TRADITIONAL POSTER - ismrm

Poster Sessions
3114. Simplified Signal Equations for Spoiled Gradient Echo MRI
James Grant Pipe 1 , Ryan K. Robison 1
1 Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, United States
This work presents simplified signal equations for spoiled gradient echo (SPGR) imaging. The framework introduces an exponential time constant TA,
which reflects magnetization loss from the rf pulse. This framework is then used for to consider image SNR and T1 contrast.
Image Segmentation Methods
Hall B Tuesday 13:30-15:30
3115. Advanced Images Algebra (ADIMA): A Novel Method for Lesion Heterogeneity Enhancement in
Multiple Sclerosis
Marios C. Yiannakas 1 , Daniel J. Tozer 1 , Klaus Schmierer 1 , Declan T. Chard 1 , Valerie M. Anderson 1 , David
H. Miller 1 , Claudia A.M Wheeler-Kingshott 1
1 UCL - Institute of Neurology, London, United Kingdom
Multiple Sclerosis lesions are known to be pathologically heterogeneous but this is not well depicted on conventional MRI. In this work a new MR analysis
method is presented which utilises conventional FSE dual echo data sets with the use of advanced images algebra (ADIMA). The method is an extension to a
previously described technique and involves image subtraction and normalisation in order to enhance the dynamic range in the image with a consequent
enhancement of lesional heterogeneity in MS lesions. The method is shown to permit classification of T2 hyper-intense lesions into “bright” and “dark”
regions in a reproducible way.
3116. Normalised Double Inversion Recovery for Quantification of Cerebral Tissue Proportional Density
Sha Zhao 1 , Simon J. P. Meara 2 , Geoff J. M. Parker 1,3
1 ISBE, The University of Manchester, Manchester, England, United Kingdom; 2 Physics Department, Clatterbridge Centre for
Oncology, Merseyside, England, United Kingdom; 3 Biomedical Imaging Institute, The University of Manchester, Manchester,
England, United Kingdom
We propose a method for obtaining 3D proportional density maps for each major brain tissue component. Using double inversion recovery (DIR), we
acquire images of grey matter, white matter, CSF and proton density. Each DIR image is divided by proton density, then an optimised correction factor is
calculated for each so that the sum of these ratio images is unity for all voxels, thereby correcting for inter-tissue relaxation time, and coil sensitivity
confounds. These proportional density images are potentially useful for studies of brain morphology and atrophy, as demonstrated in a cohort of healthy
volunteers of different ages.
3117. Method for Constructing Rapid Prototyping from MR Data
Cristobal Arrieta 1,2 , Sergio Uribe, 2,3 , Carlos Sing-Long 1,2 , Jorge Ramos 4 , Alex Vargas 5 , Pablo
Irarrazaval 1,2 , Cristian Tejos 1,2
1 Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile; 2 Biomedical Imaging Center,
Pontificia Universidad Catolica de Chile, Santiago, Chile; 3 Department of Radiology, Pontificia Universidad Catolica de Chile,
Santiago, Chile; 4 Department of Mechanical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile; 5 Department of
Surgery, Pontificia Universidad Catolica de Chile, Santiago, Chile
Rapid Prototyping (RP) allows building realistic replicas of biological structures. The building process consists of acquiring imaging data, segmenting the
structures of interest, triangulating the segmentation and printing. When RPs of soft tissues are built, segmentation becomes an important issue because of
the low contrast between structures. Therefore, threshold, region-growing or edge-detection based segmentation tend to fail, making this process extremely
tedious as important human assistance is required. We propose the use of an implicit Active Contour technique to facilitate the segmentation process. We
evaluated our method by constructing an RP of a pathological heart scanned with a standard CMR.
3118. Segmentation of the Rat Hippocampal Mossy Fiber Network from MEMRI Under Inhomogenous B1
Field
Way Cherng Chen 1 , Kai-Hsiang Chuang 1
1 Singapore Bioimaging Consortium, A*STAR, Singapore, Singapore
A method of segmenting the rat hippocampal mossy fiber network from MEMRI under inhomogeneous B1 field was introduced. High-pass filtering was
used to correct the intensity inhomogeneity, followed by multi-level Otsu thresholding and removal of clusters with 10 or less lpixels to obtain final
segmented image. High-pass filtering corrected for intensity inhomogeneity and enhanced the edges of the network making it preferable to N3 correction.
Comparison with manual segmentation on 5 data sets yielded a t-value of 0.390>0.05 and a true positive rate of 91.8%.
3119. Automatic Segmentation of MR Images for Long-Bone Cross-Sectional Image Analysis
Shing Chun Benny Lam 1 , Hamidreza Salilgheh Rad 1 , Jeremy Magland 1 , Felix W. Wehrli 1
1 Radiology, University of Pennsylvania, Philadelphia, PA, United States
A software program has been developed to automatically segment cortical bone region from cross-sectional MR image of long bones such as the tibial shaft
and extract geometric and parametric information from the segmented region. Our results show that the parameters obtained from the automatic
segmentation software are in good agreement with those obtained from manual segmentation. With these parameters, the mechanical properties of the
cortical bone can be quantified and analyzed over subject groups at different stages.