

findings that are hard for human eyes to see. [93,94].

Conclusion:

The impact of AI technology on radiology has been extensively

addressed in the field. AI interpretation takes

a major step ahead with AI-RAD companion, which pretends

to be a major advancement in interpretation. As

a way, AI deep learning reconstruction methods are

considered to be advanced reconstruction methods

that gradually reduce the radiation dose to the patient.

Automated AI positioning and advanced parameter selection

thus eventually work to reduce the radiation

dose. This review concludes that AI enhances diagnosis

and treatment planning, potentially transforming the

medical imaging and healthcare sector completely. R

Abbreviations:

Artificial Intelligence (AI), Computer Assisted Diagnosis

(CAD), Deep Convolutional Neural Network (DCNN),

Filtered back projection (FBP), AEC (Automated Exposure

Control)

References

1. Razavian N, Knoll F, Geras KJ. Artificial Intelligence

Explained for Nonexperts. Semin Musculoskelet Radiol

2020; 24(1): 3-11.

2. Machinery C. Computing machinery and intelligence

Turing. Mind. 1950;59(236):433.

3. Hosny A, Parmar C, Quackenbush J, et al. Artificial

intelligence in radiology. Nat Rev Cancer.

2018;18(8):500-10.

4. Strohm L, Hehakaya C, Ranschaert ER, et al. Implementation

of artificial intelligence (AI) applications

in radiology: hindering and facilitating factors. Eur

Radiol. 2020;30(10):5525-32.

5. Van Assen M, Muscogiuri G, Caruso D, et al. Artificial

intelligence in cardiac radiology. Radiol Med.

2020;125(11):1186–99.

6. Mintz Y, Brodie R. Introduction to artificial intelligence

in medicine. Minim Invasive Ther Allied Technol.

2019;28(2):73–81.

7. Calandriello L, Walsh SL. Artificial intelligence for

thoracic radiology: from research tool to clinical

practice. Eur Respir J. 2021;57(5):2100192.

8. Chassagnon G, Vakalopoulou M, Paragios N, Revel

MP. Artificial intelligence applications for thoracic

imaging. Eur J Radiol. 2020; 1;123:108774.

9. Zhang Z, Seeram E. The use of artificial intelligence

in computed tomography image reconstruction - a

literature review. J Med Imaging Radiat Sci. 2020;

1;51(4):671-7

10. Auffermann WF, Gozansky EK, Tridandapani S. Artificial

intelligence in cardiothoracic radiology. Am J

Roentgenol. 2019 ;212(5):997-1001.

11. Kobayashi T, Xu XW, MacMahon H, et al. Effect of

a computer-aided diagnosis scheme on radiologists'

performance in detection of lung nodules on radiographs.

Radiology. 1996;199(3):843-8.

12. Tchou PM, Haygood TM, Atkinson EN, et al. Interpretation

time of computer-aided detection at screening

mammography. Radiology. 2010;257(1):40-6

13. Siemens Healthineers. AI-Rad Companion. Available

from: https://www.siemens-healthineers.com/

digital-health-solutions/ai-rad-companion.

14. Bengio Y, Courville A, Vincent P. Representation

learning: A review and new perspectives. IEEE Trans

Pattern Anal Mach Intell. 2013;35(8):1798-828.

15. Chartrand G, Cheng PM, Vorontsov E, et al. Deep

learning: a primer for radiologists. Radiographics.

2017;37(7):2113-31

16. Yacoub B, Varga-Szemes A, Schoepf UJ, et al. Impact

of artificial intelligence assistance on chest CT interpretation

times: a prospective randomized study. Am

J Roentgenol. 2022;219(5):743-51

17. Rao B, Zohrabian V, Cedeno P, et al. Utility of artificial

intelligence tool as a prospective radiology peer

reviewer - detection of unreported intracranial hemorrhage.

Acad Radiol. 2021;28(1):85-93

18. Leung KM, Curran-Everett D, Regan EA, et al. Translation

of adapting quantitative CT data from research

to local clinical practice: validation evaluation

of fully automated procedures to provide lung

volumes and percent emphysema. J Med Imaging.

2020;7(2):022404.

19. Kambadakone A. Artificial intelligence and CT image

reconstruction: potential of a new era in radiation

dose reduction. J Am Coll Radiol. 2020;17(5):649-51

73

H R J


VOLUME 10 | ISSUE 4

20. Kalra MK, Sodickson AD, Mayo-Smith WW. CT radiation:

key concepts for gentle and wise use. Radiographics.

2015;35(6):1706-21.

21. Willemink MJ, Noël PB. The evolution of image reconstruction

for CT - from filtered back projection to

artificial intelligence. Eur Radiol. 2019;29(5):2185-95

22. Singh R, Wu W, Wang G, Kalra MK. Artificial intelligence

in image reconstruction: the change is here.

Phys Med. 2020;79:113-25.

23. La Rivière PJ, Bian J, Vargas PA. Penalized-likelihood

sinogram restoration for computed tomography. IEEE

Trans Med Imaging. 2006;25(8):1022-36.

24. Lee T, Seeram E. The use of artificial intelligence in

computed tomography image reconstruction: a systematic

review. Radiology. 2020;4(2):30-8.

25. Szczykutowicz TP, Toia GV, Dhanantwari A, Nett B. A

review of deep learning CT reconstruction: concepts,

limitations, and promise in clinical practice. Curr Radiol

Rep. 2022;10(9):101-15.

26. Wu D, Ren H, Li Q. Self-supervised dynamic CT perfusion

image denoising with deep neural networks.

IEEE Trans Radiat Plasma Med Sci. 2020;5(3):350-61.

27. Jensen CT, Liu X, Tamm EP, et al. Image quality assessment

of abdominal CT by use of new deep learning

image reconstruction: initial experience. Am J Roentgenol.

2020 ;215(1):50-7.

28. Oostveen LJ, Meijer FJ, de Lange F, et al. Deep learning–

based reconstruction may improve non-contrast cerebral

CT imaging compared to other current reconstruction

algorithms. Eur Radiol. 2021;31:5498-506.

29. Nakamura Y, Higaki T, Tatsugami F, et al. Possibility

of deep learning in medical imaging focusing improvement

of computed tomography image quality.

J Comput Assist Tomogr. 2020;44(2):161-7

30. Brendlin AS, Plajer D, Chaika M, et al. AI denoising

significantly improves image quality in whole-body

low-dose computed tomography staging. Diagnostics.

2022 ;12(1):225.

31. Nam JG, Ahn C, Choi H, et al. Image quality of ultralow-dose

chest CT using deep learning techniques:

potential superiority of vendor-agnostic post-processing

over vendor-specific techniques. Eur Radiol.

2021;31(7):5139-47.

32. Akagi M, Nakamura Y, Higaki T, et al. Deep learning

reconstruction improves image quality of abdominal

ultra-high-resolution CT. Eur Radiol.

2019;29(11):6163-6171.

33. PhantomX. Deep learning reconstruction of CT images

[internet]. PhantomX; 2022 Mar 25 [cited 2025 Jan 25].

Available from: https://phantomx.de/2022/03/25/

deep-learning-reconstruction-of-ct-images.

34. Doda Khera R, Singh R, Homayounieh F, et al. Deploying

clinical process improvement strategies to reduce

motion artifacts and expiratory phase scanning in

chest CT. Sci Rep. 2019 Aug;9(1):11858.

35. Yu L, Liu X, Leng S, Kofler JM, et al. Radiation dose reduction

in computed tomography: techniques and future

perspective. Imaging Med. 2009;1(1):65.

36. Salimi Y, Shiri I, Akavanallaf A, et al. Fully automated

accurate patient positioning in computed tomography

using anterior–posterior localizer images and

a deep neural network: a dual-center study. Eur Radiol.

2023;33(5):3243-52.

37. DeMarco JJ, Cagnon CH, Cody DD, et al. Estimating

radiation doses from multidetector CT using Monte

Carlo simulations: effects of different size voxelized

patient models on magnitudes of organ and effective

dose. Phys Med Biol. 2007;52(9):2583.

38. Goldman LW. Principles of CT: radiation dose and image

quality. J Nucl Med Technol. 2007;35(4):213-25

39. GE Healthcare. AI Auto-Positioning White Paper. Available

from: https://www.gehealthcare.fr/-/jssmedia/

gehc/us/images/products/revolution-ascend/files/

ai-auto-positioning-white-paper.pdf?rev=-1

40. Fiagbedzi EW, Gorleku PN, Nyarko S, et al.The role

of artificial intelligence (AI) in radiation protection

of computed tomography and fluoroscopy: A review.

Open J Med Imaging. 2022;12(1):25-36.

41. Saltybaeva N, Schmidt B, Wimmer A, et al. Precise

and automatic patient positioning in computed tomography:

avatar modeling of the patient surface

using a 3-dimensional camera. Invest Radiol. 2018

;53(11):641-6.

42. Booij R, Budde RPJ, Dijkshoorn ML, et al. Accuracy

of automated patient positioning in CT using a

3D camera for body contour detection. Eur Radiol.

2019;29(5):2079-2088.

43. Boone JM, Cooper III VN, Nemzek WR, et al. Monte

Carlo assessment of computed tomography dose to

tissue adjacent to the scanned volume. Med Phys.

2000;27(10):2393-407.

44. Precise Position. AI to save time and improve precision

in CT patient positioning [internet]. Philips.

com. Available from: https://www.philips.com/c-

74


VOLUME 10 | ISSUE 4

H R J

dam/b2hc/master/resourse-catalog/landing/precise-suite/incisive_precise_position.pdf.

45. Toth TL, Cesmeli E, Ikhlef A, Horiuchi T. Image quality

and dose optimization using novel x-ray source filters

tailored to patient size. Med Imaging 2005;5745:283-91.

46. Huda W, Lieberman KA, Chang J, Roskopf ML. Patient

size and x-ray technique factors in head computed

tomography examinations. I. Radiation doses. Med

Phys. 2004;31(3):588-94.

47. McCollough CH, Primak AN, Braun N, et al. Strategies

for reducing radiation dose in CT. Radiol Clin North

Am. 2009;47(1):27-40.

48. McCollough CH, Leng S. Use of artificial intelligence

in computed tomography dose optimization. Ann

ICRP. 2020;49(1_suppl):113-25.

49. Sahbaee P, Abadi E, Segars WP, et al. The effect of contrast

material on radiation dose at CT: Part II. A systematic

evaluation across 58 patient models. Radiology.

2017;283(3):749-57.

50. Zeng G, Guo Y, Zhan J, et al. A review on deep learning

MRI reconstruction without fully sampled k-space.

BMC Med Imaging. 2021;21(1):195.

51. Terpstra ML, Maspero M, d'Agata F, et al. Deep learning-based

image reconstruction and motion estimation

from undersampled radial k-space for real-time

MRI-guided radiotherapy. Phys Med Biol.

2020 ;65(15):155015.

52. Küstner T, Qin C, Sun C, Ning L, Scannell CM. The intelligent

imaging revolution: artificial intelligence in

MRI and MRS acquisition and reconstruction. Magn

Reson Mater Phys Biol Med. 2024;1-5.

53. Shimron E, Tamir JI, Wang K, Lustig M. Implicit

data crimes: Machine learning bias arising from

misuse of public data. Proc Natl Acad Sci. 2022

Mar;119(13):e2117203119.

54. Pruessmann KP, Weiger M, Scheidegger MB, Boesiger

P. SENSE: sensitivity encoding for fast MRI. Magn Reson

Med. 1999;42(5):952-62.

55. Griswold MA, Jakob PM, Heidemann RM, et al. Generalized

autocalibrating partially parallel acquisitions

(GRAPPA). Magn Reson Med. 2002;47(6):1202-10.

56. Lustig M, Pauly JM. SPIRiT: iterative self-consistent

parallel imaging reconstruction from arbitrary

k-space. Magn Reson Med. 2010;64(2):457-71.

57. Zhou Z, Hu P, Qi H. Stop moving: MR motion correction

as an opportunity for artificial intelligence.

Magn Reson Mater Phys Biol Med. 2024;1-3.

58. Li X, Zhang L, Yang J, Teng F. Role of artificial intelligence

in medical image analysis: a review of current

trends and future directions. J Med Biol Eng. 2024;1-3.

59. Küstner T, Fuin N, Hammernik K, et al. CINENet: deep

learning-based 3D cardiac CINE MRI reconstruction

with multi-coil complex-valued 4D spatio-temporal

convolutions. Sci Rep. 2020 ;10(1):13710.

60. Schlemper J, Caballero J, Hajnal JV, et al. A deep cascade

of convolutional neural networks for MR image reconstruction.

In: Information Processing in Medical Imaging:

25th International Conference, IPMI 2017, Boone,

NC, USA, June 25-30, 2017, Proceedings 25. Boone:

Springer International Publishing; 2017. p. 647-58.

61. Kaur P, Singh RK. A review on optimization techniques

for medical image analysis. Concurr Comput

Pract Exp. 2023;35(1):e7443.

62. Sohan MF, Basalamah A. A systematic review on federated

learning in medical image analysis. IEEE Access.

2023;11:28628-44.

63. Ranschaert E, Topff L, Pianykh O. Optimization of radiology

workflow with artificial intelligence. Radiol

Clin. 2021;59(6):955-66.

64. Liao Y, Liu H, Spasić I. Deep learning approaches to

automatic radiology report generation: A systematic

review. Informatics Med Unlocked. 2023;39:101273

65. Kocak B. Artificial intelligence to predict task activation

from resting state fMRI. Eur Radiol. 2021;31 (7):5251-2.

66. Wu C, Zhang R, Kotagiri R, Bouvry P. Strategic decisions:

survey, taxonomy, and future directions from

artificial intelligence perspective. ACM Comput Surv.

2023;55(12):1-30.

67. Singal AG, Tiro JA, Murphy CC, et al. Patient-reported

barriers are associated with receipt of hepatocellular

carcinoma surveillance in a multicenter cohort

of patients with cirrhosis. Clin Gastroenterol Hepatol.

2021;19(5):987-95.

68. Teerasarntipan T, Sritunyarat Y, Prathyajuta P, et al.

Physician- and patient-reported barriers to hepatocellular

carcinoma surveillance: A nationwide survey.

Medicine. 2022;101(36):e30538.

69. Oren O, Gersh BJ, Bhatt DL. Artificial intelligence

in medical imaging: switching from radiographic

pathological data to clinically meaningful endpoints.

Lancet Digit Health. 2020;2(9):e486-8.

70. Cao LL, Peng M, Xie X, et al. Artificial intelligence

in liver ultrasound. World J Gastroenterol. 2022

Jul;28(27):3398.

75

H R J


VOLUME 10 | ISSUE 4

71. Zhou LQ, Wang JY, Yu SY, et al. Artificial intelligence

in medical imaging of the liver. World J Gastroenterol.

2019;25(6):672..

72. Dadoun H, Rousseau AL, de Kerviler E, et al. Deep

learning for the detection, localization, and characterization

of focal liver lesions on abdominal US images.

Radiol Artif Intell. 2022;4(3):e210110.

73. Jiang P, Ergu D, Liu F, Cai Y, Ma B. A review of YOLO

algorithm developments. Procedia Comput Sci.

2022;199:1066-73.

74. Cheng R. A survey: Comparison between Convolutional

Neural Network and YOLO in image identification.

J Phys Conf Ser. 2020;1453(1):012139.

75. Chaiteerakij R, Ariyaskul D, Kulkraisri K, et al. Artificial

intelligence for ultrasonographic detection and

diagnosis of hepatocellular carcinoma and cholangiocarcinoma.

Sci Rep. 2024;14(1):20617.

76. HM H. Accelerated image reconstruction using ordered

subsets of projection data. IEEE Trans Med Imaging.

1994;13(4):594-600.

77. Zhu B, Liu JZ, Cauley SF, et al. Image reconstruction

by domain- transform manifold learning. Nature.

2018;555(7697):487-92.

78. Häggström I, Schmidtlein CR, Campanella G, Fuchs

TJ. Deep PET: A deep encoder–decoder network for

directly solving the PET image reconstruction inverse

problem. Med Image Anal. 2019;54:253-62.

79. Utomo CP, Kardiana A, Yuliwulandari R. Breast cancer

diagnosis using artificial neural networks with

extreme learning techniques. IJARAI. 2014;3(4):4-10.

80. Kermany DS, Goldbaum M, Cai W, et al. Identifying

medical diagnoses and treatable diseases by image

based deep learning. Cell. 2018;172(5):1122-1131

81. Osoba OA, Welser W. The risks of artificial intelligence

to security and the future of work. Santa Monica,

CA: RAND; 2017 Dec 6

82. Lambin P, Rios-Velazquez E, Leijenaar R, et al.. Radiomics:

extracting more information from medical

images using advanced feature analysis. Eur J Cancer.

2012;48(4):441-6.

83. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images

are more than pictures, they are data. Radiology.

2016 ;278(2):563-77.

84. Rubin DL, Kahn Jr CE. Common data elements in radiology.

Radiology. 2017;283(3):837-44.

85. Nance JW Jr, Meenan C, Nagy PG. The future of the

radiology information system. AJR Am J Roentgenol.

2013;200(5):1064-70

86. Kolossváry M, Kellermayer M, Merkely B, Maurovich-Horvat

P. Cardiac computed tomography radiomics:

a comprehensive review on radiomic techniques.

J Thorac Imaging. 2018 ;33(1):26-34.

87. Kalmet PH, Sanduleanu S, Primakov S, et al. Deep

learning in fracture detection: a narrative review.

Acta Orthop. 2020;91(2):215-20.

88. Ajmera P, Kharat A, Botchu R, et al. Real-world analysis

of artificial intelligence in musculoskeletal trauma.

J Clin Orthop Trauma. 2021;22:101573.

89. Lee CS, Lee MS, Byon SS, et al. Computer-aided automatic

measurement of leg length on full leg radiographs.

Skeletal Radiol. 2021;1:0

90. Pei Y, Yang W, Wei S, Cai R, et al. Automated measurement

of hip–knee–ankle angle on the unilateral

lower limb X-rays using deep learning. Phys Eng Sci

Med. 2021 Mar;44:53-62.

91. Schock J, Truhn D, Abrar DB, et al. Automated analysis

of alignment in long-leg radiographs by using a

fully automated support system based on artificial

intelligence. Radiol Artif Intell. 2020;3(2):e200198.

92. Simon S, Schwarz GM, Aichmair A, et al. Fully automated

deep learning for knee alignment assessment

in lower extremity radiographs: a cross-sectional diagnostic

study. Skeletal Radiol. 2022;1:1-1.

93. Lee CS, Nagy PG, Weaver SJ, Newman-Toker DE.

Cognitive and system factors contributing to diagnostic

errors in radiology. Am J Roentgenol. 2013

Sep;201(3):611-7.

94. Hartigan S, Brooks M, Hartley S, et al. Review of the basics

of cognitive error in emergency medicine: still no

easy answers. West J Emerg Med. 2020 Nov;21(6):125.

Ready - Made

Citation

Akhil Raj.P, Kamalesh.C, Renisha Divina Dsouza, Vashist Baburao Mhalsekar.

Recent advances and future perspectives of artificial intelligence in medical

imaging: a review, Hell J Radiol 2025; 10(4): 64-76.

76

H R

J

Popliteal Artery Pathology: An Uncommon Yet Critical Clinical Challenge, p. 78-83

VOLUME 10 | ISSUE 4


Vascular Imaging

Popliteal Artery Pathology:

An Uncommon Yet Critical

Clinical Challenge

Andrioti Petropoulou Nefeli, Papatheodorou Athanasios, Tsanis Antonios

Radiology and Interventional Department, Hellenic Red Cross Hospital Korgialeneio-Benakeio, Athens, Greece


part a

Popliteal Artery Pathology:

An Uncommon Yet Critical Clinical Challenge

A 46-year-old woman presented at the hospital with a

20-day history of intermittent claudication of the right

calf, which had been gradually worsening. The patient

had a history of mild tobacco use and oral contraceptive

use. There was no history of rheumatologic or other

systemic disease.

Clinical examination revealed a right lower limb

slightly colder than the left. Initial laboratory tests

were unremarkable. An arterial duplex ultrasound of

the right lower extremity demonstrated mural thick-

ening of the popliteal artery (Fig.1) and occlusion of the

proximal portion of the tibial-fibular trunk.

The distal arteries were patent with no evidence of

thrombosis. Further evaluation with echocardiogram

and transesophageal echocardiography showed no abnormalities.

Computed tomography arteriography (CTA) and digital

subtraction angiography (DSA) (Fig. 2) were conducted,

confirming the previous findings and revealed

the presence of collateral arterial network at the right

lower limb. Magnetic resonance imaging (MRI) of the

knee was performed as depicted below (Fig. 3,4).

Key words

Knee MRI, Popliteal Artery Entrapment Syndrome (PAES),

Artery Occlusive Diseases, Popliteal fossa

Corresponding

Author,

Guarantor

Nefeli Andrioti Petropoulou, MD, Radiology resident

Email: nefeli.andre@gmail.com

Radiology and Interventional Department, Hellenic Red Cross Hospital

Korgialeneio-Benakeio

78


VOLUME 10 | ISSUE 4

H R J

Figure 1. US of the popliteal artery, axial view.

Figure 2. DSA of the popliteal fossa.

Figure 3. Axial view, proton-density fat-suppression

weighted imaging of the right knee.

Figure 4. Sagittal view, T1 weighted

imaging of the right knee.

79

H R

J


VOLUME 10 | ISSUE 4

Diagnosis

Popliteal Artery Entrapment Syndrome (PAES)

Based on the MRI findings and the negative results from

other tests, a diagnosis of Popliteal Artery Entrapment

Syndrome (PAES) was made.

The MRI revealed abnormal thickening of the popliteal

artery wall at a length of 2.3 cm. In the same region, a

supernumerary ectopic slip of the medial gastrocnemius

muscle head was identified compressing the popliteal artery

both medially and laterally. These imaging findings

are consistent with PAES.

Popliteal artery entrapment syndrome (PAES) is a potential

cause of intermittent lower extremity pain in individuals

without atherosclerotic risk factors, particularly

affecting young active males. Although it might be asymptomatic

for some time, it might cause lower limb claudication,

sensation of coldness and numbness during physical

activity.

Unlike peripheral arterial disease, in which case pain

typically subsides with rest, the discomfort may persist

even after activity has ceased. Current understanding of

PAES pathogenesis suggests that repeated compression of

the popliteal artery results in physical damage to the vessel

wall, leading to luminal narrowing, aneurysm formation,

thrombosis, or distal embolization.

PAES is classified into two forms: anatomical PAES, as

in our case, and functional PAES [1, 2]. Anatomic PAES is

a rare congenital anomaly. Normally, the popliteal artery

and vein run in between the medial and lateral head of

gastrocnemius muscle. However, there may be an anatomy

variation of the popliteal artery and the gastrocnemius

muscle, or there may be an anomalous fibrous band

or the popliteus muscle, which can cause compression and

damage of the vessel.

Those variations can result in six forms of arterial compression.

Type I through V are anatomical in nature. Type

I involves the artery taking a medial route around the gastrocnemius

muscle, while in Type II, the medial head of

the gastrocnemius attaches laterally.

Type III is characterized by an accessory slip of the gastrocnemius

muscle and Type IV occurs when the artery

passes beneath the popliteus muscle or a fibrous band associated

with it.

Type V involves a combination of any of the previous

anatomical variations (Types I to IV) along with concurpart

b

rent compression of the popliteal vein. Our patient is

characterized as category III (there is a supernumerary

ectopic bundle of the medial head of the gastrocnemius).

Functional PAES (Type VI form), could also lead to arterial

occlusion, although there is no visible anatomical abnormality

in the popliteal fossa and it is due to anomalous hypertrophy

of the gastrocnemius muscle. Most studies indicate

a higher prevalence of PAES in male athletes, with

a mean age between 30 and 35 years, and it is often bilateral

[3, 4].

Long term arterial compression causes chronic vascular

wall microtrauma which may become irreversible

over time, resulting in localized premature arteriosclerosis,

thrombus formation, with distal ischemia, as well as

poststenotic ectasia or aneurysm formation.

Early diagnosis is crucial, as PAES is a progressing disorder,

and timely treatment can help prevent significant

complications [5].

If PAES must be ruled out, a positional stress test should

be conducted. This involves performing altering maneuvers

of dorsiflexion or plantar flexion of the forefoot while

monitoring the pedal pulse. In case of PAES, pedal pulse

often disappears [4]. Additionally, signs of reduced perfusion

may be present, including pallor, coldness of the peripheral

lower limb, and cyanosis in acute cases, though

sometimes there may be no observable findings.

Imaging diagnostic methods include angiography, computed

tomography angiography (CTA), Doppler ultrasound

and magnetic resonance imaging (MRI). While digital

subtraction angiography (DSA) is highly accurate, MRI

is generally preferred for evaluating the popliteal fossa

due to its non-invasive nature, lack of radiation exposure,

and superior soft-tissue differentiation over CT [5, 6 ].

Literature reports the use of repeated dorsiflexion and

plantar flexion of the foot, followed by maintaining plantar

flexion throughout the MRI to provoke symptoms in

case of no anatomic variation detection.

However, maintaining this position is often challenging

for patients, leading to motion and thus artifacts which

lowers the diagnostic value of the examination [7].

MRI, DSA and CTA are also useful in identifying bilateral

disease, as approximately two-thirds of patients with

symptoms have contralateral involvement. However,

there is still no definitive consensus on which diagnostic

method is the most effective [8].

80


VOLUME 10 | ISSUE 4

H R J

Figure 1. US, axial view. There are focal thickening and

an anomaly at the popliteal artery wall (arrow).

Figure 2. DSA of the popliteal fossa. There is an anomaly

and fuzziness of the lateral wall of the popliteal artery

(arrow).There is also occlusion of the proximal portion of

the tibial-fibular trunk (arrowheads).

Figure 3. Axial view, proton-density fat-suppression

weighted imaging. There is a supernumerary ectopic

bundle of the medial head of gastrocnemius (arrow)

in contact with the lateral wall of the popliteal artery

(arrowhead).

Figure 4. Sagittal view, T1 weighted

imaging. There is a supernumerary

ectopic bundle of the medial head of

gastrocnemius (arrow).

81

H R

J


VOLUME 10 | ISSUE 4

PAES should be differentially diagnosed from other entities

with similar symptoms. MRI is a valuable imaging

tool for distinguishing PAES from other conditions, ruling

out other causes of popliteal fossa pain, such as popliteal

cyst rupture, synovial cysts, and injuries to bones,

muscles, or ligaments.

Moreover, MRI can assess vessel wall, allowing the evaluation

of other vascular conditions, including vasculitis,

cystic adventitial disease of the popliteal artery, iliac artery

endofibrosis, iliac or femoral giant cell arteritis, collagen

vascular disease, popliteal artery aneurysm, as well

as chronic compartment syndrome and thromboembolism

[7].

Surgery for PAES is typically reserved for cases with

confirmed symptoms and a structural cause. The procedure

involves decompressing the popliteal artery being

entrapped by the muscle.

During surgery, revascularization is often achieved

through grafts, which generally have a high success rate,

with five-year patency rates above 90%. Literature indicates

that many patients experience significant symptom

relief and can resume normal physical activities within

three months following the surgery. If the condition is

diagnosed early and the popliteal artery is not severely

damaged, a simpler surgical approach such as fasciotomy,

myotomy, or fibrous band sectioning might be sufficient

to alleviate the problem [5, 6].

Our patient underwent surgical resection of the supernumerary

ectopic bundle of the medial head of the right

gastrocnemius muscle, along with bypass of the occluded

popliteal artery using a venous graft obtained from

the great saphenous vein. The procedure was successful,

and the patient remains asymptomatic postoperatively.

PAES should always be ruled out especially in patients

who lack atherosclerotic risk factors, particularly young

adults, presenting with lower extremity pain that persist

after rest or with findings of acute limb ischemia.

However, a medical history indicating presence of peripheral

vascular disease should not rule out PAES. MRI

play a critical role in the evaluation of the disease and

are often considered superior to other diagnostic modalities

by many experts. Prompt diagnosis is critical as if it

is left untreated, it can result in permanent vessel damage

[2, 5]. R

Conflict of Interest:

The authors declared no conflicts of interest.

Funding:


82


VOLUME 10 | ISSUE 4

H R J

References

1. Palareti G, Legnani C, Cosmi B, et al. Comparison between

different D-Dimer cutoff values to assess the individual

risk of recurrent venous thromboembolism:

Analysis of results obtained in the DULCIS study. Int

J Lab Hematol 2016; 38:42–49. doi:10.1111/ijlh.12426

2. Saa L, Firouzbakht PK, Otahbachi M. A case of overlooked

popliteal artery entrapment syndrome.

Cureus 2019; 11:e4252. doi:10.7759/cureus.4252.

3. Labmayr V, Aliabadi A, Tiesenhausen K, et al. Popliteal

artery entrapment syndrome (PAES) in a

17-year-old adolescent. Case Rep Vasc Med 2019;

2019:1–4. doi:10.1155/2019/8540631.

4. Kwon YJ, Kwon TW, Gwon JG, et al. Anatomical popliteal

artery entrapment syndrome. Ann Surg Treat

Res 2018; 94:262–269. doi:10.4174/astr.2018.94.5.262.

5. Hai Z, Guangrui S, Yuan Z, et al. CT angiography and

MRI in patients with popliteal artery entrapment

syndrome. Am J Roentgenol 2008; 191:1760–1766.

doi:10.2214/AJR.07.4012.

6. Carneiro Júnior FCF, Carrijo ENA, Araújo ST, et al.

Popliteal artery entrapment syndrome: A case report

and review of the literature. Am J Case Rep

2018; 19:29–34. doi:10.12659/AJCR.905170.

7. Cheng TJL, Thian YL, Sia SY, Hallinan JTPD. Clinics

in diagnostic imaging (187). Singapore Med J 2018;

59:339–344. doi:10.11622/smedj.2018071.

8. Williams AB. Bilateral popliteal artery entrapment

syndrome: An approach to diagnosis and

salvage. Case Rep Vasc Med 2020; 2020:1–3.

doi:10.1155/2020/2403280.

Ready - Made

Citation

Andrioti Petropoulou Nefeli, Papatheodorou Athanasios, Tsanis Antonios.

Popliteal Artery Pathology: An Uncommon Yet Critical Clinical Challenge,

Hell J Radiol 2025; 10(4): 78-83.

83

H R

J

A case of aberrant central venous catheter position on chest X-ray: Ectopic placement or benign finding?, p. 84-87

VOLUME 10 | ISSUE 4


Vascular Imaging

A case of aberrant central venous

catheter position on chest X-ray:

Ectopic placement or benign finding?

Belivanis Michail, Samaras Vaios, Roubanis Dimitrios

Department of Radiology, Laiko General Hospital, Athens, Greece


part a

A case of aberrant central venous catheter position

on chest X-ray: Ectopic placement or benign finding?

A 59 year old male patient with no significant medical

history was admitted to the nephrology department

with acute kidney injury. An uncuffed left jugular

central venous dialysis catheter was inserted at the

bed-side. The procedure was uneventful, with blood

aspirated from the catheter revealing low oxygen pressure,

consistent with venous blood. A chest x-ray was

performed, which demonstrated an aberrant catheter

course on the left mediastinal heart border (Fig.1). No

previous imaging was available and there was no history

of previous vascular interventions. A non contrast

chest CT scan was performed to clarify the catheter’s

position (Fig. 2, 3).

Figure 1. Chest x-ray.

Corresponding

Author,

Guarantor

Belivanis Michail ,MD, Radiology Resident

E-mail: mpelmike@yahoo.gr

Department of Radiology, Laiko General Hospital

84


VOLUME 10 | ISSUE 4

H R J

Figure 2. Non contrast chest CT.

Figure 3. Non contrast chest CT.

part b

Diagnosis

Duplication of the superior vena cava (SVC)

Non-contrast CT of the chest demonstrated the catheter’s

correct insertion in the left internal jugular vein.

Throughout its course it remained in a vascular structure

arising from the confluence of the left internal jugular

and left subclavian vein, coursing laterally to the

aortic arch, anteriorly to the left hilum and terminating

at the anatomical site of the coronary sinus. This vessel

was identified as a persistent left superior vena cava

(PLSVC) (Fig. 2, 3). The presence of a hypoplastic bridging

vein between the PLSVC and the SVC, identified as a

left brachiocephalic vein, was also noted (Fig. 2).

Superior vena cava duplication (SVCD) is an anatomical

variant of the chest venous anatomy occurring in

approximately 0,3% of the general population. In patients

with congenital heart disease the rate is significantly

higher, between 10-11%. This anatomical variant

occurs due to the persistence of the left anterior cardinal

vein during embryonic development, which normally

regresses. In cases of duplication, there is typically

a right SVC that drains into the right atrium and a left

SVC that drains into the coronary sinus and right atrium

(~90%) or, less commonly, directly into the left atrium

(~8-10%) [1].

Various modalities can be utilised for evaluation: Echocardiography,

which is both cheap and widely available

and may detect the condition perinatally, being however

operator dependent and often difficult to interpret;

Computed Tomography with IV contrast, which offers

the best spatial resolution, multiplanar imaging and

reformatting, with the drawbacks of ionizing radiation

and possible contrast allergy and nephrotoxicity; Magnetic

Resonance Imaging, which doesn’t have the aforementioned

cons of a CT scan but isn’t as widely available

and Angiography, which is the gold standard but due to

its invasive nature, radiation and iodinated contrast burden

isn’t routinely used for this specific condition’s diagnosis

but may detect it incidentally [2].

The differential diagnosis for a suspected PLSVC includes

both vascular and non-vascular structures. Vascular

structures include the vertical vein, levoatriocardinal

vein, left superior intercostal vein, aberrant left

brachiocephalic vein, pericardiophrenic vein, and vascular

structures secondary to surgery.

The distinction between them requires the identification

of the vessel’s origin and drainage site and the

course between them according to mediastinal structures,

the expected direction of blood flow and the presence

of concomitant findings of other cardiac or non-car-

85

H R

J


VOLUME 10 | ISSUE 4

Figure 1. Chest x-ray demonstrating an aberrant central

venous catheter course.

Figure 3. Coronal plane reformatting of the chest CT

demonstrating the position of the catheter’s tip.

diac diseases. Additionally, masses such as enlarged

lymph nodes and neurofibromas arising from the phrenic

nerve may mimic a PSLVC [2].

Although patients are usually asymptomatic in the

coronary sinus and right atrium draining variant, there

is an association between PLSVC and supraventricular

arrhythmias, as well as an increase in the difficulty of interventional

procedures such as cardiovascular electronic

device implantation [3].

Figure 2. Chest CT demonstrating a persistent left

superior vena cava with the catheter within (arrow) and a

hypoplastic left brachiocephalic vein (arrowhead).

Additionally, arrhythmias, cardiogenic shock, cardiac

tamponade, and coronary sinus thrombosis can develop

due to catheterization [4], highlighting the importance

of knowing of the variant’s presence before any

interventions.

There are cases of successful hemodialysis through a

catheter inserted in a PLSVC, as long as echocardiography

confirms right atrial drainage, CT scan reveals patency

of the left brachiocephalic vein, ECG reveals no

arrhythmia and aspirated blood gas analysis confirms

venous blood [5].

A repeat, contrast enhanced chest CT was performed

that confirmed non-patency of the hypoplastic left brachiocephalic

vein (not shown). Thus, we recommended

that the catheter be removed to avoid venous congestion

of the left upper limb and neck in case of thrombosis of

the PLSVC. After removal, a new catheter was inserted

on a different site and the patient underwent hemodialysis

successfully. No immediate complications related to

the first catheter’s short stay were observed.

This case demonstrates the significance of being aware

of the presence of seemingly benign anatomical variations

and their associated complications, since even

routine interventions may disrupt physiological mechanisms

and cause unforeseen deleterious effects.

When inserting a central venous catheter, reviewing

any prior imaging is essential in selecting the most appropriate

target vessel, and when no imaging is available,

as in our case, investigating and revising as necessary

is vital. R

86


VOLUME 10 | ISSUE 4

H R J

Conflict of Interest:

The authors declared no conflicts of interest.

Funding:


References

1. Albay S, Cankal F, Kocabiyik N, et al. Double superior

vena cava. Morphologie. 2006;90(288):39-42.

doi:10.1016/s1286-0115(06)74317-x

2. Azizova A, Onder O, Arslan S, et al. Persistent left superior

vena cava: clinical importance and differential

diagnoses. Insights Imaging. 2020;11(1):110. Published

2020 Oct 15. doi:10.1186/s13244-020-00906-2

3. Shafi I, Hassan AAI, Akers KG, et al. Clinical and procedural

implications of congenital vena cava anomalies

in adults: A systematic review. Int J Cardiol.

2020;315:29-35. doi:10.1016/j.ijcard.2020.05.017

4. Roessler G, Zoeller K, Clark N. Duplicate superior

vena cava: An unexpected finding. Ann Vasc Surg

Brief Rep Innov. 2024;4(3):100316.doi:10.1016/j.

avsurg.2024.100316

5. Kute VB, Vanikar AV, Gumber MR, et al. Hemodialysis

through persistent left superior vena cava. Indian J Crit

Care Med. 2011;15(1):40-42. doi:10.4103/0972-5229.78223

Key words

persistent left superior vena cava, congenital anomaly

Ready - Made

Citation

Belivanis Michail, Samaras Vaios, Roubanis Dimitrios.

A case of aberrant central venous catheter position on chest X-ray:

Ectopic placement or benign finding?, Hell J Radiol 2025; 10(4): 84-87.

87

Guidelines for Authors

1. Scope


(“HjR”) is the official journal of the

Hellenic Radiological Society, first

published in 1968. This revived edition

of HjR, published in English, aspires

to promote scientific knowledge

in Radiology and allied sciences

both at diagnosis and image guided

therapy, worldwide. It is a peer-reviewed

Journal, aiming at raising the

profile of current evidence-based

imaging practice and at improving

the scientific multidisciplinary dialogue.

HjR presents clinically pertinent,

original research and timely

review articles. It is open to International

authors and readers and offers

a compact forum of communication

to medical imaging and related

science specialists.

2. Language

British English is the official language

of the journal. All submitted

manuscripts should be written in

British English.

3. How to submit a paper

All submissions for peer-review

should be performed online through

the journal website www.hjradiology.org.

The Editor-in-Chief and the Associate

Editors will perform the initial

assessment of the manuscript and

if the manuscript is suitable for the

journal and the submission is complete,

it will be sent to the relative

reviewers by the appropriate section

Editor. The reviewing process

that is followed is double blinded.

During on-line submission, authors

can enter the name/s of non-preferred

reviewers. The time allocated

for reviewers to assess the manuscript

and submit their recommendation

is three weeks. The Editor-in-chief

makes the final decision

for publication.

The Editorial office will communicate

the reviewer’s comments and

the decision to the authors.

4. Manuscript originality and copyright

The submitted manuscript should be

original, should not contain previously

published material and should

not be under consideration for publication

in another journal. Previous

publication of an abstract as an

oral presentation or scientific exhibit,

either in print or electronically,

does not preclude submission of an

original article for publication. Any

potential overlap with prior publications

should be clearly stated by

theauthors to avoid redundant publications.

The submission needs to

be approved by all co-authors and,

in case of original research, a ‘guarantor’

of the study is required. As

‘guarantor’ may be considered a

senior author that is deemed to take

overall responsibility for all aspects

of the study (ethics, originality, consent,

data handling, and all aspects

of Good Medical Practice). The ‘guarantor’

of the study does not necessarily

need to be the corresponding

author. The HjR will not hold legal

responsibility should there be any

claim for compensation. All authors

need to sign the copyright transfer

form (http://hjradiology.org/docs/

Copyright Transfer Form.doc).

5. Ethical issues

Authorship should be based on the

criteria defined by the International

Committee of Medical Journal

Editors recommendations (http://

www.icmje.org/icmje-recommendations.pdf).

Complaints and appeals

The Editorial Board (EB) will discuss

in detail all the complaints and appeals

on the decision of rejection of

a submitted manuscript or any concerns

or allegations regarding misconduct,

and will decide accordingly.

An independent panel of international

experts, including renowned

journal editors and directors of academic

departments, may be consulted

if these issues arise.

Conflict of interest - competing

interests

It is the authors’ responsibility to

fully disclose in the cover letter any

financial interests (shareholding or

employment by industry, etc.) relative

to the work under consideration.

Any conflict should be stated

in the manuscript before the Reference

section. Authors should acknowledge

any source of funding

provided for the submitted subject

matter (i.e. grants, financial support

for presentations at meetings, etc).

For clinical trials, the registration

number, if available, should be included.

Advertisements will be kept

out of the main text body of the journal

and it is the Editor’s responsibility

that the editorial decisions will

not be influenced by advertisers and

sponsors. Special issues follow the

routine editorial process and are by

no means influenced by advertisers.

The EB retains the right to refuse

any advertisement for any reason.

Data sharing and communication

The EB may permit the use of figures

or text for scientific reasons. Audits

of rejection rates, review of completion

time and other data, such as the

impact factor of the journal, will be

regularly published. Premature publication

in the mass or social media

is not advised until the article has

appeared electronically in public.

Patient data

It is the authors’ responsibility to

protect patient anonymity. All identifying

data (name, identification

numbers, initials) must be removed

from text, images and tables. If it

is mandatory for a patient’s face to

be included in the manuscript, the

eyes should be sufficiently masked.

If there is a possibility that a patient

may be identified from a photograph

or relevant legend and text, the patient’s

written consent should be

submitted.

Research ethics and compliance

The HjR follows the guidelines of the

International Committee of Medical

Journal Editors (www.icmje.org). It

is the authors’ responsibility to confirm

that any experimental investigation

on human subjects report-

88

H R J

ed in a submitted manuscript has

been performed with the subjects’

informed consent and following the

approval of the appropriate institution

review board. For prospective

clinical studies with human subjects,

the authors are required to

confirm that both an appropriate

institution review board approval

and informed consent have been

obtained. For retrospective clinical

studies, the authors are required

to confirm than an institution review

board waiver of informed consent

has been obtained. For clinical

studies involving human subjects

with no access to ethics review committees,

the authors should confirm

that they followed the principles

outlined in the Helsinki Declaration

(World Medical Association

Declaration of Helsinki: ethical

principles for medical research involving

human subjects. JAMA 2013;

310(20): 2191-2194). Authors may be

asked to document the above at any

time point.

For studies involving animals, the

authors should indicate whether the

procedures followed the local regulatory

principles on animal experimentation.

In this case, the following

statement needs to be added in

the text: “All applicable international,

national, and/ or institutional

guidelines for the care and use of

animals were followed”.

Permissions and plagiarism

Figures and images should have

not been previously published. For

the use of any figures already published

elsewhere by one of the authors

or by means of another publication,

the authors are required to

obtain written permission from the

copyright owner(s) and to submit

the evidence in the submission process.

Disclosure should be made at

the time of submission, accordingly.

Plagiarism will not be accepted

in any case. Dedicated software will

be used on this purpose. All submissions

will be first screened by a similarity

detection software before

being assigned for peer review. If

the overall text similarity index is

>20%, or the duplication rate is >5%,

the manuscript is returned to the

authors without peer review. The

similarity report will be included

in the editor’s message. In cases of

plagiarism, citation manipulation,

data and results fabrication, or any

suspicion of research misconduct,

the EB will follow the guidelines of

the Committee on Publication Ethics

(https://publicationethics.org/

guidance/Guidelineshumansubjects).

The EB reserves the right of

rejecting submissions if there is

any doubt about whether the procedures

described above have been

followed accurately.

Options for publication,

post-publication discussions and

corrections

All manuscripts are subjected to

blind peer review after being initially

assessed for quality by the section

editors. The section editors will first

evaluate the possibility of duplicate

publications. Reviewers are selected

for their known expertise, objectivity

and proven commitment and

professionalism.

Reviewers should handle the material

confidentially and are not allowed

to share or copy. In case of

major disagreement between two

reviewers, the manuscript will either

be assigned to a third reviewer,

or the section editor will make

a decision. The decision on acceptance

or rejection is based solely on

the reviewers’ evaluation, the importance

of the manuscript and its

relevance to the aims of the journal.

The EB will decide upon any letters

to the editor commenting on prior

publications. Corrections on prior

publications related to authors’

names, affiliations, major flaws or

any kind of errors will also be discussed

by the EB and prompt changes

will be published in a subsequent

issue. The changes will be listed in

the table of contents to ensure that

they will be linked to the initial publication

to which they pertain in major

databases such as in PubMed.

6. Types of manuscript

The HjR accepts the following types

of articles:

• Original articles: The paper

needs to offer new knowledge on

diagnostic or interventional radiology.

The conclusions need to be

sound and supported by statistical

analysis.

When the accuracy of a diagnostic

test is assessed, following the

Standards for Reporting of Diagnostic

Accuracy (STARD) flow diagram

(http://www.stard-statement.

org) is suggested. A structured abstract

of 250 words, 3-5 keywords,

text upto 4,500 words, figures (up to

four figures or eight figure parts), a

maximum of six tables, a maximum

of fifty references and a maximum

of seven authors are required for

original articles.

Original articles on relative to Radiology

scientific fields, i.e. Medical

Physics, Nuclear Medicine, Radiotherapy,

Advanced image analysis/

AI and Biomedical Engineering, are

welcome for submission.

• Review Articles: The journal may

accept systematic reviews, meta

analyses and literature reviews of

a subject. An unstructured abstract

of 200 words, 3-5 keywords, text of

no more than 6,000 words, figures

(up to eight figures), a maximum

of six tables, a maximum of a hundred

references and a maximum of

three authors are required for review

articles.

• Pictorial Essays: The purpose

of pictorial essays is to provide a

teaching message through high

quality images. A brief text is required

to accompany figures. An

unstructured abstract of 200 words,

3-5 keywords, text of no more than

6,000 words, a maximum of fifteen

figures, a maximum of six tables, a

maximum of a hundred references

and a maximum of four authors are

required for pictorial essays.

• Letters to the editor: Communication

to the editor is welcome and

will be published if it offers pertinent

and/ or constructive comment

on articles published in the HjR. Let-

89

H R J

ters are published at the discretion

of the Editorial team and should be

received within three months after

on-line publication of an article.

Following acceptance, letters will

be sent to authors for response. Letter

communications should include

text of no more than 500 words, up

to two figures and ten references,

without any abstract or keywords

and a maximum of three authors.

• Clinical Case-Test Yourself:

The purpose of the “Clinical Case-

Test Yourself” manuscript is to offer

readers the chance to test their

skills in diagnosing rare disorders.

The submitted cases should contain

radiologic features that allow

a narrow differential or an accurate

diagnosis. The “Clinical Case-

Test Yourself” manuscripts are not

case reports with previously unreported

imaging findings. The format

for this manuscript consists of

two parts. Part “A” represents the

“Question” and consists of a title, a

brief clinical history (maximum 100

words) and up to five figure parts

with at least two showing pathology.

The figure legends should only

indicate the modality and should

not reveal the abnormal findings.

Part “B” represents the “Answer”

and consists of a line statement of

the diagnosis, and a discussion of up

to 1,000 words, with no more than

ten references. In Part “B” the same

figures as in Part “A” are repeated

with the addition of explanatory

legends and arrows. A maximum

of three authors is allowed.

• Commentary: Commentary articles

are based on a current hot topic

published by experts. They are published

following an invitation of the

Editorial team, should target a research

or review article and should

be received before the on-line publication

of the targeted article. Commentaries

should include a text of

no more than 800 words, up to five

references, without any abstract or

key words and a maximum of two

authors.

• Editorial: The Editorials are published

by a member of the EB upon a

special occasion or by an expert who

will address a topic of general interest

following an EB invitation. Editorials

should include a text of no

more than 1000 words, up to ten references

and up to one figure and/or

table-drawing.

7. Manuscript organisation

A manuscript must contain the following

parts for submission:

• Cover letter: Each manuscript

needs to be accompanied by a cover

letter signed by the corresponding

author on behalf of the rest of

the authors stating that the article

is not under consideration in another

journal. In case of article resubmission

a point-by-point answer to

the reviewers’ comments needs to

be submitted with the cover letter.

• Title page: It includes the title

of the manuscript, the names, affiliations

and e-mail addresses of

all authors and the affiliation, address,

e-mail address, telephone

and fax number of the corresponding

author.

The name and affiliation of the

‘guarantor’ of the study needs to be

included in the title page for original

articles.

• Blinded manuscript: Blinded title

page including only the title of

the manuscript with no affiliation.

• Abstract: An abstract presenting

the most important results and

conclusions is required for all papers

except for Letters to the Editor.

For Original Articles the abstract

needs to be structured as follows:

Purpose, Material and Methods,

Results, Conclusions.

For Reviews and Pictorial Essays, a

1-paragraph unstructured abstract

is required.

• Keywords: Below the abstract,

3 to 5 keywords are required. Keywords

need to be selected from the

Medical Subject Headings (MeSH)

database of the National Library of

Medicine.

• Text structure: the text of the

Original Articles needs to be organised

as follows: Introduction, Material

and Methods, Results and Discussion.

Review Articles and Pictorial

Essays require Introduction and

Discussion sections only.

• Fonts: The suggested font is double

spaced Times New Roman (12

pt). The text should display page

and line numbers throughout its

length.

• Abbreviations: Abbreviations

should be used as minimum as possible.

When used, they should be

defined the first time they are used,

followed by the acronym or abbreviation

in parenthesis.

• Measurement Units: All measurements

should be mentioned in

international units (SI). The full

stop should be used as a decimal

(i.e. 3.5 cm). Spaces should be added

around the plus/minus symbol (i.e.

13.6 ± 1.2). There should not be any

spaces around range indicators (i.e.

15-20) or equality/inequality symbols

(i.e. r=0.37, p<0.005).

• Acknowledgements, sponsorships

and grants: Acknowledgements

need to be placed at the end

of the manuscript before ‘References’

section. Any grant received or

sponsorship from pharmaceutical

companies, biomedical device manufacturers

or other corporations

whose products or services have

been used needs to be included in

the Conflicts of Interest Form and

also mentioned in the acknowledgements

section. If not, the phrase

“This project did not receive any

specific funding.” should appear at

the end of the text.

• Ethical approval: The name of

the Ethics Committee that approved

the study should be mentioned at

the end of the text. Depending on

the type of study (experimental investigation

on human subjects, prospective

or retrospective clinical

study with human subjects, clinical

study involving human subjects

with no access to ethics review committee,

study involving animals) appropriate

institution review board

approval or waiver, informed consent,

accordance of the Helsinki

Declaration principles or local regulatory

principles on animal experi-

90

mentation, should appear at the end

of the text. Specific details are outlined

in the section on Research ethics

and compliance.

8. Figures and Tables

All figures and tables need to be cited

in text consecutively in the order

in which they appear in text

into brackets and in Arabic numbers:

i.e. (Fig. 1) and (Table 1). Figure

parts need to be identified with

lower case letters, i.e (Fig. 1a).

Figures need to be of high quality.

Vector graphics, scanned line

drawings and line drawings need

to be in bitmap format and should

have a minimum resolution of 1,200

dpi. Halftones (photographs, drawings

or paintings) need to be in TIFF

or JPEG format, up to 174 mm wide,

up to 234 mm high and in minimum

resolution of 300 dpi.

A figure caption and a table caption

need to be added in the figure

and table section respectively for

each figure and table. Explanatory

signs (arrows, asterisks etc) should

be used when imaging findings are

not obvious. These should be white,

black or in shades of grey and proportionate

in size compared to the

size of the image. Please refrain

from using coloured signs.

Tables should appear at the end

of the main document, numbered in

Arabic numerals, each on a different

page. Each table should have a title

describing its content.

Abbreviations appearing in the

table need to be explained in a footnote.

All table columns must have a

subhead that describes the type of

data included in the column.

9. References

The accuracy of references is the responsibility

of the authors. The EB

suggests to the authors to be accurate

regarding citations and check

meticulously the correct primary

source.

References need to be cited in the

text in the order in which they appear.

The numbering needs to be in

Arabic numbers and placed in the

respective areas of text into square

brackets i.e [1].

References that have not been

published at the point of submission

need to cited with the respective

DOI (digital object identifier) number

given for on-line first articles.

All authors (surnames and initials

of first name) should be listed

when they are three or fewer. If authors

are more than three, the first

three authors should be listed, then

‘et al.’ needs to follow the name of

the third author.

When a book chapter is cited,

the authors and title of the chapter,

editors, book title, edition, city

and country, publisher, year and

specific chapter pages should be

mentioned.

For Online Document, the following

should be mentioned: authors

(if any), title of page, name of institution

or owner of Web site; URL;

dates of publication, update, and

access.

Reference examples:

Journal article:

Krokidis M, Hatzidakis A. Percutaneous

minimally invasive treatment

of malignant biliary strictures: current

status. Cardiovasc Intervent

Radiol 2014; 37(2): 316-323.

or

Krokidis M, Hatzidakis A. Percutaneous

minimally invasive treatment

of malignant biliary strictures: current

status. Cardiovasc Intervent

Radiol 2014; doi: 10.1007/s00270-

013-0693-0. Epub 2013 Jul 13.

Book chapters:

Allen G, Wilson D. Current role for

Ultrasonography. In: Karantanas A

(ed). Sports Injuries in children and

adolecents (Medical Radiology, Diagnostic

Imaging). Springer, Berlin

Heidelberg New York 2011, pp 83-97.

Online document:

National Institute for Health and

Care Excellence. SIR-Spheres for

treating inoperable hepatocellular

carcinoma. Available via nice.org.

uk/guidance/mib63. Published May

10, 2013. Updated October 2, 2013.

Accessed January 25, 2014.

10. Review of manuscripts

Revised manuscripts should be resubmitted

according to the Editor’s

H R J

letter. For accepted manuscripts,

authors need to make proof corrections

within 72 hours upon pdf

supplied, check the integrity of the

text, accept any grammar or spelling

changes and check if all the Tables

and Figures are included and

properly numbered. Once the publication

is online, no further changes

can be made. Further changes

can only be published in form of

Erratum.

11. Submission Preparation Checklist

As part of the submission process,

authors are required to check off

their submission’s compliance with

all of the following items, and submissions

may be returned to authors

that do not adhere to these

guidelines:

•The submission has not been previously

published, nor is it before

another journal for consideration

(or an explanation has been provided

in Comments to the Editor).

•The submission file is in OpenOffice,

Microsoft Word, RTF, or Word-

Perfect document file format.

•Where available, URLs for the

references have been provided.

The text is double spaced; uses a

12-point Times New Roman font;

employs italics, rather than underlining

(except with URL addresses).

All illustrations and figures should

be submitted separately as additional

files.

• Tables should appear at the end

of the main document.

•The text adheres to the stylistic

and bibliographic requirements

outlined in the Author Guidelines.

•If submitting to a peer-reviewed

section of the journal, the instructions

in “Ensuring a Blind Review”

have been followed.

•All authors have sufficiently

participated and read the submitted

material and fully agree to its

content.

12. Article processing charges

All articles are processed and published,

if accepted, free of charge.

There are no article processing

charges.

91

24 O

ΕΛΛΗΝΙΚΗ

ΑΚΤΙΝΟΛΟΓΙΚΗ

ΕΤΑΙΡΕΙΑ

ΠΑΝΕΛΛΉΝΙΟ

ΑΚΤΙΝΟΛΟΓΙΚΟ

ΣΥΝΕΔΡΙΟ

Χτίζοντας γέφυρες

με το Μέλλον

11 –14

ΔΕΚΕΜΒΡΊΟΥ

2025

radiologycongress.gr

Ωδείο

Αθηνών

ΑΘΉΝΑ

Κορυφαία συστήματα απεικόνισης και διάγνωσης

Καινοτόμες τεχνολογίες - Τεχνητή Νοημοσύνη

Ευέλικτα προγράμματα χρηματοδότησης

ΑΘΗΝΑ Εθν. Αντιστάσεως 93, 154 51, Νέο Ψυχικό +30 210 67 90 000

ΘΕΣΣΑΛΟΝΙΚΗ Πλ. Σιντριβανίου 4, 546 21 +30 2310 233 251

info@papapostolou.gr www.papapostolou.gr

ΠΑΤΡΑ ΙΩΑΝΝΙΝΑ ΗΡΑΚΛΕΙΟ ΧΑΝΙΑ ΛΑΡΙΣΑ ΠΤΟΛΕΜΑΪΔΑ

T10 Iss4 Hjr Radiology

Transform your PDFs into Flipbooks and boost your revenue!

Delete template?

Save as template ?