Low Rank plus Sparse Decomposition of ODF Distributions: Whole brain Statistical Analysis of Higher Order Diffusion Datasets
Steven H. Baete1,2, Ying-Chia Lin1,2, 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
Recent advances in data acquisition make it possible to use high quality diffusion data for routine in vivo study of white matter architecture. The dimensionality of these data sets requires a more robust methodology for their statistical analyses than currently available. Here we propose a apply Low-Rank plus Sparse (L+S) matrix decomposition 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 to replicate the established negative association between global white matter integrity and physical obesity in the Human Connectome dataset.
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