Improving temporal resolution in fMRI using low-rank plus sparse matrix decomposition

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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