Journal article
Efficient parallel reconstruction for high resolution multishot spiral diffusion data with low rank constraint
Magnetic resonance in medicine, Vol.77(3), pp.1359-1366
03/2017
DOI: 10.1002/mrm.26199
PMID: 26968846
Abstract
To propose a novel reconstruction method using parallel imaging with low rank constraint to accelerate high resolution multishot spiral diffusion imaging.
The undersampled high resolution diffusion data were reconstructed based on a low rank (LR) constraint using similarities between the data of different interleaves from a multishot spiral acquisition. The self-navigated phase compensation using the low resolution phase data in the center of k-space was applied to correct shot-to-shot phase variations induced by motion artifacts. The low rank reconstruction was combined with sensitivity encoding (SENSE) for further acceleration. The efficiency of the proposed joint reconstruction framework, dubbed LR-SENSE, was evaluated through error quantifications and compared with ℓ1 regularized compressed sensing method and conventional iterative SENSE method using the same datasets.
It was shown that with a same acceleration factor, the proposed LR-SENSE method had the smallest normalized sum-of-squares errors among all the compared methods in all diffusion weighted images and DTI-derived index maps, when evaluated with different acceleration factors (R = 2, 3, 4) and for all the acquired diffusion directions.
Robust high resolution diffusion weighted image can be efficiently reconstructed from highly undersampled multishot spiral data with the proposed LR-SENSE method. Magn Reson Med 77:1359-1366, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
Details
- Title: Subtitle
- Efficient parallel reconstruction for high resolution multishot spiral diffusion data with low rank constraint
- Creators
- Congyu Liao - Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, ChinaYing Chen - Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, ChinaXiaozhi Cao - Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, ChinaSong Chen - Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, ChinaHongjian He - Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, ChinaMerry Mani - Department of Psychiatry, University of Iowa, Iowa City, Iowa, USAMathews Jacob - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USAVincent Magnotta - Department of Radiology, University of Iowa, Iowa City, Iowa, USAJianhui Zhong - Center for Innovative and Collaborative Detection and Treatment of Infectious Diseases, Zhejiang University, Hangzhou, Zhejiang, China
- Resource Type
- Journal article
- Publication Details
- Magnetic resonance in medicine, Vol.77(3), pp.1359-1366
- DOI
- 10.1002/mrm.26199
- PMID
- 26968846
- NLM abbreviation
- Magn Reson Med
- ISSN
- 0740-3194
- eISSN
- 1522-2594
- Publisher
- United States
- Grant note
- P30 CA086862 / NCI NIH HHS
- Language
- English
- Date published
- 03/2017
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Psychiatry; Iowa Neuroscience Institute; Radiation Oncology
- Record Identifier
- 9984051781402771
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