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Improved Dynamic Contrast-Enhanced MRI Using Low Rank with Joint Sparsity

Zhang, Jichang; Najeeb, Faisal; Wang, Xinpei; Xu, Pengfei; Omer, Hammad; Zheng, Jianjun; Zhang, Jingfeng; Francis, Sue; Glover, Paul; Bowtell, Richard; Wang, Chengbo

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Authors

Jichang Zhang

Faisal Najeeb

Xinpei Wang

Pengfei Xu

Hammad Omer

Jianjun Zheng

Jingfeng Zhang

PAUL GLOVER paul.glover@nottingham.ac.uk
Associate Professor

Chengbo Wang



Abstract

This work presents a free-breathing dynamic contrast-enhanced (DCE) MRI reconstruction method called low-rank plus sparse (L+S) with joint sparsity. The proposed method improved dynamic contrast performance by integrating an additional temporal Fast Fourier Transform (FFT) constraint into the standard L+S decomposition method. In the proposed method, both temporal total variation (TV) sparsity constraint and temporal FFT constraint are integrated into a standard L+S decomposition model, forming L+S with joint sparsity. Temporal TV and Temporal FFT aim to suppress under-sampling artifacts and improve dynamic contrast in DCE-MRI, respectively. A fast composite splitting algorithm (FCSA) is adopted for solving the L+S model with multiple sparsity constraints, maintaining the reconstruction efficiency. A computer simulation framework was developed to compare the performance of L+S with joint sparsity and other reconstruction schemes. The performance of L+S with joint sparsity was tested using computer simulation and several liver DCE-MRI datasets. The proposed L+S based method achieved around four times faster reconstruction speed than the GRASP method. With the support of an additional sparsity constraint, the peak DCE signal in the proposed method was increased by more than 20% over that of a standard L+S decomposition.

Journal Article Type Article
Acceptance Date Nov 4, 2022
Online Publication Date Nov 14, 2022
Publication Date 2022
Deposit Date Nov 18, 2022
Publicly Available Date Dec 2, 2022
Journal IEEE Access
Electronic ISSN 2169-3536
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed Peer Reviewed
Volume 10
Pages 121193-121203
DOI https://doi.org/10.1109/access.2022.3222313
Public URL https://nottingham-repository.worktribe.com/output/13752897
Publisher URL https://ieeexplore.ieee.org/document/9950488

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