Abstract Details
Activity Number:
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215
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Type:
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Invited
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Date/Time:
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Monday, August 5, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Imaging
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Abstract - #307198 |
Title:
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A Model for the Detection of Abnormal Regions in Quantitative Cerebral Maps with Application to Myelin Water Fraction Maps
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Author(s):
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Sandra Milena Hurtado RĂșa*+
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Companies:
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Weill Medical College of Cornell University
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Keywords:
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Bayesian statistics ;
Classification problems ;
MRI ;
T2-relaxometry ;
MCMC ;
Brain imaging
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Abstract:
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T2-relaxometry is an increasingly popular MRI technique that can separate the contribution of various tissue components in the brain, i.e; it can quantify myelin water fraction (MWF) maps. These maps have wide applicability in neurological disorders like multiple sclerosis (MS). Clinically, a group of experts classify lesions after a visual analysis of individual maps. Statistical models have to potential to classify lesions on white and gray matter brain regions in a multi-subject design. Statistical models that identify abnormal regions while account for the spatial distribution of the brain maybe beneficial for diagnostic and clinical care. This talk proposes a Bayesian hierarchical model for the analysis of quantitative cerebral maps. A generalized linear classifier is proposed accounting for spatial ROI variability on normalized brain maps (fixed number of ROI per image). A flexible structure is considered by including symmetric and asymmetric link functions. A simulation studies the model performance. The model has potential application in multiple sclerosis research.
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Authors who are presenting talks have a * after their name.
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