Affiliations: [a] State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing, China | [b] School of Physics and Electronic Engineering, Chongqing Normal University, Chongqing, China
Correspondence:
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Corresponding authors: Pan Guo, School of Physics and Electronic Engineering, Chongqing Normal University, Chongqing 401331, China. Tel.: +86 135 2747 2600; E-mail: guopan@cqnu.edu.cn. Zheng Xu, State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China. Tel.: +86 135 2747 5882; E-mail: xuzheng@cqu.edu.cn
Abstract: Comparing with superconducting and permanent magnet magnetic resonance imaging (MRI) system, ultra-low- field MRI (uMRI) system has a much lower weight, which can be used in some special scenes, such as real-time image monitoring on bedside or in the ambulance for human brain disease (stroke). In order to make the uMRI system more compact, lighter, and smaller, in this work, we proposed a hybrid method for designing uMRI electromagnet. The method consists of integer linear programming (ILP) and nonlinear optimization, which were bridged by empirical principles. ILP was used to determine the coil distribution of uMRI electromagnet. Considering that the coil distribution, acquired with ILP, is not conducive for magnet processing, two empirical principles were applied to adjust the coil distribution with decreasing electromagnet performance as little as possible. At last, nonlinear optimization was exploited to eliminate the negative influence on magnetic field distribution which is introduced by coil adjustment and make electromagnet satisfy the design requirement of magnetic field homogeneity. This hybrid method combines mathematical models and empirical principles, which provides a way to design electromagnet efficiently. A small electromagnet with homogeneity of 10 ppm in a spherical region of interest (ROI) with diameter of 80 mm is designed and built in this paper.
Keywords: Ultra-low-field MRI, electromagnet design, integer linear programming, nonlinear optimization
