波谱学杂志

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基于多通道并行接收与Transformer重建的NMR环境噪声抑制方法

赵静1,鲍庆嘉2,3,张志2,3,陈罡2,3,吴肇博1,黄臻1*,刘朝阳2,3#   

  1. 1. 武汉轻工大学 电气与电子工程学院,湖北 武汉 430023;2. 中国科学院精密测量科学与技术创新研究院,磁共振波谱与成像全国重点实验室,武汉磁共振中心,湖北 武汉 430071;3. 中国科学院大学,北京 100049
  • 收稿日期:2025-05-19 修回日期:2025-06-04 出版日期:2025-06-04 在线发表日期:2025-06-04
  • 通讯作者: 黄臻;刘朝阳 E-mail:zhenhuang@whpu.edu.cn;chyliu@apm.ac.cn

NMR Environmental Noise Suppression Method Based on Multi-Channel Parallel Reception and Transformer Reconstruction

ZHAO Jing1,BAO Qingjia2,3,ZHANG Zhi2,3,CHEN Gang2,3,WU Zhaobo1,HUANG Zhen1*,LIU Chaoyang2,3#   

  1. 1. School of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China; 2. State Key Laboratory of Magnetic Resonance Spectroscopy and Imaging, National Center for Magnetic Resonance in Wuhan, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China; 3. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2025-05-19 Revised:2025-06-04 Published:2025-06-04 Online:2025-06-04
  • Contact: HUANG Zhen; LIU Chaoyang E-mail:zhenhuang@whpu.edu.cn;chyliu@apm.ac.cn

摘要: 便携式核磁共振(NMR)波谱仪在实际应用中常受外部电磁干扰(EMI)的影响,导致信噪比低,进而影响物质分析和结构解析的准确性.为了解决这一问题,本文提出了一种基于多参考线圈检测外部电磁环境噪声,并利用深度学习方法预测主接收线圈中环境噪声的新方法.该方法通过多通道接收线圈并行获取周围环境的电磁特性,并将这些信号输入到多通道Transformer重构(MCTR)网络中.MCTR网络通过捕捉环境电磁信号的长程依赖关系,实时预测环境噪声,并从原始NMR信号中去除预测的噪声,从而实现噪声抑制.实验结果表明,该方法在仿真数据和实际NMR实验数据中均表现出良好的噪声抑制效果,显著提高了便携式NMR仪器的检测性能.与传统噪声抑制方法相比,本方法能够更高效、精确地抑制环境噪声,并保持信号的高质量,具有较强的鲁棒性.该方法为便携式NMR仪器在复杂电磁干扰环境中的应用提供了有效的技术支持,并有望推动NMR技术在现场检测等领域的发展.

关键词: 便携式NMR, 多通道并行接收, 参考线圈, 噪声抑制, Transformer

Abstract:  Portable nuclear magnetic resonance (NMR) spectrometers often suffer from external electromagnetic interference (EMI), leading to low signal-to-noise ratios (SNR) and reduced accuracy in material analysis. To address this problem, we propose a novel method that uses multiple reference coils to monitor environmental EMI noise and a multi-channel Transformer reconstruction (MCTR) network to predict and suppress noise in the main receiver coil. By capturing spatial electromagnetic patterns via parallel channels and modeling long-range dependencies, the MCTR network effectively estimates and removes noise from raw NMR signals in real time. Experiments on both simulated and real NMR data demonstrate that this approach significantly enhances noise suppression and maintains high signal fidelity, outperforming traditional denoising techniques. This method improves the robustness and performance of portable NMR systems in EMI-prone environments, supporting broader field applications.

Key words: Portable NMR, Multi-channel Parallel Reception, Reference Coils, Noise Suppression, Transformer

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