Abstract #2041

Thinking Outside the Voxel: A Joint Spatial-Angular Basis for Sparse Whole Brain HARDI Reconstruction

Evan Schwab¹, Rene Vidal², and Nicolas Charon³

¹Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States, ²Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States, ³Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, United States

Sparse modeling of dMRI signals has become of major interest for advanced protocols such as HARDI which require a large number of q-space measurements. With few exceptions, prior work have developed bases to sparsely represent q-space signals per voxel with additional constraints of spatial regularity. In this work, we propose a single basis to represent an entire HARDI dataset by modeling spatial and angular domains jointly, achieving an unprecedented level of sparsity. With a globally compressed representation we can then redefine HARDI processing, diffusion estimation, feature extraction and segmentation, and drastically reduce acquisition time and data storage.

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