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Abstract #0327

Field-corrected imaging for sparsely-sampled fMRI by exploiting low-rank spatiotemporal structure

Hien Nguyen 1 and Gary Glover 2

1 Department of Electronics & Computer Engineering, Hanoi University of Science & Technology, Hanoi, Vietnam, 2 Department of Radiology, Stanford University, California, United States

Magnetic field gradients near air-tissue interfaces cause signal dropout, hampering BOLD fMRI. To make the data less prone to T2* susceptibility artifacts, it is desirable to reduce the readout duration. This can be achieved by undersampling k-space, which has been investigated for dynamic MRI and recently proposed for fMRI. In this work, we demonstrate a new field-corrected imaging approach to sparsely sampled fMRI, coined functional LOw Rank Approximations (fLORA). Specifically, we exploit partial separability (PS)-induced low rank structure of fMRI data via group-sparse regularization, combined with magnetic field inhomogeneity compensation.

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