TRADITIONAL POSTER - ismrm

Poster Sessions
3126. Development of Partial Volume Segmentation of Brain Tissue Based on Diffusion Tensor Imaging
(DTI)
Seiji Kumazawa 1 , Takashi Yoshiura 2 , Hiroshi Honda 2 , Fukai Toyofuku 1 , Yoshiharu Higashida 1
1 Department of Health Sciences, Kyushu University, Fukuoka, Japan; 2 Department of Clinical Radiology, Kyushu University,
Fukuoka, Japan
To study the cortical/subcortical diffusivity in neurological diseases, brain tissue segmentation methods based on DTI data have been proposed. However, a
partial volume effect might complicate the segmentation. We present a brain tissue segmentation method based on DTI data. The features of our method
include the conducting of the segmentation in DTI space without any registration, and the estimation of the partial volume fractions of each tissue type
within a voxel using a maximum a posteriori probability principle. The results of the digital phantom experiment and human DTI data demonstrate that our
method was able to perform a reasonable segmentation for brain tissue on DTI data.
3127. Characterization of Local Field Disturbances Through Phase Derivative Mapping
Hendrik de Leeuw 1 , Mandy Conijn 1 , Peter R. Seevinck 1 , Jeroen Hendrikse 2 , Gerrit H. van de Maat 1 , Chris
J.G. Bakker 1
1 Image Sciences Institute, Utrecht, Netherlands; 2 Radiology, University Medical Center Utrecht, Utrecht, Netherlands
In MRI studies, magnitude images are often used as the only source of information. Especially in the presence of local field distortions, this might be
considered suboptimal, since information on the local magnetic field is encoded in the signal phase. Studies that use signal dephasing only, do not allow
discrimination between paramagnetic and diamagnetic disturbances, since signal dephasing is independent of the sign of the field. We will show, by analysis
of microbleeds and calcifications in the brain, that by using the phase derivative, local field disturbances can be detected and analyzed in terms of positive or
negative susceptibility deviations.
3128. Detection of Abnormal Human Brain Structure from MRI Using Symmetry Features
Chi-Hsuan Tsou 1 , Tun Jao 1,2 , Jiann-Shing Jeng 3 , Jyh-Horng Chen, 1,4
1 Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan; 2 Department of
Neurology, National Taiwan University Hospital, Taipei, Taiwan; 3 Stroke Center and Department of Neurology, National Taiwan
University Hospital, Taipei, Taiwan; 4 Interdisciplinary MRI/MRS Lab, Department of Electrical Engineering, National Taiwan
University, Taipei, Taiwan
Brain magnetic resonance images (MRI) is crucial in modern medical diagnoses. However, there is usually a time delay between images acquisition and
interpretation of radiologists and/or doctors who prescribe the images, which may contribute to clinical exacerbation of the patients. In this preliminary
study, we use symmetry index to discriminate between normal brain structures and intracranial pathologies, and to provide a foundation for images autoalarm
system in the future. Experimental results of the proposed algorithm on 24 MR images (11 pathological, 13 healthy), show that the symmetric index
can help differentiate the normal and abnormal brain structures with promising performance.
3129. Automatic Detection of the Anterior and Posterior Commissures from T1-Weighted Images
Islem Rekik 1,2 , Linda Marrakchi-Kacem 1,3 , Jean-François Mangin 1,3 , Denis Le Bihan 1,3 , Cyril Poupon 1,3 ,
Fabrice Poupon 1,3
1 NeuroSpin, CEA, Saclay, France; 2 ESIEE, Noisy-le-Grand, France; 3 IFR49, Paris, France
Frame-based interventional MRI and multi-subject image analysis often rely on the manual selection of the Anterior Commissure (AC) and the Posterior
Commissure (PC) that are used to define the standard referential of Talairach. We developed a fast and fully automatic identification of the AC and PC
points from T1-weighted MR images, thus leading to an automation of the image processing step during the neurosurgery planning.
3130. Objective Assessment of T2-Based Liver Lesion Classifiers
Christian Graff 1 , Eric W. Clarkson 2 , Maria I. Altbach 2
1 Division of Imaging and Applied Math/OSEL/CDRH, U. S. Food and Drug Administration, Silver Spring, MD, United States;
2 Department of Radiology, University of Arizona, Tucson, AZ, United States
Classification of lesions as benign or malignant is an important imaging task. In liver, transverse relaxation time (T2) can be used as a classifier. Recently a
radial fast spin-echo technique has been developed to obtain T2 estimates within a single breath-hold during which under-sampled radial k-space lines are
acquired. The degree of under-sampling in this technique motivated the development of various post-processing techniques that attempt to enforce prior
information to compensate for data under-sampling. In this work we evaluate these proposed algorithms through the use of a receiver-operatingcharacteristic
(ROC) based metric which directly measures the classification performance of each algorithm.
3131. Bladder Wall Extraction and Mapping for MR Cystography
Jerome Zhengrong Liang 1,2 , Chaijie Duan 1 , Xianfeng Gu 2 , Mark E. Wagshul 1 , Hongbin Zhu 1 , Yi Fan 1 ,
Hongbing Lu 3
1 Radiology, Stony Brook University, Stony Brook, NY, United States; 2 Computer Science, Stony Brook University, Stony Brook, NY,
United States; 3 Biomedical Engineering, Fourth Military Medical University, Xian, China
MRI-based virtual cystoscopy, MR cystography, T1-weighted imaging, bladder cancer, tumor recurrence, image segmentation, conformal mapping, 3-D to
2-D flattening