Optimization Strategies for Gradient Magnetic Field Uniformity in Magnetic Resonance Imaging Systems

Liang-Gang Sun ( Characteristic Medical Center of PAP, Tianjin 300162, China )

https://doi.org/10.37155/3060-8708-0204-5

Abstract

The gradient magnetic field is a core component of magnetic resonance imaging (MRI) systems, and its uniformity is directly related to spatial resolution, signal-to-noise ratio, and lesion detection accuracy, making it a key factor constraining imaging quality. At present, the gradient magnetic field in clinical and research MRI systems is susceptible to disturbances arising from coil design, electromagnetic interference, and mechanical deformation, and its uniformity often fails to meet the requirements of high-precision imaging. To address this issue, this paper systematically reviews the evaluation indices and influencing mechanisms of gradient magnetic field uniformity, and proposes targeted optimization strategies from four dimensions: coil structure design, electromagnetic compatibility, mechanical stability, and post-processing calibration and compensation, including novel coil design approaches.

Keywords

Magnetic resonance imaging; gradient magnetic field; uniformity optimization; eddy current compensation; conformal technology

Full Text

PDF

References

[1] Xie Wanming, Xie Bing, Song Xiaomin, et al. Research progress and clinical applications of 5.0 T magnetic resonance imaging technology [J]. Medical Equipment, 2024, 37(16): 162–164.
[2] Hao Qiaoli, Wang Liangwei, Zhao Yanqiang, et al. Current status and implications of global basic research on magnetic resonance imaging [J]. World Scientific Research and Development, 2023, 45(4): 518–529.
[3] Ma Jing, Qi Shimin, Wang Ying, et al. An automatic slice thickness detection software for magnetic resonance imaging [J]. China Science and Technology Information, 2025(8): 117–119.
[4] Chu Chengchen, Ji Zhiyong, Li Bin. Research on performance evaluation of magnetic resonance imaging equipment based on information entropy [J]. China Medical Equipment, 2022, 19(2): 40–45.
[5] Wei Wei, Lü Xin, Ma Menghang, et al. Glioma segmentation algorithm based on multimodal magnetic resonance imaging and scale feature differences [J]. Journal of Xi’an Polytechnic University, 2025, 39(3): 129–137.
[6] Song Chunhu, Chen Qiaoyan, Che Shao, et al. Research progress on ultra-flexible radiofrequency receiving coils for magnetic resonance imaging [J]. Life Science Instruments, 2022, 20(2): 4–16. DOI:10.11967/2022200401.
[7] Xiao Shun, Chu Chengchen, Wang Yuanbing, et al. Progress analysis of compressed sensing techniques in magnetic resonance imaging technology [J]. China Medical Devices, 2021, 36(11): 4–9.

Copyright © 2026 Liang-Gang Sun Creative Commons License Publishing time:2025-08-30
This work is licensed under a Creative Commons Attribution 4.0 International License