Fast reconstruction algorithm of magnetic resonance imaging based on wavelet tree sparsity structure
BAO Zhongwen1, DUAN Jizhong1*， YANG Jundong2
(1 Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, Yunnan, China;2 School of Information Science and Engineering, Yunnan University, Kunming 650500, Yunnan, China)
Abstract:
In order to enhance the reconstruction speed of magnetic resonance imaging, a new fast reconstruction algorithm of magnetic resonance imaging based on wavelet tree sparse structure is proposed. Based on the wavelet tree sparse constraint, combining L1 regularization term and TV regularization term constraint, and least square fidelity term constitutes a reconstruction optimization. First, the variable splitting method is used to separate the variables, and then the alternating direction method of multipliers is used to decompose the reconstruction problem into several easy-to-solve subproblems. The solution of each sub-problem can obtain an analytical solution, which can effectively increase the reconstruction performance of magnetic resonance imaging. The experimental results show that the reconstruction effect of our algorithm is better than the comparative WaTMRI algorithm, and the imaging reconstruction speed is about 3.3 times faster than the WaTMRI algorithm on average.
KeyWords:
magnetic resonance imaging; compressed sensing; wavelet tree sparsity; alternating direction method of multipliers(ADMM)
