Journal Article

Compressed sensing based dynamic MR image reconstruction by using 3D-total generalized variation and tensor decomposition: k-t TGV-TD

Han, Lulu, Wang, Zhikang, Xia, Ling, Chu, Yonghua, Xu, Wenlong, Jiang, Mingfeng, Sun, Jianzhong, Zhang, Jucheng

2022

Overview

Purpose Compressed Sensing Magnetic Resonance Imaging (CS-MRI) is a promising technique to accelerate dynamic cardiac MR imaging (DCMRI). For DCMRI, the CS-MRI usually exploits image signal sparsity and low-rank property to reconstruct dynamic images from the undersampled k-space data. In this paper, a novel CS algorithm is investigated to improve dynamic cardiac MR image reconstruction quality under the condition of minimizing the k-space recording. Methods The sparse representation of 3D cardiac magnetic resonance data is implemented by synergistically integrating 3D total generalized variation (3D-TGV) algorithm and high order singular value decomposition (HOSVD) based Tensor Decomposition, termed k-t TGV-TD method. In the proposed method, the low rank structure of the 3D dynamic cardiac MR data is performed with the HOSVD method, and the localized image sparsity is achieved by the 3D-TGV method. Moreover, the Fast Composite Splitting Algorithm (FCSA) method, combining the variable splitting with operator splitting techniques, is employed to solve the low-rank and sparse problem. Two different cardiac MR datasets (cardiac perfusion and cine MR datasets) are used to evaluate the performance of the proposed method. Results Compared with the state-of-art methods, such as k-t SLR, 3D-TGV, HOSVD based tensor decomposition and low-rank plus sparse method, the proposed k-t TGV-TD method can offer improved reconstruction accuracy in terms of higher peak SNR (PSNR) and structural similarity index (SSIM). The proposed k-t TGV-TD method can achieve significantly better and stable reconstruction results than state-of-the-art methods in terms of both PSNR and SSIM, especially for cardiac perfusion MR dataset. Conclusions This work proved that the k-t TGV-TD method was an effective sparse representation way for DCMRI, which was capable of significantly improving the reconstruction accuracy with different acceleration factors.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Algorithms / Datasets / Decomposition / Dynamic cardiac MR imaging / Heart / Heart - diagnostic imaging / High speed trains / Higher-order singular value decomposition / Humans / Image processing / Image Processing, Computer-Assisted - methods / Image quality / Image reconstruction / Imaging / Magnetic resonance imaging / Magnetic Resonance Imaging - methods / Mathematical analysis / Medicine / Medicine & Public Health / Methods / Performance evaluation / Perfusion / Radiology / Rankings / Representations / Resonance / Singular value decomposition / Sparse representation / Sparsity / Splitting / Tensors / Total generalized variation / Wavelet transforms