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Abstract #2063

Automated Segmentation of Multiple Sclerosis Brain Lesions at 7T

Blake Dewey 1 , Pascal Sati 1 , Snehashis Roy 2 , Luisa Vuolo 1,3 , Colin Shea 1 , Dzung Pham 2 , and Daniel S. Reich 1

1 National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda, Maryland, United States, 2 Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, United States, 3 Departments of Neurology and Radiology, University of Florence, Florence, Italy

Automatic lesion segmentation is required for analyzing large datasets provided by multicontrast 3D high-resolution imaging of multiple sclerosis. Although several methods have been proposed for MRI at clinical field strength (3T and below), 7T imaging remains uninvestigated due to more severe image bias (B1 field inhomogeneities, etc.) that can deeply impact the results of the existing segmentation algorithms. In this study, we propose an optimized multicontrast 3D high-resolution acquisition protocol combined with the use of advanced nonlinear bias correction and the LesionTOADS algorithm to create robust image segmentation and lesion load quantification at 7T.

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