Electromagnetic Interference Suppression Method for an Unshielded Portable Ultra-Low Field Magnetic Resonance Imaging Scanner
Yang Lei1, He Wei1, He Yucheng2,3, Wu Jiamin2,4, Xu Zheng1
1. School of Electrical Engineering Chongqing University Chongqing 400044 China; 2. Shenzhen Academy of Aerospace Technology Shenzhen 518057 China; 3. School of Life Northwestern Polytechnical University Xi’an 710072 China; 4. School of Mechatronics Engineering Harbin Institute of Technology Harbin 150001 China
Abstract：The ultra-low field (ULF) magnetic resonance imaging (MRI) scanner features much lower cost, much lighter weight, much smaller footprint, and fewer safety concerns than high-filed ones. However, the intrinsic low signal strength of ULF MRI scanners makes them more easily impacted by electromagnetic interference (EMI). The conventional electromagnetic shielding methods passively block the EMI signal but restrict the mobility of ULF MRI scanners. Therefore, the passive electromagnetic shielding should be discarded. The reference channel-based active EMI suppression methods outperform others for better suppression rate and electromagnetic environment adaptation. However, the present deep-learning reference channel-based methods have drawbacks of demanding additional scan time and modification of pulse sequences. This paper proposes a signal-analysis reference channel-based active EMI suppression method with the following features. (1) The EMI detecting coils with appropriate locations avoid sample MR signal. (2) The EMI-detecting coils of reference channels and RF-receiving coils of the MR channel acquire the reference EMI signal and contaminated MR signals synchronously. (3) The reference EMI signals estimate EMI signals in the MR channel (primary EMI signal). (4) The denoised MR signal is obtained by subtracting the estimated primary EMI signal from the contaminated MR signal. The EMI transmission paths are analyzed, and human-body coupling is the main path. The ‘ring’ shaped EMI pickup coil is designed to sample human body coupled EMI signal. The ‘ring’ shaped EMI pickup coil, along with one surface coil and one solenoid coil, constitutes three reference channels. It is found that reference EMI signals acquired in a limited time interval also contribute to estimating EMI signals in the MR channel precisely. Then, the concept termed related window is proposed. A data point at a specific time in the k-space of the MR channel is estimated by the data points ink-spaces of all reference channels within the related window. The periphery data of the k-spaces of each channel are selected as calibration data to calculate the transfer matrix for reference EMI signals without the calibration measurement and modification of pulse sequences. The calibration target matrix is constructed from the calibration data of the MR channel, and the calibration source matrix is formed by the calibration data of reference channels in the related window. The two matrixes are decomposed into multiple layers by wavelet analysis. The least-square method calculates the transfer matrix in each layer. The reconstruction source matrix is constructed from k-spaces of reference channels within related windows and decomposed into the same layers. The primary EMI signal is estimated by the reconstruction source matrix and transfer factors in each layer. The denoised MR signal is acquired by subtracting the estimated EMI signal of the MR channel from the contaminated MR signal. The active EMI suppression system is fitted to a home-built portable unshielded 50 mT MRI scanner. The one-dimensional signal analysis and in-vivo scan are carried out in a building located in an industrial park without EMI restrictions. The image results show that the signal-to-noise rate (SNR) is improved by 3.9-fold, and the active EMI suppression system ensures that the ULF MRI scanner works normally without shielding.
杨磊, 何为, 贺玉成, 吴嘉敏, 徐征. 开放式超低场移动磁共振系统的电磁干扰抑制方法[J]. 电工技术学报, 2024, 39(15): 4708-4717.
Yang Lei, He Wei, He Yucheng, Wu Jiamin, Xu Zheng. Electromagnetic Interference Suppression Method for an Unshielded Portable Ultra-Low Field Magnetic Resonance Imaging Scanner. Transactions of China Electrotechnical Society, 2024, 39(15): 4708-4717.
[1] 李晓南, 任雯廷, 刘国强, 等. 高分辨率磁共振电特性成像及脑肿瘤诊断初步研究[J]. 电工技术学报, 2021, 36(18): 3860-3866. Li Xiaonan, Ren Wenting, Liu Guoqiang, et al.Preliminary conductivity reconstruction by high- resolution magnetic resonance electrical properties tomography for brain tumor diagnosis[J]. Transactions of China Electrotechnical Society, 2021, 36(18): 3860-3866. [2] 冯焕, 姜晖, 王雪梅. 功能磁共振成像在肿瘤学领域的应用[J]. 电工技术学报, 2021, 36(4): 705-716. Feng Huan, Jiang Hui, Wang Xuemei.Application of functional magnetic resonance imaging in the field of oncology[J]. Transactions of China Electrotechnical Society, 2021, 36(4): 705-716. [3] 陈顺中, 王秋良, 孙万硕, 等. 3T动物磁共振成像传导冷却超导磁体研究[J]. 电工技术学报, 2023, 38(4): 879-888. Chen Shunzhong, Wang Qiuliang, Sun Wanshuo, et al.The study of a 3T conduction-cooled superconducting magnet for animal magnetic resonance imaging[J]. Transactions of China Electrotechnical Society, 2023, 38(4): 879-888. [4] Liney G.MRI in clinical practice[M]. New York, USA: Springer Science & Business Media, 2007. [5] Sarracanie M, LaPierre C D, Salameh N, et al. Low- cost high-performance MRI[J]. Scientific Reports, 2015, 5(1): 15177. [6] 万裁, 何为, 沈晟, 等. 超低场磁共振膝关节正交接收线圈设计[J]. 电工技术学报, 2024, 39(7): 1923-1931. Wan Cai, He Wei, Shen Sheng, et al.Design of a quadrature receive coil for ultra-low-field knee magnetic resonance imaging[J]. Transactions of China Electrotechnical Society, 2024, 39(7): 1923-1931. [7] 徐显能, 徐征. 印制电路板射频线圈的部分元等效电路研究[J]. 电工技术学报, 2022, 37(17): 4284-4293. Xu Xianneng, Xu Zheng.Research on the partial element equivalent circuit of radio frequency coil on the printed circuit board[J]. Transactions of China Electrotechnical Society, 2022, 37(17): 4284-4293. [8] Sheth K N, Mazurek M H, Yuen M M, et al.Assessment of brain injury using portable, low-field magnetic resonance imaging at the bedside of critically ill patients[J]. JAMA Neurology, 2020, 78(1): 41-47. [9] Prabhat A M, Crawford A L, Mazurek M H, et al.Methodology for low-field, portable magnetic resonance neuroimaging at the bedside[J]. Frontiers in Neurology, 2021, 12: 760321. [10] Cooley C Z, McDaniel P C, Stockmann J P, et al. A portable scanner for magnetic resonance imaging of the brain[J]. Nature Biomedical Engineering, 2021, 5(3): 229-239. [11] Koonjoo N, Zhu B, Bagnall G C, et al.Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction[J]. Scientific Reports, 2021, 11(1): 8248. [12] Padormo F, Cawley P, Dillon L, et al.In vivo T1 mapping of neonatal brain tissue at 64 mT[J]. Magnetic Resonance in Medicine, 2023, 89(3): 1016-1025. [13] Yuen M M, Prabhat A M, Mazurek M H, et al. Portable, low-field magnetic resonance imaging enables highly accessible and dynamic bedside evaluation of ischemic stroke[J]. Science Advances, 2022, 8(16): eabm3952. [14] He Yucheng, He Wei, Tan Liang, et al.Use of 2.1 MHz MRI scanner for brain imaging and its preliminary results in stroke[J]. Journal of Magnetic Resonance, 2020, 319: 106829. [15] O’Reilly T, Teeuwisse W M, de Gans D, et al. In vivo 3D brain and extremity MRI at 50 mT using a permanent magnet Halbach array[J]. Magnetic Resonance in Medicine, 2021, 85(1): 495-505. [16] Liu Bin, Sang Xinzhu, Xing Shujun, et al.Noise suppression in brain magnetic resonance imaging based on non-local means filter and fuzzy cluster[J]. Optik, 2015, 126(21): 2955-2959. [17] Zaroubi S, Goelman G.Complex denoising of MR data via wavelet analysis: application for functional MRI[J]. Magnetic Resonance Imaging, 2000, 18(1): 59-68. [18] Campbell-Washburn A E, Atkinson D, Nagy Z, et al. Using the robust principal component analysis algorithm to remove RF spike artifacts from MR images[J]. Magnetic Resonance in Medicine, 2016, 75(6): 2517-2525. [19] Chen Haiguang, Avram H, Kaufman L, et al.T2 restoration and noise suppression of hybrid MR images using wiener and linear prediction techniques[J]. IEEE Transactions on Medical Imaging, 1994, 13(4): 667-676. [20] 赵杰, 周智鹏, 高玄飞, 等. 正弦采样信号中单一脉冲干扰信号的快速剔除实践方法[J]. 电气技术, 2021, 22(3): 52-55. Zhao Jie, Zhou Zhipeng, Gao Xuanfei, et al.A practical method for the rapid elimination of a single pulse interference signal from sinusoidal sampling signals[J]. Electrical Engineering, 2021, 22(3): 52-55. [21] Srinivas S A, Cauley S F, Stockmann J P, et al.External dynamic interference estimation and removal (EDITER) for low field MRI[J]. Magnetic Resonance in Medicine, 2022, 87(2): 614-628. [22] Liu Yilong, Leong A T L, Zhao Yujiao, et al. A low- cost and shielding-free ultra-low-field brain MRI scanner[J]. Nature Communications, 2021, 12(1): 7238. [23] Su Jiasheng, Pellicer-Guridi R, Edwards T, et al.A CNN based software gradiometer for electromagnetic background noise reduction in low field MRI applications[J]. IEEE Transactions on Medical Imaging, 2022, 41(5): 1007-1016. [24] 张婷婷, 于明, 李宾, 等. 基于Wavelet降噪和支持向量机的锂离子电池容量预测研究[J]. 电工技术学报, 2020, 35(14): 3126-3136. Zhang Tingting, Yu Ming, Li Bin, et al.Capacity prediction of lithium-ion batteries based on Wavelet noise reduction and support vector machine[J]. Transactions of China Electrotechnical Society, 2020, 35(14): 3126-3136. [25] Massey F J Jr. The Kolmogorov-Smirnov test for goodness of fit[J]. Journal of the American Statistical Association, 1951, 46(253): 68-78. [26] Nesterov Y.Introductory lectures on convex optimization: a basic course[M]. New York, USA: Springer Science & Business Media, 2014. [27] Gu Xiangqian, Kang Hongwen, Cao Hongxing.The least-square method in complex number domain[J]. Progress in Natural Science, 2006, 16(3): 307-312.
2024, Vol. 39 
