ELECTRONIC POSTER - ismrm

Wednesday 13:30-15:30 Computer 10
13:30 3316. An Open-Source Platform for Routine Clinical 1H Magnetic Resonance
Spectroscopy Processing
Frederick Shic 1 , Alexander P. Lin 2 , J. Bob Brown 3 , Stefan Bluml 4 , Brian D. Ross 5
1 Yale Child Study Center, Yale University School of Medicine, New Haven, CT, United States; 2 Radiology,
Brigham and Women's Hospital, Boston, MA, United States; 3 Alcor Consulting, Inc., Fremont, CA, United
States; 4 Radiology, Keck School of Medicine of USC, Los Angeles, CA, United States; 5 Huntington Medical
Research Institutes, Pasadena, CA, United States
1H MRS data analysis research traditionally emphasizes novel and powerful, but complex, methods for quantifying spectroscopic
data, creating barriers for less technical users. At the other extreme, many everyday users of MRS simply adopt manufacturer’s
standards for data processing, resulting in widespread incompatibilities in cross-institutional comparability. Here, we emphasize the
critical need for usability in MRS data processing and present an open-source platform which is intuitive, easy-to-use, yet complete,
flexible, and powerful. We show that in vitro and in vivo variability is low, and suggest that this platform may serve to provide
accessible, widespread, and consistent MRS data processing.
14:00 3317. Long-Term Reproducibility of MRS System
Agnieszka Polnik 1 , Magdalena Wicher 2 , Tomasz Banasik 2 , Aleksandra Kieltyka 2 , Marek
Konopka 3 , Maria Sokó³ 4
1 Department of Medical Physics, Maria Sk³odowska-Curie Memorial Cancer cenetr and Institute of Oncology,
Gliwice, Poland; 2 Helimed Diagnostic Imaging, Katowice, Poland; 3 Helimed Diagnosic Imaging, Katowice,
Poland; 4 Maria Sk³odowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice, Poland
The puropse of this work was to assess long-term variability of magnetic resonance spectroscopy in vivo using a standard brain
phantom. The measurements were performed from April 2006 to September 2009. Short and long echo time spectra were acquired
from the volume of interest located in the isocentre. The total number of spectra measured for short echo time was equal to 99, while
for long echo time – 96. LCModel software was used for estimation of the metabolite levels. Coefficients of variation did not exceed
the value of 6% for any metabolite over three years of the experiment.
14:30 3318. Simple Correction of Chemical Shift Changes in Quantitation
Andrii Lazariev 1 , Florence Fauvelle 2 , Martial Piotto 3,4 , Karim Elbayed 4 , Jacques
Namer 5 , Dirk van Ormondt 6 , Danielle Graveron-Demilly 1
1 Laboratoire Creatis-LRMN; CNRS UMR 5220; INSERM U630; INSA de Lyon, Université Claude Bernard
Lyon 1, Villeurbanne, France; 2 CRSSA/BCM, Grenoble, France; 3 Bruker BioSpin, Wissembourg, France;
4 Institut de Chimie, Strasbourg, France; 5 Department of Biophysics and Nuclear Medicine, University Hospitals
of Strasbourg, Strasbourg, France; 6 Delft University of Technology, Delft, Netherlands
High-resolution magic angle spinning (HRMAS) 1H spectroscopy is playing an increasingly important role for diagnosis. This
technique enables setting up metabolite profiles of ex vivo pathological and healthy tissue. Automatic quantitation of HRMAS signals
will provide reliable reference profiles to monitor diseases and pharmaceutical follow-up. Nevertheless, for several metabolites
chemical shifts often slightly differ according to the microenvironment in the tissue or cells, in particular with its pH. This hampers
accurate estimation of the metabolite concentrations mainly when using quantitation algorithms based on a metabolite basis-set. In this
work, a very simple method to circumvent this problem is proposed.
15:00 3319. Classification on Ex-Vivo MRS Signals of Glioma Samples
Bernd Merkel 1 , Frauke Nehen 2 , Yasemin Oezdemir 1 , Markus Thorsten Harz 1 , Dieter
Leibfritz 2 , Rudolf Fahlbusch 3 , Horst Karl Hahn 1
1 Fraunhofer MEVIS, Bremen, Germany; 2 Institute of Organic Chemistry, University of Bremen, Bremen,
Germany; 3 International Neuroscience Institute, Hannover, Germany
The goal of this work is the automated classification of glioma samples with high-resolution ex-vivo MR-spectroscopy. HR-MRS is a
sensitive method to detect metabolite changes in different tumor and tissue types. Altogether 47 biopsates of healthy, tumor margin
and tumor center tissue, measured on a 600 Mhz spectrometer, were analyzed. For further analysis, the lipophilic compounds were
omitted and only the hydrophilic ones were analyzed. By the application of ICA and further classification and feature reduction
techniques, we show that the tumor margin is distinctively different from the tumor center.