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k‐t FASTER: Acceleration of functional MRI data acquisition using low rank constraints
Author(s) -
Chiew Mark,
Smith Stephen M.,
Koopmans Peter J.,
Graedel Nadine N.,
Blumensath Thomas,
Miller Karla L.
Publication year - 2015
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.25395
Subject(s) - computer science , sampling (signal processing) , acceleration , artificial intelligence , pattern recognition (psychology) , functional magnetic resonance imaging , rank (graph theory) , temporal resolution , algorithm , computer vision , mathematics , medicine , physics , filter (signal processing) , combinatorics , quantum mechanics , classical mechanics , radiology
Purpose 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. Theory and Methods 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). Results 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. Conclusion 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. Magn Reson Med 74:353–364, 2015. © 2014 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.