Abstract
PurposeIn 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 MethodsUsing 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).ResultsThe 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.ConclusionWith 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 Wiley Periodicals, Inc.
Highlights
Strategies for accelerating functional MRI (FMRI) data acquisition have received strong interest since the technique was introduced for neuroimaging
The retrospective sampling data showed that k-‐t FASTER produced the lowest error, approximately 3-‐4%, and the highest quality resting state networks (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
Summary
Strategies for accelerating functional MRI (FMRI) data acquisition have received strong interest since the technique was introduced for neuroimaging. Faster sampling of blood oxygenation level dependent (BOLD) signals provides improved statistical power and richness of the temporal modelling in brain dynamics measured with FMRI [1]. Higher sampling bandwidths can reduce aliasing of physiological noise [2] and provide finer characterisations of hemodynamic responses. Higher sampling rates can reduce the imaging durations required to achieve RSN estimation (to a limit, dictated by the low-‐frequency nature of the fluctuations), or more importantly, improve estimation of RSNs with greater temporal dimensionality over the same total scan time. FMRI data is modelled as a linear mixture of components (commonly using statistically independent component models), and robust unmixing of these RSN components can require many time points [5]. Recent work has suggested that higher sampling rates can reveal frequency-‐specific RSN characteristics unavailable to methods with smaller sampling bandwidths [6]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
