电工技术学报2022，Vol.37Issue(17)：4269-4283,15.DOI:10.19595/j.cnki.1000-6753.tces.210959
基于矩阵式线圈的感应式磁声磁粒子浓度成像研究
Magnetoacoustic Concentration Tomography of Magnetic Nanoparticles with Magnetic Induction Based on Matrix Coil
摘要
Abstract
Magnetoacoustic concentration tomography of magnetic nanoparticles (MNPs) with magnetic induction (MACT-MI) is a new method for MNPs concentration tomography. To reduce the amplitude of the excitation current source and produce a more stable magnetoacoustic signal, MACT-MI system based on matrix coils was studied. Optimal design of matrix coils used the artificial fish swarm algorithm based on beetle antennae search algorithm (BAS-AFSA). According to the optimization results the physical process of MACT-MI was solved by using COMSOL and then the results were compared with the results of Maxwell coils. A concentration gradient model was also constructed based on the distribution of MNPs in the actual biological tissue environment, and both the magnetic force and sound pressure distribution in the region of interest (ROI) were obtained. The results show that the matrix coils designed in this paper can produce a more stable sound pressure signal and a more uniformly distributed gradient magnetic field with equal size under a smaller excitation. According to the constructed concentration gradient model, both the magnetic force and the width of the sound pressure wave of MNPs are proportional to the width of the gradient region, and the peak value of sound pressure is proportional to the concentration gradient in the gradient region. The results provide a prospective basis for the design of imaging equipment, the follow-up experiments and clinical applications of MACT-MI.关键词
磁声磁粒子浓度成像/天牛须-鱼群算法/矩阵式线圈/浓度渐变/磁力/声压Key words
Magnetic nanoparticles with magnetic induction (MACT-MI)/artificial fish swarm algorithm based on beetle antennae search algorithm/matrix coils/concentration gradient/magnetic force/sound pressure分类
信息技术与安全科学引用本文复制引用
闫孝姮,李政兴,孙迪,陈伟华,高鹏..基于矩阵式线圈的感应式磁声磁粒子浓度成像研究[J].电工技术学报,2022,37(17):4269-4283,15.基金项目
This work is partially supported by the Natural Science Foundation of Liaoning Province(2019-ZD-0039)and the Basic Scientific Research Project of Liaoning Education Department(LJ2020JCL003). （2019-ZD-0039）
