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Abstract #1026

A compressed sensing approach to super-resolution diffusion MRI from multiple low-resolution images

Lipeng Ning 1,2 , Kawin Setsompop 2,3 , Cornelius Eichner 3 , Oleg Michailovich 4 , Carl-Fredrik Westin 1,2 , and Yogesh Rathi 1,2

1 Brigham and Women's Hospital, Boston, MA, United States, 2 Harvard Medical School, Boston, MA, United States, 3 Massachusetts General Hospital, MA, United States, 4 University of Waterloo, Ontario, Canada

We present a novel compressed sensing approach for super resolution reconstruction (SRR) of diffusion MRI using multiple anisotropic low-resolution images. The diffusion signal in each voxel is estimated using spherical ridgelets while the spatial correlation between neighboring voxels is accounted for using total-variation (TV) regularization. The experimental result using in-vivo human brain data shows that the proposed SRR method is capable of recovering complex fiber orientations at a very high spatial resolution, similar to a physically acquired gold-standard data. Hence it has potential to be applied in clinical settings to study mental diseases and to reduce partial-volume effect.

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