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A joint compressed-sensing and super-resolution approach for very high-resolution diffusion imaging.
Neuroimage 2016; 125:386-400N

Abstract

Diffusion MRI (dMRI) can provide invaluable information about the structure of different tissue types in the brain. Standard dMRI acquisitions facilitate a proper analysis (e.g. tracing) of medium-to-large white matter bundles. However, smaller fiber bundles connecting very small cortical or sub-cortical regions cannot be traced accurately in images with large voxel sizes. Yet, the ability to trace such fiber bundles is critical for several applications such as deep brain stimulation and neurosurgery. In this work, we propose a novel acquisition and reconstruction scheme for obtaining high spatial resolution dMRI images using multiple low resolution (LR) images, which is effective in reducing acquisition time while improving the signal-to-noise ratio (SNR). The proposed method called compressed-sensing super resolution reconstruction (CS-SRR), uses multiple overlapping thick-slice dMRI volumes that are under-sampled in q-space to reconstruct diffusion signal with complex orientations. The proposed method combines the twin concepts of compressed sensing and super-resolution to model the diffusion signal (at a given b-value) in a basis of spherical ridgelets with total-variation (TV) regularization to account for signal correlation in neighboring voxels. A computationally efficient algorithm based on the alternating direction method of multipliers (ADMM) is introduced for solving the CS-SRR problem. The performance of the proposed method is quantitatively evaluated on several in-vivo human data sets including a true SRR scenario. Our experimental results demonstrate that the proposed method can be used for reconstructing sub-millimeter super resolution dMRI data with very good data fidelity in clinically feasible acquisition time.

Authors+Show Affiliations

Brigham and Women's Hospital, Harvard Medical School, Boston, USA. Electronic address: lning@bwh.harvard.edu.Massachusetts General Hospital, Harvard Medical School, Boston, USA.Electrical and Computer Engineering, University of Waterloo, Canada.Massachusetts General Hospital, Harvard Medical School, Boston, USA.Brigham and Women's Hospital, Harvard Medical School, Boston, USA.Brigham and Women's Hospital, Harvard Medical School, Boston, USA.Brigham and Women's Hospital, Harvard Medical School, Boston, USA.

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

26505296

Citation

Ning, Lipeng, et al. "A Joint Compressed-sensing and Super-resolution Approach for Very High-resolution Diffusion Imaging." NeuroImage, vol. 125, 2016, pp. 386-400.
Ning L, Setsompop K, Michailovich O, et al. A joint compressed-sensing and super-resolution approach for very high-resolution diffusion imaging. Neuroimage. 2016;125:386-400.
Ning, L., Setsompop, K., Michailovich, O., Makris, N., Shenton, M. E., Westin, C. F., & Rathi, Y. (2016). A joint compressed-sensing and super-resolution approach for very high-resolution diffusion imaging. NeuroImage, 125, pp. 386-400. doi:10.1016/j.neuroimage.2015.10.061.
Ning L, et al. A Joint Compressed-sensing and Super-resolution Approach for Very High-resolution Diffusion Imaging. Neuroimage. 2016 Jan 15;125:386-400. PubMed PMID: 26505296.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A joint compressed-sensing and super-resolution approach for very high-resolution diffusion imaging. AU - Ning,Lipeng, AU - Setsompop,Kawin, AU - Michailovich,Oleg, AU - Makris,Nikos, AU - Shenton,Martha E, AU - Westin,Carl-Fredrik, AU - Rathi,Yogesh, Y1 - 2015/10/23/ PY - 2015/06/17/received PY - 2015/09/11/revised PY - 2015/10/20/accepted PY - 2015/10/28/entrez PY - 2015/10/28/pubmed PY - 2016/10/1/medline KW - Compressed sensing KW - Diffusion MRI KW - Spherical ridgelets KW - Super resolution reconstruction SP - 386 EP - 400 JF - NeuroImage JO - Neuroimage VL - 125 N2 - Diffusion MRI (dMRI) can provide invaluable information about the structure of different tissue types in the brain. Standard dMRI acquisitions facilitate a proper analysis (e.g. tracing) of medium-to-large white matter bundles. However, smaller fiber bundles connecting very small cortical or sub-cortical regions cannot be traced accurately in images with large voxel sizes. Yet, the ability to trace such fiber bundles is critical for several applications such as deep brain stimulation and neurosurgery. In this work, we propose a novel acquisition and reconstruction scheme for obtaining high spatial resolution dMRI images using multiple low resolution (LR) images, which is effective in reducing acquisition time while improving the signal-to-noise ratio (SNR). The proposed method called compressed-sensing super resolution reconstruction (CS-SRR), uses multiple overlapping thick-slice dMRI volumes that are under-sampled in q-space to reconstruct diffusion signal with complex orientations. The proposed method combines the twin concepts of compressed sensing and super-resolution to model the diffusion signal (at a given b-value) in a basis of spherical ridgelets with total-variation (TV) regularization to account for signal correlation in neighboring voxels. A computationally efficient algorithm based on the alternating direction method of multipliers (ADMM) is introduced for solving the CS-SRR problem. The performance of the proposed method is quantitatively evaluated on several in-vivo human data sets including a true SRR scenario. Our experimental results demonstrate that the proposed method can be used for reconstructing sub-millimeter super resolution dMRI data with very good data fidelity in clinically feasible acquisition time. SN - 1095-9572 UR - https://www.unboundmedicine.com/medline/citation/26505296/A_joint_compressed_sensing_and_super_resolution_approach_for_very_high_resolution_diffusion_imaging_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S1053-8119(15)00973-8 DB - PRIME DP - Unbound Medicine ER -