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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