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In functional MRI (fMRI), faster sampling of data can provide richer temporal information and increase temporal degrees of freedom. However, acceleration is generally performed on a volume-by-volume basis, without consideration of the intrinsic spatio-temporal data structure. We present a novel method for accelerating fMRI data acquisition, k-t FASTER (FMRI Accelerated in Space-time via Truncation of Effective Rank), which exploits the low-rank structure of fMRI data.Using matrix completion, 4.27× retrospectively and prospectively under-sampled data were reconstructed (coil-independently) using an iterative nonlinear algorithm, and compared with several different reconstruction strategies. Matrix reconstruction error was evaluated; a dual regression analysis was performed to determine fidelity of recovered fMRI resting state networks (RSNs).The retrospective sampling data showed that k-t FASTER produced the lowest error, approximately 3-4%, and the highest quality RSNs. These results were validated in prospectively under-sampled experiments, with k-t FASTER producing better identification of RSNs than fully sampled acquisitions of the same duration.With k-t FASTER, incoherently under-sampled fMRI data can be robustly recovered using only rank constraints. This technique can be used to improve the speed of fMRI sampling, particularly for multivariate analyses such as temporal independent component analysis.

Original publication

DOI

10.1002/mrm.25395

Type

Journal article

Journal

Magnetic resonance in medicine

Publication Date

08/2015

Volume

74

Pages

353 - 364

Addresses

FMRIB Centre, University of Oxford, Oxford, United Kingdom.

Keywords

Brain, Humans, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Magnetic Resonance Imaging, Image Enhancement, Brain Mapping, Sensitivity and Specificity, Reproducibility of Results, Sample Size, Algorithms, Data Compression, Numerical Analysis, Computer-Assisted, Signal Processing, Computer-Assisted