Gang Liu

High-sensitivity miniature optically pumped magnetometers (OPMs) enable noninvasive magnetocardiography (MCG) with flexible sensor configurations. Accurate magnetic source imaging (MSI) critically depends on precise registration between MCG measurements and anatomical computed tomography (CT) data, while registration errors can substantially degrade source localization accuracy. In this study, we systematically analyze the data acquisition and coordinate transformation processes to identify major sources of registration error in OPM-MCG systems. We show that existing approaches often overlook sensor pose deviations introduced by substituting the manufactured sensor array with an ideal design model during registration. To address this limitation, we propose an optical image–based sensor array calibration method using the local tangent plane distance (LTPD) measure and a sensor face filtering (SFF) algorithm. The proposed method directly extracts sensor positions and orientations without relying on predefined array parameters, thereby correcting errors caused by sensor assembly inaccuracies and array deformation. Experimental results demonstrate a mean position error of 0.53 mm and an orientation error of 0.84° for the sensitive axis. In addition, we investigate the effects of data volume and registration algorithm selection on transformation accuracy, revealing their critical roles in achieving high-precision registration. The optimized registration pipeline improves both accuracy and computational efficiency, facilitating reliable MSI for both rigid and wearable OPM-MCG systems.

The sensitivity and long‐term stability of the K–Rb– comagnetometer are significantly influenced by the signal‐to‐noise ratio (SNR) of the laser detection system. For the first time, this article establishes a quantitative relationship between probe laser power density (PLPD) and the output signal, while also providing a systematic classification of noise sources contributing to the detection signal. Based on this analysis, an SNR model for the comagnetometer's output is developed, and a method to enhance the performance of the K–Rb– comagnetometer through PLPD optimization is proposed. Experimental results demonstrate that increasing the probe laser beam diameter enhances the scale factor by more than threefold compared to the conventional 1 mm beam diameter. At the optimal PLPD point, the comagnetometer's SNR at 1 Hz improved by 32 % compared to the pre‐optimization state. The probe background sensitivity and stability increased by 43 % and 41 %, respectively, while the output signal sensitivity and stability improved by 28 % and 27 %. Furthermore, the SNR enhancement method proposed in this paper also shows potential for application in other atomic sensors, such as SERF magnetometers and nuclear magnetic resonance gyroscopes.

High-power devices used in hospitals generate harmonic magnetic field disturbances inside magnetic shielding cylinders (MSCs). These disturbances exhibit axial-gradient spatial distributions, which substantially degrade the imaging quality of magnetocardiography (MCG). To address this issue, a high-precision nonlinear spatial gradient disturbance suppression method is proposed for single-open MSCs. First, a nonlinear gradient coil design is developed to generate variable magnetic field gradients, precisely matching the nonlinear gradient distribution induced by external magnetic disturbances in the MSC. Subsequently, the entire system is modeled, and a composite controller based on proportional-integral repetitive control-linear extended state observer (PIRC-LESO) is designed to enhance the suppression of magnetic field disturbances with harmonic characteristics. Finally, an active magnetic field disturbance suppression system was implemented to evaluate the effectiveness of the nonlinear spatial gradient disturbance suppression method. Experimental results demonstrate that the proposed method reduces the peak-to-peak magnetic field disturbance from 42.0 to 6.9 pT and significantly enhances the signal-to-noise ratio of the MCG signals.

Recent advancements in optically pumped magnetometers (OPMs) have driven the development of magnetocardiography (MCG) systems, which provide high-quality signals and greater flexibility at a lower cost. While the OPM sensor configuration is more flexible, it introduces new challenges to the co-registration of MCG and computed tomography (CT), which directly impacts the spatial accuracy of source localization. In this study, we present an automatic co-registration method consisting of two main components: sensor localization and sensor-to-CT registration. Sensor positions and orientations are determined within 3D images reconstructed through optical scanning based on the bed registration unit. Abdominal surface matching is employed to automatically align anatomical images with sensor positions. The results demonstrate that the mean registration position and orientation errors across all sensors on the reference phantom are 0.49 mm and less than 0.2°, respectively, with a computational runtime of less than 10 seconds. Compared with existing methods involving manual procedures, such as moving digitizers to markers and removing sensors, the proposed co-registration method is more accurate and efficient.

Optically Pumped Magnetometer (OPM) is an emerging non-invasive biological magnetic imaging sensor. The vapor cell, its core component, requires stable temperature control to achieve ultra-high sensitivity in weak magnetic field detection. This paper presents a full-time, magnetic-field-free temperature measurement scheme for atomic vapor cells, employing an light absorption model that converts light intensity variations into temperature readings. First, we enhance the conventional light absorption temperature measurement model by incorporating the influences from static and modulated magnetic fields. This model modification enabled full-time temperature control achieving a 0.72°C reduction in measurement error. Subsequently, we experimentally quantify and analyze the temperature control performance under different incident light intensities and across various OPM configurations. Furthermore, cross-validation with platinum resistance temperature measurements demonstrates comparable temperature control performance of our proposed method. Most importantly, replacing platinum resistance sensors with our light absorption method enhances the OPM’s sensitivity by 2.83 fT/√Hz.

In the electrophysiological study of ischemic chronic heart failure (I-CHF) patients, cardiac electrical activity is captured using body surface electrodes in traditional electrocardiography (ECG). However, due to its limited spatial resolution, surface potential changes are reflected by ECG, and deep cardiac electrophysiological signals are struggled to be accurately detected, especially under complex pathological conditions. To address this limitation, a forward problem analytical model based on a three-dimensional cardiac-torso model for magnetocardiography (MCG) is proposed. An individualized ventricular-torso dual-domain model is constructed, and an improved diffusion equation is employed to accurately simulate the potential distribution between the endocardium and epicardium in I-CHF patients. The dynamic characteristics of the three-dimensional equivalent current density within cardiac tissue are analyzed. The simulated three-dimensional potential distribution is converted into a body surface magnetic field distribution and compared with MCGs obtained using a spin-exchange relaxation-free (SERF) magnetometer array. The proposed electromagnetic conversion analytical model demonstrates improvements in accuracy. At the peak moments of the R-wave and T-wave, significant differences in SSIM values are observed between normal individuals and ICHF patients. SSIM values for normal individuals are higher (R-wave 0.9066, T-wave 0.8537) compared to I-CHF patients (R-wave 0.8135, T-wave 0.8231). The simulated and measured data for the healthy group are consistent, while for the I-CHF group, the simulated gain and offset increase from 0.98 and 1.98 to 1.06 and 2.25, and the measured gain and offset decrease from 0.81 and 3.02 to 0.78 and 2.87. These results highlight the model’s ability to differentiate between normal and I-CHF conditions, providing a promising tool for early diagnosis of diseases.

Wearable optically pumped magnetometers (OPMs) operating in the spin-exchange relaxation-free (SERF) regime are an emerging alternative for biomagnetic field measurements. Nevertheless, the wearable implementation of OPM inevitably entails positional variations, leading to magnetic field disturbance and the introduction of amplitude errors, consequently impacting the precision of magnetic source localization. To solve this problem, an enhanced method is presented for wearable OPMs, which can achieve real-time suppression of magnetic field disturbance without overshoot. First, we analyze the influence mechanism of magnetic field disturbance on OPMs, thereby clarifying the nonlinear impact that disturbance exerts on the system. Then, a magnetic field disturbance suppression system based on active disturbance rejection control (ADRC) is developed to estimate and compensate for magnetic field disturbance in real time. The experimental results demonstrate that the ADRC method enables overshoot-free fast response and tracking suppression for the sensitive x-axis magnetic field disturbances. Moreover, the settling time is shortened to 3.3 ms compared to the 15.4 ms required by the PI method, which extends the effective duration of the signal under measurement. Furthermore, the amplitude errors in the y-axis and z-axis are reduced by 38.29% and 47.10%, respectively. Finally, the enhanced magnetic field disturbance suppression method proposed in this paper provides valuable technical support for wearable medical applications of OPM.

Ischemic chronic heart failure (ICHF) that is accompanied by bundle-branch block (BBB) represents a complex cardiac electrophysiological abnormality that has a significant impact on cardiac function. Under complex pathological cardiac conditions, signal changes are often insufficiently detected. Owing to the fixed and limited number of electrode placements of the electrocardiogram (ECG) subtle, abnormal, cardiac electrophysiological signals are difficult to capture comprehensively. In contrast, magnetocardiographs (MCGs) measured by spin-exchange relaxation-free (SERF) magnetometers, with their higher sensitivity and spatial resolution, represent cardiac electrical activity more accurately. However, owing to the longer electrical-conduction pathways of the cardiac deep structure and the superimposition of abnormal signals from the BBB-accompanied ICHF, these abnormal signals are weak and capture by SERF magnetometers is difficult; precise identification of the MCG features is thus more challenging. To address these technical limitations and enhance the detection of cardiac electrophysiological abnormalities, a more precise solution is required. In this paper, a comprehensive cardiac electrophysiological-conduction model and simulation method are proposed. Conduction pathways are mathematically modeled, and their contributions to the magnetic field are calculated. Detailed images of cardiac electrical activity are obtained through interpolation of MCGs. More accurate MCG data are generated by solving the comprehensive myocardial current I’_total. Simulated MCGs provide new insights for the MCG-technology optimization and highlight the value of cardiac electrophysiological conduction models in MCG measurements, thereby significantly improving the early detection and diagnosis of BBB.