Fuyixue Wang1,2, Berkin Bilgic1, Zijing Dong3, Mary Kate Manhard1, Ned Ohringer1, Bo Zhao1, Melissa Haskell1,4, Stephen F Cauley1, Qiuyun Fan1, Thomas Witzel1,2, Elfar Adalsteinsson2,5,6, Lawrence L Wald1,2, Kawin Setsompop1,2. 1. A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts. 2. Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts. 3. Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China. 4. Department of Biophysics, Harvard University, Cambridge, Massachusetts. 5. Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts. 6. Institute for Medical Engineering and Science, MIT, Cambridge, Massachusetts.
Abstract
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.
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.
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