原子磁力计脑磁图刚性头盔设计及适配范围量化评估

颜冰; 吉洁; 彭禹铭; 牛燕雄; 孙畅; 丁铭

doi:10.11999/JEIT260860

原子磁力计脑磁图刚性头盔设计及适配范围量化评估

doi: 10.11999/JEIT260860 cstr: 32379.14.JEIT260860

北京航空航天大学仪器科学与光电工程学院北京 100191

基金项目: 国家重点研发计划(2022YFC2403900)

详细信息

作者简介:
颜冰：男，博士生，研究方向脑磁图系统设计与关键元器件开发

彭禹铭：男，博士生，研究方向脑磁数据处理算法

牛燕雄：男，博士，教授，研究方向新型光电系统的设计、光电测试技术以及光电图像处理及目标识别技术

孙畅：女，博士，助理研究员，研究方向量子磁场传感与光纤激光

丁铭：女，博士，教授，研究方向心脑磁系统与临床应用

通讯作者:
丁铭　mingding@buaa.edu.cn

中图分类号: TP212.3; R741.044
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出版历程
- 收稿日期: 2026-06-16
- 修回日期: 2026-10-14
- 录用日期: 2026-10-15
- 网络出版日期: 2026-10-22

Design and Quantitative Evaluation of the Fitting Range of a Rigid Helmet for Optically Pumped Magnetometer Magnetoencephalography

School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing, 100191, China

摘要

摘要: 针对原子磁力计脑磁图(Optically Pumped Magnetometer Magnetoencephalography, OPM-MEG)系统中刚性头盔设计缺乏统一方法、固定尺寸头盔适配范围尚不明确的问题，本文提出一套面向OPM-MEG刚性头盔的多维度量化设计与适配范围评估方法。该方法以标准脑模型为基础，结合头盔内壁曲面构建、传感器阵列布局、头部尺寸变化模拟以及多指标探测性能评价，实现了从头盔结构设计到适配范围判定的定量化分析。本文以ICBM152标准脑模板为基础构建头盔内壁曲面，结合64通道磁场传感器阵列完成刚性头盔结构设计，并进一步建立头部尺度变化条件下的适配范围评估框架。通过构建不同头部尺度模型，采用前向场F范数和不可探测体素等指标，对固定尺寸刚性头盔的探测能力和空间覆盖范围进行定量分析，并以306通道超导量子干涉仪脑磁图(Superconducting Quantum Interference Device Magnetoencephalography, SQUID-MEG)系统作为参考基线进行比较。结果表明，随着头部尺寸减小，固定尺寸刚性头盔的前向场强度和有效探测范围均呈下降趋势。当头部尺度降低至标准头模型的0.66～0.69倍时，整体探测能力下降至参考SQUID-MEG系统水平；若采用空间覆盖率作为更严格判据，适配下限约为标准头模型的0.89倍。不可探测体素分析结果显示，在0.5～1.0倍标准头模型范围内，所设计头盔的整体探测性能仍优于参考SQUID-MEG系统；若以新增局部探测盲区不显著增加作为工程适配标准，则较优适配下限约为标准头模型的0.7倍。研究结果表明，所提出的多维度量化评估方法能够为OPM-MEG刚性头盔的标准化设计、型号规划和临床适用人群分析提供定量依据。
- 原子磁力计脑磁图 /
- 刚性头盔 /
- 适配范围 /
- F范数 /
- 不可探测体素
Abstract: Objective Magnetoencephalography (MEG) is a non-invasive technique for detecting weak magnetic fields generated by neuronal activity. Compared with conventional superconducting quantum interference device-based MEG systems, optically pumped magnetometer-based magnetoencephalography (OPM-MEG) allows sensors to operate at room temperature and to be placed closer to the scalp, thereby improving signal detection efficiency and supporting wearable system design. In practical OPM-MEG systems, rigid helmets can provide stable sensor positions and improve repeated measurement consistency. However, because head size varies across subjects, a fixed-size rigid helmet may increase the sensor-to-scalp distance for smaller heads, resulting in reduced forward-field strength and limited detectable coverage. Existing OPM-MEG helmet studies have mainly focused on system implementation and experimental validation, whereas quantitative methods for evaluating the size-adaptation range of fixed-size rigid helmets remain insufficient. This study proposes a standard-head-model-based design and multidimensional quantitative evaluation method for an OPM-MEG rigid helmet, aiming to provide guidance for helmet standardization, size planning, and engineering application. Methods The ICBM152 standard head model was used as the geometric reference for helmet design. The scalp surface was extracted and truncated according to the brain coverage requirement, and a smooth inner-wall surface was generated. Following the principles of maintaining basic wearing clearance, reducing the sensor-to-cortex distance, and ensuring whole-brain coverage, a 64-channel OPM-MEG sensor array was arranged to cover the frontal, parietal, temporal, and occipital regions. Based on the inner-wall surface and sensor positions, a rigid helmet structure with sensor slots and ear openings was constructed, considering structural stability, 3D-printing feasibility, and wearing comfort. To evaluate the size-adaptation range, the standard head model was defined as the 1.0-scale model, and six head-size models with scale factors of 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0 were generated. During scaling, the helmet and sensor array remained fixed, while the head model was proportionally resized with the top of the head kept unchanged. A 306-channel SQUID-MEG system was used as the reference baseline. The Frobenius norm of the lead field was used to evaluate forward-field detection capability, while undetectable voxels were used to quantify effective detectable coverage. Mean ratio, median ratio, spatial coverage rate, undetectable-voxel ratio, Jaccard similarity, newly undetectable voxels, and recovered detectable voxels were calculated for comparison. Results and Discussions The simulation results showed that head-size mismatch had a clear influence on the detection capability of the fixed-size OPM-MEG rigid helmet. As the head scale increased from 0.5 to 1.0, the Frobenius norm over the cortical surface increased continuously, indicating enhanced forward-field strength when cortical sources became closer to the fixed sensor array. The mean Frobenius norm increased from 1.81×10−9 at the 0.5-scale model to 2.82×10−9 at the 1.0-scale model, corresponding to 88.9% and 139.0% of the SQUID-MEG reference baseline, respectively. The median ratio showed a similar increase from 87.1% to 139.1%. Based on linear interpolation, the mean and median Frobenius norm ratios reached the SQUID-MEG reference level at head-scale factors of approximately 0.66 and 0.69, respectively. However, when spatial coverage was considered, a stricter threshold was obtained: a head-scale factor of approximately 0.89 was required for 95% of cortical source points to reach at least 95% of the reference detection capability. This indicates that global statistical indicators may reflect the average detection capability, whereas spatial coverage can more sensitively reveal local performance degradation. The undetectable-voxel analysis provided complementary evidence for detectable coverage. As the head scale decreased from 1.0 to 0.5, undetectable regions gradually expanded, mainly in lower and peripheral areas of the region of interest. The number of undetectable voxels increased from 1809 at the 1.0-scale model to 4396 at the 0.5-scale model, corresponding to 11.56% and 28.10% of all voxels, respectively. In contrast, the SQUID-MEG reference system had 5063 undetectable voxels, accounting for 32.36%. Therefore, within the tested range of 0.5–1.0, the designed OPM-MEG rigid helmet maintained fewer undetectable voxels than the SQUID-MEG baseline. Nevertheless, newly undetectable voxels appeared when the head scale decreased to 0.6 and 0.5, indicating local degradation of detectable coverage. These results suggest that the size-adaptation range of a fixed-size rigid helmet is not a single fixed value, but depends on the selected evaluation criterion and application scenario. Conclusions This study proposes a standard-head-model-based design and quantitative evaluation method for the size-adaptation range of an OPM-MEG rigid helmet. A 64-channel rigid helmet was designed using the ICBM152 standard head model, and its performance was evaluated under different head-size conditions by combining forward-field strength and detectable-coverage metrics. When the mean and median Frobenius norm ratios were used as overall performance criteria, the lower adaptation limits were approximately 0.66 and 0.69 times the standard head model. When spatial coverage was used as a stricter criterion, the lower limit increased to approximately 0.89. Based on the undetectable-voxel analysis, the helmet maintained better overall detectable coverage than the SQUID-MEG reference within the tested range, while the preferred engineering adaptation limit was approximately 0.7. These findings provide quantitative guidance for OPM-MEG rigid helmet standardization, size planning, applicable population analysis, and engineering application.
- OPM-MEG /
- Rigid helmet design /
- size-adaptation range /
- Frobenius norm /
- Undetectable voxel

HTML全文

图 1 OPM-MEG系统的头盔设计流程

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图 2 64通道OPM-MEG刚性头盔结构示意图

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图 3 不同头部尺度下F范数的空间分布及其相对SQUID-MEG参考基线的变化结果

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图 4 不同头部尺度下F范数的统计分布结果

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图 5 相对参考基线的归一化性能指标

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图 6 不同头部尺度下不可探测体素的空间分布及其相对SQUID-MEG参考基线的差异结果

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图 7 不同头部尺度下不可探测体素的总体统计结果

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图 8 不同头部尺度下不可探测体素相对于SQUID-MEG参考基线的归一化比较结果

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