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Abstract #1045

Low Rank plus Sparse Decomposition of ODF Distributions for Improved Detection of Group Differences in Diffusion Spectrum Imaging

Steven H. Baete1,2, Jingyun Chen1,2,3, Ricardo Otazo1,2, and Fernando E. Boada1,2

1Center for Advanced Imaging Innovation and Research (CAI2R), NYU School of Medicine, New York, NY, United States, 2Center for Biomedical Imaging, Dept of Radiology, NYU School of Medicine, New York, NY, United States, 3Steven and Alexandra Cohen Veterans Center for Posttraumatic Stress and Traumatic Brain Injury, Dept of Psychiatry, NYU School of Medicine, New York, NY, United States

Recent advances in data acquisition make it possible to use Diffusion Spectrum Imaging (DSI) as a clinical tool for in vivo study of white matter architecture. The dimensionality of DSI data sets requires a more robust methodology for their statistical analyses than currently available. Here we propose a combination of Low-Rank plus Sparse (L+S) matrix decomposition and Principal Component Analysis to reliably detect voxelwise group differences in the Orientation Distribution Function that are robust against the effects of noise and outliers. We demonstrate the performance of this approach using simulations to assess group differences between known ODF distributions.

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