Advanced Search
Turn off MathJax
Article Contents
GAO Qiannan, ZHANG Jiayu, ZHU Yingen, WANG Wenjin, JI Jiansong, JI Xiaoyue. Split-architecture Non-contact Optical Seismocardiography Triggering System for Cardiac Magnetic Resonance Imaging[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251098
Citation: GAO Qiannan, ZHANG Jiayu, ZHU Yingen, WANG Wenjin, JI Jiansong, JI Xiaoyue. Split-architecture Non-contact Optical Seismocardiography Triggering System for Cardiac Magnetic Resonance Imaging[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251098

Split-architecture Non-contact Optical Seismocardiography Triggering System for Cardiac Magnetic Resonance Imaging

doi: 10.11999/JEIT251098 cstr: 32379.14.JEIT251098
  • Received Date: 2025-10-15
  • Accepted Date: 2026-01-12
  • Rev Recd Date: 2026-01-10
  • Available Online: 2026-01-27
  •   Objective  Cardiac-cycle synchronization is required in Cardiovascular Magnetic Resonance (CMR) to reduce motion artifacts and maintain quantitative accuracy. At high field strengths, ElectroCardioGram (ECG) triggering is affected by magnetohydrodynamic effects and scanner-related ElectroMagnetic Interference (EMI). Electrode placement and lead routing also increase setup burden. Contact-based mechanical sensors still require skin contact, and optical photoplethysmography can introduce long physiological delay. A fully contactless, EMI-robust mechanical surrogate is therefore needed. This study develops a split-architecture, non-contact optical SeismoCardioGraphy (SCG) triggering system for CMR and evaluates its availability, beatwise detection performance, and timing characteristics under practical body-coil coverage.  Methods  The split-architecture system consists of a near-magnet optical acquisition unit and a far-magnet computation-and-triggering unit connected by fiber-optic links to minimize conductive pathways near the scanner (Fig. 2). The acquisition unit uses a defocused industrial camera and laser illumination to record speckle-pattern dynamics over the anterior chest without physical contact (Fig. 3). Dense optical flow is computed in a chest region of interest, and the displacement field is projected onto a principal motion direction to form a one-dimensional SCG sequence (Fig. 4). Drift suppression, smoothing, and short-window normalization are applied. Trigger timing is refined with a valley-constrained gradient search within a physiologically bounded window to reduce spurious detections and improve temporal consistency (Fig. 4). A benchmark dataset is collected from 20 healthy volunteers under three coil configurations: no body coil, an ultra-flexible body coil, and a rigid body coil (Fig. 5, Fig. 6, Table 3). ECG serves as the reference, and CamPPG and radar are recorded for comparison. Beatwise precision, recall, and F1 score are computed against ECG R peaks, and availability is reported as the fraction of usable segments under unified quality criteria (Table 4). Backward and forward physiological delays and delay variability are summarized across subjects and coil conditions (Table 5, Table 6). Key windowing and refractory parameters are assessed for sensitivity (Table 2). Runtime is measured to evaluate real-time feasibility, including the cost of dense optical flow and the overhead of one-dimensional processing and triggering (Table 7).  Results and Discussions  Under no-coil and ultra-flexible-coil conditions, the optical SCG trigger achieves high availability (about 97.6%) and strong beatwise performance. F1 reaches about 0.91 under the ultra-flexible coil (Table 4, Table 5). The backward physiological delay remains on the order of several tens of milliseconds, and delay jitter is generally within a few tens of milliseconds (Table 5, Table 6). Under the rigid body coil, performance decreases sharply. Mechanical decoupling between the coil surface and the chest wall weakens and distorts the vibration signature, which blurs AO-related features and increases false triggers (Fig. 1). This effect appears as lower precision and F1 and as a shift toward longer and more variable delays compared with the other conditions (Table 4, Table 6). Relative to CamPPG, which reflects peripheral blood-volume dynamics and typically lags further behind the ECG R peak, the optical SCG surrogate provides a more proximal mechanical marker with reduced trigger phase lag (Fig. 9, Table 5). EMI robustness is supported by representative segments: ECG waveforms show visible distortion under interference, whereas the optical SCG surrogate remains interpretable because acquisition and transmission near the scanner are fully optical and electrically isolated (Fig. 8). Parameter analysis supports a moderate processing window and a 0.5 s minimum interbeat interval as a stable choice across subjects (Table 2). Runtime analysis shows that dense optical flow dominates computational cost, whereas one-dimensional processing and triggering add little overhead. Throughput exceeds the acquisition frame rate, supporting real-time triggering (Table 7).  Conclusions  A split-architecture, non-contact optical SCG triggering system is developed and validated under three representative body-coil configurations. Fiber-optic separation between near-magnet acquisition and far-magnet processing improves EMI robustness while maintaining real-time trigger output. High availability, strong beatwise performance, and short physiological delay are demonstrated under no-coil and ultra-flexible-coil conditions (Table 4, Table 5). Rigid-coil coverage exposes a clear limitation caused by reduced mechanical coupling, which motivates further optimization for mechanically decoupled or heavily occluded scenarios (Fig. 1, Table 6).
  • loading
  • [1]
    BARNWELL J D, KLEIN J L, STALLINGS C, et al. Image-guided optimization of the ECG trace in cardiac MRI[J]. The International Journal of Cardiovascular Imaging, 2012, 28(3): 587–593. doi: 10.1007/s10554-011-9865-7.
    [2]
    国家药监局. 国家药监局关于发布优化全生命周期监管支持高端医疗器械创新发展有关举措的公告[A/OL]. (2025-07-05). https://www.nmpa.gov.cn/xxgk/ggtg/ylqxggtg/ylqxqtggtg/20250703163951182.html, 2025.

    National Medical Products Administration (NMPA). Announcement on issuing measures to optimize whole-life-cycle regulation to support the innovative development of high-end medical devices[A/OL]. (2025-07-05). https://www.nmpa.gov.cn/xxgk/ggtg/ylqxggtg/ylqxqtggtg/20250703163951182.html, 2025.
    [3]
    上海市人民政府. 上海市促进高端医疗器械产业全链条发展行动方案[A/OL]. (2025-09-15). https://www.shanghai.gov.cn/nw12344/20250915/91ccfe1a601d40ecbb579034a030cfa8.html, 2025.

    Shanghai Municipal People’s Government. Action plan for promoting the full-chain development of the high-end medical device industry[A/OL]. (2025-09-15). https://www.shanghai.gov.cn/nw12344/20250915/91ccfe1a601d40ecbb579034a030cfa8.html, 2025.
    [4]
    TASDELEN B, YAGIZ E, CINBIS B R, et al. Contactless cardiac gating at 0.55T using high-amplitude pilot tone with interference cancellation (HAPTIC)[J]. Magnetic Resonance in Medicine, 2025, 94(3): 1182–1190. doi: 10.1002/mrm.30528.
    [5]
    SNYDER C J, DELABARRE L, METZGER G J, et al. Initial results of cardiac imaging at 7 tesla[J]. Magnetic Resonance in Medicine, 2009, 61(3): 517–524. doi: 10.1002/mrm.21895.
    [6]
    KRUG J, ROSE G, STUCHT D, et al. Limitations of VCG based gating methods in ultra high field cardiac MRI[J]. Journal of Cardiovascular Magnetic Resonance, 2013, 15(S1): W19. doi: 10.1186/1532-429X-15-S1-W19.
    [7]
    ROSENZWEIG S, SCHOLAND N, HOLME H C M, et al. Cardiac and respiratory self-gating in radial MRI using an adapted singular spectrum analysis (SSA-FARY)[J]. IEEE Transactions on Medical Imaging, 2020, 39(10): 3029–3041. doi: 10.1109/TMI.2020.2985994.
    [8]
    CROWE M E, LARSON A C, ZHANG Qiang, et al. Automated rectilinear self-gated cardiac cine imaging[J]. Magnetic Resonance in Medicine, 2004, 52(4): 782–788. doi: 10.1002/mrm.20212.
    [9]
    NIJM G M, SAHAKIAN A V, SWIRYN S, et al. Comparison of self-gated cine MRI retrospective cardiac synchronization algorithms[J]. Journal of Magnetic Resonance Imaging, 2008, 28(3): 767–772. doi: 10.1002/jmri.21514.
    [10]
    LARSON A C, WHITE R D, LAUB G, et al. Self-gated cardiac cine MRI[J]. Magnetic Resonance in Medicine, 2004, 51(1): 93–102. doi: 10.1002/mrm.10664.
    [11]
    ZHU Yingen, GE Yao, WEI Qiang, et al. Camera-based Bi-modal PPG-SCG: Sleep privacy-protected contactless vital signs monitoring[J]. IEEE Internet of Things Journal, 2025, 12(4): 4375–4389. doi: 10.1109/JIOT.2024.3484752.
    [12]
    LIU Lin, YU Dongfang, LU Hongzhou, et al. Camera-based seismocardiogram for heart rate variability monitoring[J]. IEEE Journal of Biomedical and Health Informatics, 2024, 28(5): 2794–2805. doi: 10.1109/JBHI.2024.3370394.
    [13]
    ZHU Y, LAI H, MO H, et al. Camera-SCG based cardiac gating for magnetic resonance imaging: A feasibility study[J].
    [14]
    WANG Wenjin, WEISS S, DEN BRINKER A C, et al. Fundamentals of camera-PPG based magnetic resonance imaging[J]. IEEE Journal of Biomedical and Health Informatics, 2022, 26(9): 4378–4389. doi: 10.1109/JBHI.2021.3136603.
    [15]
    KORDING F, SCHOENNAGEL B, LUND G, et al. Doppler ultrasound compared with electrocardiogram and pulse oximetry cardiac triggering: A pilot study[J]. Magnetic Resonance in Medicine, 2015, 74(5): 1257–1265. doi: 10.1002/mrm.25502.
    [16]
    VOLLBRECHT T M, BISSELL M M, KORDING F, et al. Fetal cardiac MRI using Doppler US gating: Emerging technology and clinical implications[J]. Radiology: Cardiothoracic Imaging, 2024, 6(2): e230182. doi: 10.1148/ryct.230182.
    [17]
    MARTINEK R, BRABLIK J, KOLARIK J, et al. A low-cost system for seismocardiography-based cardiac triggering: A practical solution for cardiovascular magnetic resonance imaging at 3 tesla[J]. IEEE Access, 2019, 7: 118608–118629. doi: 10.1109/ACCESS.2019.2936184.
    [18]
    ZHOU Zhiqin, HUANG Jia, LI Haozhe, et al. Camera seismocardiogram based monitoring of left ventricular ejection time[J]. IEEE Transactions on Biomedical Engineering, 2025, 72(9): 2609–2622. doi: 10.1109/TBME.2025.3548090.
    [19]
    WANG Zi, XIAO Min, ZHOU Yirong, et al. Deep separable spatiotemporal learning for fast dynamic cardiac MRI[J]. IEEE Transactions on Biomedical Engineering, 2025, 72(12): 3642–3654. doi: 10.1109/TBME.2025.3574090.
    [20]
    LI Ning, TOUS C, DIMOV I P, et al. Design of a low-cost, self-adaptive and MRI-compatible cardiac gating system[J]. IEEE Transactions on Biomedical Engineering, 2023, 70(11): 3126–3136. doi: 10.1109/TBME.2023.3280348.
    [21]
    ADCOX K, ADLER S S, AFANASIEV S, et al. Formation of dense partonic matter in relativistic nucleus-nucleus collisions at RHIC: Experimental evaluation by the PHENIX collaboration[J]. Nuclear Physics A, 2005, 757(1/2): 184–283. doi: 10.1016/j.nuclphysa.2005.03.086.
    [22]
    SPICHER N, MADERWALD S, LADD M E, et al. Heart rate monitoring in ultra-high-field MRI using frequency information obtained from video signals of the human skin compared to electrocardiography and pulse oximetry[J]. Current Directions in Biomedical Engineering, 2015, 1(1): 69–72. doi: 10.1515/cdbme-2015-0018.
    [23]
    DONG Zhekang, JI Xiaoyue, LAI C S, et al. Design and implementation of a flexible neuromorphic computing system for affective communication via memristive circuits[J]. IEEE Communications Magazine, 2023, 61(1): 74–80. doi: 10.1109/MCOM.001.2200272.
    [24]
    GANTI V G, GAZI A H, AN S, et al. Wearable seismocardiography-based assessment of stroke volume in congenital heart disease[J]. Journal of the American Heart Association, 2022, 11(18): e026067. doi: 10.1161/JAHA.122.026067.
    [25]
    DONG Zhekang, ZHU Liyan, ZHOU Shiqi, et al. FE-SpikeFormer: A camera-based facial expression recognition method for hospital health monitoring[J]. IEEE Journal of Biomedical and Health Informatics, 2025: 1–11. doi: 10.1109/JBHI.2025.3589267.
    [26]
    董哲康, 杜晨杰, 林辉品, 等. 基于多通道忆阻脉冲耦合神经网络的多帧图像超分辨率重建算法[J]. 电子与信息学报, 2020, 42(4): 835–843. doi: 10.11999/JEIT190868.

    DONG Zhekang, DU Chenjie, LIN Huipin, et al. Multi-channel memristive pulse coupled neural network based multi-frame images super-resolution reconstruction algorithm[J]. Journal of Electronics & Information Technology, 2020, 42(4): 835–843. doi: 10.11999/JEIT190868.
    [27]
    董哲康, 钱智凯, 周广东, 等. 基于忆阻的全功能巴甫洛夫联想记忆电路的设计、实现与分析[J]. 电子与信息学报, 2022, 44(6): 2080–2092. doi: 10.11999/JEIT210376.

    DONG Zhekang, QIAN Zhikai, ZHOU Guangdong, et al. Memory circuit design, implementation and analysis based on memristor full-function Pavlov associative[J]. Journal of Electronics & Information Technology, 2022, 44(6): 2080–2092. doi: 10.11999/JEIT210376.
    [28]
    周师琦, 王俊帆, 赖俊升, 等. 结合贝叶斯Autoformer的多维自适应短期电力负荷概率预测方法[J]. 电子与信息学报, 2024, 46(12): 4432–4440. doi: 10.11999/JEIT240398.

    ZHOU Shiqi, WANG Junfan, LAI Junsheng, et al. Multi-view adaptive probabilistic load forecasting combing Bayesian Autoformer network[J]. Journal of Electronics & Information Technology, 2024, 46(12): 4432–4440. doi: 10.11999/JEIT240398.
    [29]
    SPICHER N, KUKUK M, MADERWALD S, et al. Initial evaluation of prospective cardiac triggering using photoplethysmography signals recorded with a video camera compared to pulse oximetry and electrocardiography at 7T MRI[J]. Biomedical Engineering Online, 2016, 15(1): 126. doi: 10.1186/s12938-016-0245-3.
    [30]
    LADROVA M, MARTINEK R, NEDOMA J, et al. Monitoring and synchronization of cardiac and respiratory traces in magnetic resonance imaging: A review[J]. IEEE Reviews in Biomedical Engineering, 2022, 15: 200–221. doi: 10.1109/RBME.2021.3055550.
    [31]
    TOGAWA T, OKAI O, and OSHIMA M. Observation of blood flow E. M. F. in externally applied strong magnetic field by surface electrodes[J]. Medical and Biological Engineering, 1967, 5(2): 169–170. doi: 10.1007/BF02474505.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(8)  / Tables(6)

    Article Metrics

    Article views (83) PDF downloads(5) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return