Learning Joint-Sparse Codes for Calibration-Free Parallel MR Imaging

• Published in: IEEE transactions on medical imaging Vol. 37; no. 1; pp. 251 - 261
• Main Authors: Shanshan Wang, Sha Tan, Yuan Gao, Qiegen Liu, Ying, Leslie, Taohui Xiao, Yuanyuan Liu, Xin Liu, Hairong Zheng, Dong Liang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Accuracy; adaptive sparse coding; Algorithms; Brain - diagnostic imaging; Calibration; Coding; Compressed sensing; Humans; Image coding; Image Processing, Computer-Assisted - methods; Image reconstruction; Imaging; In vivo methods and tests; joint sparsity; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; MR image reconstruction; Parallel imaging; Representations; Robustness; Sensitivity; Sparsity
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2017.2746086

The integration of compressed sensing and parallel imaging (CS-PI) has shown an increased popularity in recent years to accelerate magnetic resonance (MR) imaging. Among them, calibration-free techniques have presented encouraging performances due to its capability in robustly handling the sensitivity information. Unfortunately, existing calibration-free methods have only explored joint-sparsity with direct analysis transform projections. To further exploit joint-sparsity and improve reconstruction accuracy, this paper proposes to Learn joINt-sparse coDes for caliBration-free parallEl mR imaGing (LINDBERG) by modeling the parallel MR imaging problem as an ℓ 2 -ℓ F -ℓ 2,1 minimization objective with an ℓ 2 norm constraining data fidelity, Frobenius norm enforcing sparse representation error and the ℓ 2,1 mixed norm triggering joint sparsity across multichannels. A corresponding algorithm has been developed to alternatively update the sparse representation, sensitivity encoded images and K-space data. Then, the final image is produced as the square root of sum of squares of all channel images. Experimental results on both physical phantom and in vivo data sets show that the proposed method is comparable and even superior to stateof-the-art CS-PI reconstruction approaches. Specifically, LINDBERG has presented strong capability in suppressing noise and artifacts while reconstructing MR images from highly undersampled multichannel measurements.