Abstract #3691
Improving temporal resolution in fMRI using low-rank plus sparse matrix decomposition
Vimal Singh 1 , David Ress 2 , and Ahmed Tewfik 1
1
Electrical Engineering, University of Texas
at Austin, Austin, Texas, United States,
2
Baylor
College of Medicine, Houston, Texas, United States
High spatial resolution in fMRI generally improves its
sensitivity to brain activation signals by reducing
partial volume effects. However, the long acquisition
times required for high spatial resolution limit the
temporal resolution in fMRI studies. Consequently, the
low temporal sampling bandwidth leads to increase in
physiological noise and poor temporal modeling of the
functional activation dynamics. This paper presents an
under-sampled fMRI recovery using low-rank plus sparse
matrix decomposition signal model. The preliminary
results on in-vivo fMRI data show recovery of BOLD
activation in superior colliculus with contrast-to-noise
ratio > 4.4 (85% of reference) up to acceleration
factors of 3.
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