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Compressed Sensing 3D‐GRASE for faster High‐Resolution MRI
Author(s) -
CristobalHuerta A.,
Poot D.H.J.,
Vogel M.W.,
Krestin G.P.,
HernandezTamames J.A.
Publication year - 2019
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.27789
Subject(s) - compressed sensing , image quality , computer science , sampling (signal processing) , imaging phantom , gaussian , poisson distribution , undersampling , data acquisition , artificial intelligence , algorithm , pattern recognition (psychology) , mathematics , computer vision , nuclear medicine , physics , medicine , statistics , image (mathematics) , quantum mechanics , operating system , filter (signal processing)
Purpose High‐resolution three‐dimensional (3D) structural MRI is useful for delineating complex or small structures of the body. However, it requires long acquisition times and high SAR, limiting its clinical use. The purpose of this work is to accelerate the acquisition of high‐resolution images by combining compressed sensing and parallel imaging (CSPI) on a 3D‐GRASE sequence and to compare it with a (CS)PI 3D‐FSE sequence. Several sampling patterns were investigated to assess their influence on image quality. Methods The proposed k‐space sampling patterns are based on two undersampled k‐space grids, variable density (VD) Poisson‐disc, and VD pseudo‐random Gaussian, and five different trajectories described in the literature. Bloch simulations are performed to obtain the transform point spread function and evaluate the coherence of each sampling pattern. Image resolution was assessed by the full‐width at half‐maximum (FWHM). Prospective CSPI 3D‐GRASE phantom and in vivo experiments in knee and brain are carried out to assess image quality, SNR, SAR, and acquisition time compared to PI 3D‐GRASE, PI 3D‐FSE, and CSPI 3D‐FSE acquisitions. Results Sampling patterns with VD Poisson‐disc obtain the lowest coherence for both PD‐weighted and T 2 ‐weighted acquisitions. VD pseudo‐random Gaussian obtains lower FWHM, but higher sidelobes than VD Poisson‐disc. CSPI 3D‐GRASE reduces acquisition time (43% for PD‐weighted and 40% for T 2 ‐weighted) and SAR (∼45% for PD‐weighted and T 2 ‐weighted) compared to CSPI 3D‐FSE. Conclusions CSPI 3D‐GRASE reduces acquisition time compared to a CSPI 3DFSE acquisition, preserving image quality. The design of the sampling pattern is crucial for image quality in CSPI 3D‐GRASE image acquisitions.