Spatiotemporal Flexible Sparse Reconstruction for Rapid Dynamic Contrast-Enhanced MRI

作     者：Hu, Yuhan Zhang, Xinlin Chen, Dicheng Yan, Zhiping Shen, Xiaoyong Yan, Gen Lin Ou-yang Lin, Jianzhong Dong, Jiyang Qu, Xiaobo 

作者机构：Xiamen Univ Natl Model Microelect Coll Fujian Prov Key Lab Plasma & Magnet Resonance Dept Elect SciSch Elect Sci & Engn Xiamen 361105 Peoples R China Fujian Med Univ Xiamen Humanity Hosp Fuzhou Peoples R China Zhejiang Univ Affiliated Hosp 1 Sch Med Hangzhou Peoples R China Xiamen Med Coll Dept Radiol Hosp 2 Xiamen Peoples R China Xiamen Univ Dept Med Imaging Southeast Hosp Med Coll Xiamen Peoples R China Xiamen Univ Magnet Resonance Ctr Zhongshan Hosp Xiamen Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING》 (IEEE Trans. Biomed. Eng.)

年 卷 期：2022年第69卷第1期

页      面：229-243页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 08[工学] 

基　　金：National Key R&D Program of China [2017YFC0108703] National Natural Science Foundation of China [61971361, 61871341, 61671399, 61811530021] Natural Science Foundation of Fujian Province of China [2018J06018] Fundamental Research Funds for the Central Universities Science and Technology Program of Xiamen [3502Z20183053] Xiamen University Nanqiang Outstanding Talents Program 

主　　题：Image reconstruction Analytical models Pipelines Magnetic resonance imaging Hospitals Spatial resolution Sparse matrices DCE-MRI parallel imaging golden-angle radial sampling sparse reconstruction fast algorithm 

摘      要：Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a tissue perfusion imaging technique. Some versatile free-breathing DCE-MRI techniques combining compressed sensing (CS) and parallel imaging with golden-angle radial sampling have been developed to improve motion robustness with high spatial and temporal resolution. These methods have demonstrated good diagnostic performance in clinical setting, but the reconstruction quality will degrade at high acceleration rates and overall reconstruction time remains long. In this paper, we proposed a new parallel CS reconstruction model for DCE-MRI that enforces flexible weighted sparse constraint along both spatial and temporal dimensions. Weights were introduced to flexibly adjust the importance of time and space sparsity, and we derived a fast-thresholding algorithm which was proven to be simple and efficient for solving the proposed reconstruction model. Results on both the brain tumor DCE and liver DCE show that, at relatively high acceleration factor of fast sampling, lowest reconstruction error and highest image structural similarity are obtained by the proposed method. Besides, the proposed method achieves faster reconstruction for liver datasets and better physiological measures are also obtained on tumor images.