Design of Gradient Magnetic Field Coil Based on an Improved Particle Swarm Optimization Algorithm for Magnetocardiography Systems

被引:31
|
作者
Zhao, Fengwen [1 ,2 ]
Zhou, Xiangyang [1 ,2 ]
Xie, Xiaoxuan [1 ,3 ,4 ]
Wang, Kai [1 ,2 ]
机构
[1] Beihang Univ, Sch Instrumentat & Optoelect Engn, Beijing 100191, Peoples R China
[2] Beihang Univ, Hangzhou Innovat Inst, Hangzhou 310051, Peoples R China
[3] Beihang Univ, Res Inst Frontier Sci, Beijing 100191, Peoples R China
[4] Hangzhou Innovat Inst, Beihang Univ, Hangzhou 310051, Peoples R China
基金
中国国家自然科学基金;
关键词
Longitudinal gradient coil; magnetocardiography system; multistage inertia weights; nonlinear goal optimization; particle swarm optimization; MRI;
D O I
10.1109/TIM.2021.3106677
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
An improved particle swarm optimization method with multistage inertia weights is proposed for the design of gradient coils for use in magnetocardiography systems. With this method, the design of the gradient coils is transformed into a constrained nonlinear objective optimization problem, and the particle swarm optimization method with multistage inertia weights is used to solve this problem and thus obtain optimized size parameters for the coils. Furthermore, through comprehensive use of several parameters in combination, including the magnetic field derivative, the target field information, and the optimization algorithm, a highly linear magnetic field is produced by considering the constraints of the coil structure and the turns ratio. Simulation results show that the linearity spatial deviation of the designed gradient coil is one order of magnitude lower than that of the Maxwell coil with a sphere of a radius of 0.5 R. Using the proposed method, two optimized gradient coils are manufactured for magnetocardiography system applications with magnetic fields that are matched well with those produced by the theoretical simulation model. Experimental results show that the maximum of the linearity spatial deviation of the proposed gradient coils reaches 2.2 x 10(-3) and 1.4 x 10(-3) along the z-axis within +/- 0.5 R, respectively.
引用
收藏
页数:9
相关论文
共 50 条
  • [1] Design of Gradient Coil for Magnetic Resonance Imaging Applying Particle-Swarm Optimization
    Cobos Sanchez, Clemente
    Fernandez Pantoja, Mario
    Gomez Martin, Rafael
    IEEE TRANSACTIONS ON MAGNETICS, 2011, 47 (12) : 4761 - 4768
  • [2] Optimization Design of AC Filters for HVDC Systems Based on Improved Particle Swarm Optimization Algorithm
    Wang, Chengliang
    Shi, Fan
    Wang, Honghua
    Yang, Qingsheng
    2019 5TH INTERNATIONAL CONFERENCE ON GREEN MATERIALS AND ENVIRONMENTAL ENGINEERING, 2020, 453
  • [3] Design of Highly Uniform Magnetic Field Coils Based on a Particle Swarm Optimization Algorithm
    Wu, Wenfeng
    Zhou, Binquan
    Liu, Zhanchao
    Wang, Jing
    Pang, Haoying
    Chen, Linlin
    Quan, Wei
    Liu, Gang
    IEEE ACCESS, 2019, 7 : 125310 - 125322
  • [4] Convergence analysis of particle swarm optimization and its improved algorithm based on gradient
    Department of Electrical and Automation, Shanghai Maritime University, Shanghai 200135, China
    不详
    Kongzhi yu Juece Control Decis, 2009, 4 (560-564):
  • [5] Phase Mask Design Based on an Improved Particle Swarm Optimization Algorithm for Depth of Field Extension
    Huang, Zeyu
    Li, Fei
    Zhu, Lina
    Ye, Guo
    Zhao, Tingyu
    APPLIED SCIENCES-BASEL, 2023, 13 (13):
  • [6] An Algorithm Based on the Improved Particle Swarm Optimization
    Ge, Ri-Bo
    PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, KNOWLEDGE ENGINEERING AND INFORMATION ENGINEERING (SEKEIE 2014), 2014, 114 : 176 - 179
  • [7] Optimization design of CVT cooling system based on improved particle swarm optimization algorithm
    State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Changsha 410082, China
    Zhongguo Jixie Gongcheng, 2008, 15 (1811-1814+1826): : 1811 - 1814
  • [8] Modal Optimization Design of Supporting Structure Based on the Improved Particle Swarm Algorithm
    Shijing D.
    Hongru C.
    Xudong W.
    Deshi W.
    Yongyong Z.
    International Journal of Engineering, Transactions A: Basics, 2022, 35 (04): : 740 - 749
  • [9] Integrated kitchen design and optimization based on the improved particle swarm intelligent algorithm
    Sun, Xin
    Ji, Xiaomin
    COMPUTATIONAL INTELLIGENCE, 2020, 36 (04) : 1638 - 1649
  • [10] Smart City Landscape Design Based on Improved Particle Swarm Optimization Algorithm
    Yao, Wenting
    Ding, Yongjun
    COMPLEXITY, 2020, 2020