Novel Noise-Robust and Fast Reconstruction Method For 3D Magnetic Particle Imaging

被引:0
|
作者
Shi Y. [1 ,2 ]
Ren S. [1 ,3 ]
Wang X. [1 ,3 ]
机构
[1] School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing
[2] Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing
[3] Chongqing Center for Microelectronics and Microsystems, Beijing Institute of Technology, Chongqing
关键词
Magnetic particle imaging; Noise robustness; Sparse reconstruction; Three-dimensional total variation;
D O I
10.15918/j.tbit1001-0645.2021.308
中图分类号
学科分类号
摘要
To improve the 3D imaging and reconstruction speed of Magnetic Particle Imaging (MPI), reduce the requirement of 3D refactoring to the completeness of sampled projection data, a novel Noise-Robust 3D Sparse Sampling Magnetic Particle Imaging (3D NRSS-MPI) method was proposed. The algorithm was arranged to reconstruct 3D MPI noisy data by solving a convex optimization problem formed with the l2 norm and sparse regular constraint of MPI projection imaging. Eliminating the limit of MPI scanning trajectory, the proposed method was designed as a universal basic model for the developing MPI technique. Taking the advantage of MPI priori information to improve the 3D reconstruction robustness of noisy MPI projection data, 3D total variation sparse operator was established to realize matrix-free operation, improving the efficiency of operation. The results of point source and coronary phantom imaging experiments show that the proposed 3D NRSS-MPI method can effectively eliminate the reconstructed image star artifacts at 1/4 undersampling, obtain a higher image signal-to-noise ratio, and make the coronary reconstruction structure similarity exceed 0.701, which can accurately reconstruct the undersampled and noisy MPI data, effectively shortening the imaging and reconstruction time by 4 times. Copyright ©2022 Transaction of Beijing Institute of Technology. All rights reserved.
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页码:543 / 550
页数:7
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