Improved dynamic contrast-enhanced MRI using low rank with joint sparsity

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

Research output: Journal PublicationArticlepeer-review

2 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)121193-121203
JournalIEEE Access
Publication statusPublished - 14 Nov 2022


  • Compressed sensing
  • dynamic contrast
  • joint sparsity
  • parallel imaging
  • reconstruction efficiency


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