Accelerating Functional MRI Using Fixed-Rank Approximations and Radial-Cartesian Sampling

作者：Chiew Mark^*; Graedel Nadine N; Mcnab Jennifer A; Smith Stephen M; Miller Karla L

来源：Magnetic Resonance in Medicine, 2016, 76(6): 1825-1836.

DOI：10.1002/mrm.26079

• fMRI
• k-t acceleration
• low-rank acceleration
• matrix completion
• k-t FASTER
• golden ratio

Purpose: Recently, k-t FASTER (fMRI Accelerated in Space-time by means of Truncation of Effective Rank) was introduced for rank-constrained acceleration of fMRI data acquisition. Here we demonstrate improvements achieved through a hybrid three-dimensional radial-Cartesian sampling approach that allows posthoc selection of acceleration factors, as well as incorporation of coil sensitivity encoding in the reconstruction.
Methods: The multicoil rank-constrained reconstruction used hard thresholding and shrinkage on matrix singular values of the space-time data matrix, using sensitivity encoding and the non-uniform Fast Fourier Transform to enforce data consistency in the multicoil non-Cartesian k-t domain. Variable acceleration factors were made possible using a radial increment based on the golden ratio. Both retrospective and prospectively under-sampled data were used to assess the fidelity of the enhancements to the k-t FASTER technique in resting and task-fMRI data.
Results: The improved k-t FASTER is capable of tailoring acceleration factors for recovery of different signal components, achieving up to R = 12.5 acceleration in visual-motor task data. The enhancements reduce data matrix reconstruction errors even at much higher acceleration factors when compared directly with the original k-t FASTER approach.
Conclusion: We have shown that k-t FASTER can be used to significantly accelerate fMRI data acquisition with little penalty to data quality. (C) 2016 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.

• 出版日期2016-12