Motion‐robust sub‐millimeter isotropic diffusion imaging through motion corrected generalized slice dithered enhanced resolution (MC‐gSlider) acquisition
Name
nihms949733.pdf
Description
Accepted version
Size
1.58 MB
Format
Adobe PDF
Checksum (MD5)
2919240cd0f26c166e9b29e8b828f969
Author(s)
Wang, Fuyixue
Bilgic, Berkin
Dong, Zijing
Manhard, Mary Kate
Ohringer, Ned
Zhao, Bo
Haskell, Melissa
Cauley, Stephen F
Fan, Qiuyun
Witzel, Thomas
Date Issued
2018
Journal
Magnetic Resonance in Medicine
Publisher
Wiley
Version
Author's final manuscript
Abstract
© 2018 International Society for Magnetic Resonance in Medicine Purpose: To develop an efficient MR technique for ultra-high resolution diffusion MRI (dMRI) in the presence of motion. Methods: gSlider is an SNR-efficient high-resolution dMRI acquisition technique. However, subject motion is inevitable during a prolonged scan for high spatial resolution, leading to potential image artifacts and blurring. In this study, an integrated technique termed Motion Corrected gSlider (MC-gSlider) is proposed to obtain high-quality, high-resolution dMRI in the presence of large in-plane and through-plane motion. A motion-aware reconstruction with spatially adaptive regularization is developed to optimize the conditioning of the image reconstruction under difficult through-plane motion cases. In addition, an approach for intra-volume motion estimation and correction is proposed to achieve motion correction at high temporal resolution. Results: Theoretical SNR and resolution analysis validated the efficiency of MC-gSlider with regularization, and aided in selection of reconstruction parameters. Simulations and in vivo experiments further demonstrated the ability of MC-gSlider to mitigate motion artifacts and recover detailed brain structures for dMRI at 860 μm isotropic resolution in the presence of motion with various ranges. Conclusion: MC-gSlider provides motion-robust, high-resolution dMRI with a temporal motion correction sensitivity of 2 s, allowing for the recovery of fine detailed brain structures in the presence of large subject movements.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
10.1002/MRM.27196
https://doi.org/10.1002/MRM.27196
