In this study, a wearable multichannel human magnetocardiogram (MCG) system based on a spin exchange relaxation-free regime (SERF) magnetometer array is developed. The MCG system consists of a magnetically shielded device, a wearable SERF magnetometer array, and a computer for data acquisition and processing. Multichannel MCG signals from a healthy human are successfully recorded simultaneously. Independent component analysis (ICA) and empirical mode decomposition (EMD) are used to denoise MCG data. MCG imaging is realized to visualize the magnetic and current distribution around the heart. The validity of the MCG signals detected by the system is verified by electrocardiogram (ECG) signals obtained at the same position, and similar features and intervals of cardiac signal waveform appear on both MCG and ECG. Experiments show that our wearable MCG system is reliable for detecting MCG signals and can provide cardiac electromagnetic activity imaging.

This paper presents a hybrid optimal design of bi-planar coils, which produce uniform fields and field gradients. It treats the coil design issue as a nonlinear programming problem, and seeks the best performance via the optimal algorithm. Target field method is firstly utilized to obtain the initial coefficients and ranges of searching areas in stream functions. Then the magnetic field relative error is defined as the fitness function for minimization, and the particle swarm optimization algorithm is adopted to optimize the above coefficients. It not only overcomes the dependence on the position distribution of target field points, but also reduces required computational dimensions without affecting the pursuit of the ultimate performance. Comparing this work with the traditional target field method, the absolute field error in the region of interest is reduced from 5.09% to 0.95% (Bx coil), 11.96% to 0.35% (Bz coil), 16.54% to 0.57% (dBx/dz coil), and 11.8% to 0.28% (dBz/dz coil). Numerical simulation and experimental results show the validity and significance of the proposed method. It has great improvement in both uniform fields and field gradients, which is beneficial to the perfect realization of nulling undesired magnetic fields.

Magnetoencephalography (MEG) can non-invasively measure the electromagnetic activity of the brain. A new type of MEG, on-scalp MEG, has attracted the attention of researchers recently. Compared to the conventional SQUID-MEG, on-scalp MEG constructed with optically pumped magnetometers is wearable and has a high signal-to-noise ratio. While the co-registration between MEG and magnetic resonance imaging (MRI) significantly influences the source localization accuracy, co-registration error requires assessment, and quantification. Recent studies have evaluated the co-registration error of on-scalp MEG mainly based on the surface fit error or the repeatability error of different measurements, which do not reflect the true co-registration error. In this study, a three-dimensional-printed reference phantom was constructed to provide the ground truth of MEG sensor locations and orientations relative to MRI. The co-registration performances of commonly used three devices—electromagnetic digitization system, structured-light scanner, and laser scanner—were compared and quantified by the indices of final co-registration errors in the reference phantom and human experiments. Furthermore, the influence of the co-registration error on the performance of source localization was analyzed via simulations. The laser scanner had the best co-registration accuracy (rotation error of 0.23° and translation error of 0.76 mm based on the phantom experiment), whereas the structured-light scanner had the best cost performance. The results of this study provide recommendations and precautions for researchers regarding selecting and using an appropriate device for the co-registration of on-scalp MEG and MRI.

In response to the higher requirements of pulsar navigation performance for deep space exploration and remote sensing missions (such as Earth-Moon navigation and space observation), this paper proposes a time-delay estimation for pulsar navigation based on Peak-trough Amplitude and Rayleigh Entropy (PARE) to further improve the estimation accuracy and calculation speed. Firstly, the Fast Folding Algorithm (FFA) is used to perform parallel profile folding. According to the convergence and significance characteristics of the folded profiles, the FFA 3-D peak-valley map is threshold screened to narrow the search range, achieve the preliminary positioning of the subsequent profile test range, and improve the efficiency of pulsar frequency calculation. Secondly, according to the high sensitive response characteristics of the peak-valley difference of the folded profiles to the test frequency, ‘peak-trough amplitude’ is introduced to perform weighted operation on Rayleigh entropy of the folded profiles, and the significance of the folded profiles are jointly tested from the amplitude and structural characteristics to improve the accuracy and stability of pulsar frequency estimation. Finally, the cross-correlation operation is used to estimate the time-delay of the folded profiles. We also conducted simulations, influencing factor analysis, and NICER satellite actual observation data verification for the PARE method and the $\chi ^{2}$ test method. NICER satellite data verification shows that compared with the $\chi ^{2}$ test method, the PARE method has a 77.83% increase in time-delay estimation speed and a 57.93% increase in time-delay estimation accuracy. In addition, this method is insensitive to changes in observation duration and has better estimation stability.

In recent years, optically pumped magnetometer (OPM)-based magnetoencephalography (MEG) has shown potential for analyzing brain activity. It has a flexible sensor configuration and comparable sensitivity to conventional SQUID-MEG. We constructed a 32-channel OPM-MEG system and used it to measure cortical responses to median and ulnar nerve stimulations. Traditional magnetic source imaging methods tend to blur the spatial extent of sources. Accurate estimation of the spatial size of the source is important for studying the organization of brain somatotopy and for pre-surgical functional mapping. We proposed a new method called variational free energy-based spatial smoothing estimation (FESSE) to enhance the accuracy of mapping somatosensory cortex responses. A series of computer simulations based on the OPM-MEG showed better performance than the three types of competing methods under different levels of signal-to-noise ratios, source patch sizes, and co-registration errors. FESSE was then applied to the source imaging of the OPM-MEG experimental data.

Background: The radiological differential diagnosis between tumor recurrence and radiation-induced necrosis (ie, pseudoprogression) is of paramount importance in the management of glioma patients.Objective: This research aims to develop a deep learning methodology for automated differentiation of tumor recurrence from radiation necrosis based on routine magnetic resonance imaging (MRI) scans.Methods: In this retrospective study, 146 patients who underwent radiation therapy after glioma resection and presented with suspected recurrent lesions at the follow-up MRI examination were selected for analysis. Routine MRI scans were acquired from each patient, including T1, T2, and gadolinium-contrast-enhanced T1 sequences. Of those cases, 96 (65.8%) were confirmed as glioma recurrence on postsurgical pathological examination, while 50 (34.2%) were diagnosed as necrosis. A light-weighted deep neural network (DNN) (ie, efficient radionecrosis neural network [ERN-Net]) was proposed to learn radiological features of gliomas and necrosis from MRI scans. Sensitivity, specificity, accuracy, and area under the curve (AUC) were used to evaluate performance of the model in both image-wise and subject-wise classifications. Preoperative diagnostic performance of the model was also compared to that of the state-of-the-art DNN models and five experienced neurosurgeons.Results: DNN models based on multimodal MRI outperformed single-modal models. ERN-Net achieved the highest AUC in both image-wise (0.915) and subject-wise (0.958) classification tasks. The evaluated DNN models achieved an average sensitivity of 0.947 (SD 0.033), specificity of 0.817 (SD 0.075), and accuracy of 0.903 (SD 0.026), which were significantly better than the tested neurosurgeons (P=.02 in sensitivity and P<.001 in specificity and accuracy).Conclusions: Deep learning offers a useful computational tool for the differential diagnosis between recurrent gliomas and necrosis. The proposed ERN-Net model, a simple and effective DNN model, achieved excellent performance on routine MRI scans and showed a high clinical applicability.