Email this article Accelerated 3D Coronary Vessel Wall MR Imaging Based on Compressed Sensing with a Block-Weighted Total Variation Regularization
- Authors: Chen Z.1,2, Zhang X.1,3, Shi C.1, Su S.1, Fan Z.4, Ji J.X.5, Xie G.1, Liu X.1
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Affiliations:
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences
- Centers for Biomedical Engineering, College of Information Science and Technology, University of Science and Technology of China
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center
- Department of Electrical and Computer Engineering, Texas A&M University
- Issue: Vol 48, No 4 (2017)
- Pages: 361-378
- Section: Original Paper
- URL: https://journals.rcsi.science/0937-9347/article/view/247666
- DOI: https://doi.org/10.1007/s00723-017-0866-0
- ID: 247666
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Abstract
Coronary vessel wall magnetic resonance (MR) imaging is important for heart disease diagnosis but often suffers long scan time. Compressed sensing (CS) has been previously used to accelerate MR imaging by reconstructing an MR image from undersampled k-space data using a regularization framework. However, the widely used regularizations in the current CS methods often lead to smoothing effects and thus are unable to reconstruct the coronary vessel walls with sufficient resolution. To address this issue, a novel block-weighted total variation regularization is presented to accelerate the coronary vessel wall MR imaging. The proposed regularization divides the image into two parts: a region-of-interest (ROI) which contains the coronary vessel wall, and the other region with less concerned features. Different penalty weights are given to the two regions. As a result, the small details within ROI do not suffer from over-smoothing while the noise outside the ROI can be significantly suppressed. Results with both numerical simulations and in vivo experiments demonstrated that the proposed method can reconstruct the coronary vessel wall from undersampled k-space data with higher qualities than the conventional CS with the total variation or the edge-preserved total variation.
About the authors
Zhongzhou Chen
Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences
Email: gx.xie@siat.ac.cn
China, Shenzhen; Shenzhen
Xiaoyong Zhang
Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences; Centers for Biomedical Engineering, College of Information Science and Technology, University of Science and Technology of China
Email: gx.xie@siat.ac.cn
China, Shenzhen; Hefei
Caiyun Shi
Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
Email: gx.xie@siat.ac.cn
China, Shenzhen
Shi Su
Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
Email: gx.xie@siat.ac.cn
China, Shenzhen
Zhaoyang Fan
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center
Email: gx.xie@siat.ac.cn
United States, Los Angeles, CA
Jim X. Ji
Department of Electrical and Computer Engineering, Texas A&M University
Email: gx.xie@siat.ac.cn
United States, College Station, TX
Guoxi Xie
Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
Author for correspondence.
Email: gx.xie@siat.ac.cn
China, Shenzhen
Xin Liu
Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
Email: gx.xie@siat.ac.cn
China, Shenzhen
