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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