High-fidelity, accelerated whole-brain submillimeter in vivo diffusion MRI using gSlider-spherical ridgelets (gSlider-SR).
High-fidelity, accelerated whole-brain submillimeter in vivo diffusion MRI using gSlider-spherical ridgelets (gSlider-SR).
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DOI:
10.1002/mrm.28232
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发表时间:
2020-10
影响因子:
3.3
通讯作者:
Rathi, Yogesh
中科院分区:
文献类型:
--
作者:
Ramos-Llorden, Gabriel;Ning, Lipeng;Liao, Congyu;Mukhometzianov, Rinat;Michailovich, Oleg;Setsompop, Kawin;Rathi, Yogesh
To develop an accelerated, robust, and accurate diffusion MRI acquisition and reconstruction technique for submillimeter whole human brain in-vivo scan on a clinical scanner. We extend the ultra-high resolution diffusion MRI acquisition technique, gSlider, by allowing under-sampling in q-space and Radio-Frequency (RF)-encoded data, thereby accelerating the total acquisition time of conventional gSlider. The novel method, termed gSlider-SR, compensates for the lack of acquired information by exploiting redundancy in the dMRI data using a basis of Spherical Ridgelets (SR), while simultaneously enhancing the signal-to-noise ratio. Using Monte-Carlo simulation with realistic noise levels and several acquisitions of in-vivo human brain dMRI data (acquired on a Siemens Prisma 3T scanner), we demonstrate the efficacy of our method using several quantitative metrics. For high-resolution dMRI data with realistic noise levels (synthetically added), we show that gSlider-SR can reconstruct high-quality dMRI data at different acceleration factors preserving both signal and angular information. With in-vivo data, we demonstrate that gSlider-SR can accurately reconstruct 860 μm diffusion MRI data (64 diffusion directions at b = 2000 s/mm2), at comparable quality as that obtained with conventional gSlider with four averages, thereby providing an eight-fold reduction in scan time (from 1 h 20 min to 10 min). gSlider-SR enables whole-brain high angular resolution dMRI at a submillimeter spatial resolution with a dramatically reduced acquisition time, making it feasible to use the proposed scheme on existing clinical scanners.
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