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Simultaneous multi-slice image reconstruction using regularized image domain split slice-GRAPPA for diffusion MRI

delete2021-05-01
delete11
PRE
AI
S
SK. HashemizadehKolowri
R
Rong‐Rong Chen
G
Ganesh Adluru
D
Douglas Dean
E
Elisabeth A. Wilde
A
Andrew L. Alexander
E
Edward DiBella *
DOI:10.1016/j.media.2021.102000delete
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Abstract

Abstract

En 中文
The main goal of this work is to improve the quality of simultaneous multi-slice (SMS) reconstruction for diffusion MRI. We accomplish this by developing an image domain method that reaps the benefits of both SENSE and GRAPPA-type approaches and enables image regularization in an optimization frame-work. We propose a new approach termed regularized image domain split slice-GRAPPA (RI-SSG), which establishes an optimization framework for SMS reconstruction. Within this framework, we use a robust forward model to take advantage of both the SENSE model with explicit sensitivity estimations and the SSG model with implicit kernel relationship among coil images. The proposed approach also allows com-bining of coil images to increase the SNR and enables image domain regularization on estimated coil-combined single slices. We compare the performance of RI-SSG with that of SENSE and SSG using in-vivo diffusion EPI datasets with simulated and actual SMS acquisitions collected on a 3T MR scanner. Recon-structed diffusion-weighted images (DWIs) and the resulting diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) maps are analyzed to evaluate the quantitative and qualitative performance of the three methods. The DWIs reconstructed by RI-SSG are closer to the single -band ground truth images than SENSE and SSG. Specifically, the proposed RI-SSG reduces the normalized root-mean-square-error (nRMSE) against ground truth images by-5% and increases the structural simi-larity index (SSIM) by-4% compared to SSG. All three methods produce similar fractional anisotropy (FA) maps using DTI representation, but mean diffusivity (MD) and fiber orientation estimates using RI-SSG are closer to the reference than SENSE and SSG. RI-SSG results in NODDI maps with noticeably smaller errors than those of SENSE and SSG and improves the accuracy of the mean value of orientation dis-persion index (ODI) by-5% and the mean value of intracellular volume fraction by-7% in regions of interest in brain white matter compared to SSG. (c) 2021 Elsevier B.V. All rights reserved.
Keywords:
Diffusion MRI
Simultaneous multi-slice
Split slice-GRAPPA
Image domain kernels
Image regularization
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Medical Image Analysis cover
Medical Image Analysis
IF:
11.8
Papers:
3.9K
Citations:
2.4W

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U
university of wisconsin madison
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Papers: 2.9W
Citations: 53
U
University of Utah
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Utah System of Higher Education
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Papers: 4.0W
Citations: 161
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