Joint reconstruction framework of compressed sensing and nonlinear parallel imaging for dynamic cardiac magnetic resonance imaging

BMC Medical Imaging

Yihang Zhou¹  Xiaoyan Wang²  Xia Zhao³  Jianxiang Liao³  Zhanqi Hu³  Lingyu Kong³  Cailei Zhao³  Jun Yang⁴ 

[1] Hong Kong Sanatorium and Hospital, 5 A Kung Ngam Village Road, Shau Kei Wan, Hong Kong, China;School of Physics and Electronic Engineering, Yuxi Normal University, 653100, Yuxi, Yunnan, China;Shenzhen Children’s Hospital, 518038, Shenzhen, Guangdong, China;Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, 518055, Shenzhen, Guangdong, China;

关键词: Dynamic cardiac MRI;    Compressed sensing;    Parallel imaging;    Nonlinear GRAPPA;   

DOI  :  10.1186/s12880-021-00685-2

来源: Springer

【 摘 要 】

Compressed Sensing (CS) and parallel imaging are two promising techniques that accelerate the MRI acquisition process. Combining these two techniques is of great interest due to the complementary information used in each. In this study, we proposed a novel reconstruction framework that effectively combined compressed sensing and nonlinear parallel imaging technique for dynamic cardiac imaging. Specifically, the proposed method decouples the reconstruction process into two sequential steps: In the first step, a series of aliased dynamic images were reconstructed from the highly undersampled k-space data using compressed sensing; In the second step, nonlinear parallel imaging technique, i.e. nonlinear GRAPPA, was utilized to reconstruct the original dynamic images from the reconstructed k-space data obtained from the first step. In addition, we also proposed a tailored k-space down-sampling scheme that satisfies both the incoherent undersampling requirement for CS and the structured undersampling requirement for nonlinear parallel imaging. The proposed method was validated using four in vivo experiments of dynamic cardiac cine MRI with retrospective undersampling. Experimental results showed that the proposed method is superior at reducing aliasing artifacts and preserving the spatial details and temporal variations, compared with the competing k-t FOCUSS and k-t FOCUSS with sensitivity encoding methods, with the same numbers of measurements.

【 授权许可】

CC BY