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Abstract #1798

Comparison of strict sparsity and low-rank constraints for accelerated FMRI data reconstruction

Charles Guan1 and Mark Chiew2

1Electrical Engineering, Stanford University, Fremont, CA, United States, 2FMRIB Centre, University of Oxford, Oxford, United Kingdom

Functional MRI has been slow to benefit from data acceleration techniques based on non-linear image reconstruction. We present a comparison of two non-linear image reconstruction methods based on sparsity and low-rank models of FMRI data. k-t FOCUSS uses an asymptotic L1 minimization program to solve for a sparse x-f reconstruction. In contrast, k-t FASTER solves for a spatio-temporally low-rank reconstruction using an iterative hard thresholding and matrix shrinkage algorithm, without requiring a pre-specified basis. We applied each algorithm to incoherently sampled FMRI data and demonstrate that the strict rank-constraint method outperforms spectral- and Karhunen-Loeve Transform (KLT)-sparsity across different metrics.

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