Biomedical Physics & Engineering Express最新文献

{"title":"RAE-Net: a multi-modal neural network based on feature fusion and evidential deep learning algorithm in predicting breast cancer subtypes on DCE-MRI.","authors":"Xiaowen Tang, Yinsu Zhu","doi":"10.1088/2057-1976/adb494","DOIUrl":"10.1088/2057-1976/adb494","url":null,"abstract":"<p><p><i>Objectives</i>Accurate identification of molecular subtypes in breast cancer is critical for personalized treatment. This study introduces a novel neural network model, RAE-Net, based on Multimodal Feature Fusion (MFF) and the Evidential Deep Learning Algorithm (EDLA) to improve breast cancer subtype prediction using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).<i>Methods</i>A dataset of 344 patients with histologically confirmed breast cancer was divided into training (n = 200), validation (n = 60), and testing (n = 62) cohorts. RAE-Net, built on ResNet-50 with Multi-Head Attention (MHA) fusion and Multi-Layer Perceptron (MLP) mechanisms, combines radiomic and deep learning features for subtype prediction. The EDLA module adds uncertainty estimation to enhance classification reliability.<i>Results</i>The RAE-Net model incorporating the MFF module demonstrated superior performance, achieving a mean accuracy of 0.83 and a Macro-F1 score of 0.78, surpassing traditional radiomics models (accuracy: 0.79, Macro-F1: 0.75) and standalone deep learning models (accuracy: 0.80, Macro-F1: 0.76). When an EDLA uncertainty threshold of 0.2 was applied, the performance significantly improved, with accuracy reaching 0.97 and Macro-F1 increasing to 0.92. Additionally, RAE-Net outperformed two recent deep learning networks, ResGANet and HIFUSE. Specifically, RAE-Net showed a 0.5% improvement in accuracy and a higher AUC compared to ResGANet. In comparison to HIFUSE, RAE-Net reduced both the number of parameters and computational cost by 90% while only increasing computation time by 5.7%.<i>Conclusions</i>RAE-Net integrates feature fusion and uncertainty estimation to predict breast cancer subtypes from DCE-MRI. The model achieves high accuracy while maintaining computational efficiency, demonstrating its potential for clinical use as a reliable and resource-efficient diagnostic tool.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143397803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Platform for detecting, managing, and manipulating characteristic points of the ECG waves through continuous wavelet transform implementation.","authors":"Frank Martínez-Suárez, Carlos Alvarado-Serrano, Oscar Casas","doi":"10.1088/2057-1976/adb589","DOIUrl":"10.1088/2057-1976/adb589","url":null,"abstract":"<p><p>This work presents open-source software that incorporates detection and delineation algorithms of characteristic points of QRS complexes and P and T waves in ECG recordings. The tool facilitates the identification of significant points in the ECG waves, allowing manual correction of the results based on user criteria, exporting the detected points, and a simultaneous visualization of the recordings and the obtained points. The main objective is to improve the management of long- and short-term recordings by reducing detection errors caused by noise, interference, and artifacts, while also providing the capability for manual results correction. To achieve these objectives, the software uses an SQL Server database, which efficiently manages the data, and detection and delineation algorithms based on the continuous wavelet transform with splines, along with alternatives to optimize processing time. The QRS complex detection algorithm was validated in a previous work with the manually annotated ECG databases: MIT-BIH Arrhythmia, European ST-T, and QT. The QRS detector obtained a Se = 99.91% and a P⁺= 99.62% on the first channel of the MIT-BIH, ST-T and QT databases over the 986,930 QRS complexes analyzed. To evaluate the delineation algorithms of the characteristic points of QRS, P and T waves, the QT and PTB databases were used. The mean and standard deviations of the differences between the automatic and manual annotations by CSE experts were calculated. The mean errors range obtained was smaller than one sample (4 ms) to around two samples (8 ms); and the mean standard deviations range was around of two samples (8 ms) to six samples (24 ms).</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143413304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Finite Element Analysis Model for Magnetomotive Ultrasound Elastometry Magnet Design with Experimental Validation.","authors":"Jacquelline Nyakunu, Christopher T Piatnichouk, Henry Chase Russell, Niels Jacob van Duijnhoven, Benjamin E Levy","doi":"10.1088/2057-1976/adb8f0","DOIUrl":"10.1088/2057-1976/adb8f0","url":null,"abstract":"<p><strong>Objective: </strong>Magnetomotive ultrasound (MMUS) using magnetic nanoparticle contrast agents has shown promise for thrombosis imaging and quantitative elastometry via magnetomotive resonant acoustic spectroscopy (MRAS). Young's modulus measurements of smaller, stiffer thrombi require an MRAS system capable of generating forces at higher temporal frequencies. Solenoids with fewer turns, and thus less inductance, could improve high frequency performance, but the reduced force may compromise results. In this work, a computational model capable of assessing the effectiveness of MRAS elastometry magnet configurations is presented and validated.&#xD;&#xD;Approach. Finite element analysis (FEA) was used to model the force and inductance of MRAS systems. The simulations incorporated both solenoid electromagnets and permanent magnets in three-dimensional steady-state, frequency domain, and time domain studies.&#xD;&#xD;Main results. The model successfully predicted that a configuration in which permanent magnets were added to an existing MRAS system could be used to increase the force supplied. Accordingly, the displacement measured in a magnetically labeled validation phantom increased by a factor of 2.2 ± 0.3 when the force was predicted to increase by a factor of 2.2 ± 0.2. The model additionally identified a new solenoid configuration consisting of four smaller coils capable of providing sufficient force at higher driving frequencies.&#xD;&#xD;Significance. These results indicate two methods by which MRAS systems could be designed to deliver higher frequency magnetic forces without the need for experimental trial and error. Either the number of turns within each solenoid could be reduced while permanent magnets are added at precise locations, or a larger number of smaller solenoids could be used. These findings overcome a key challenge toward the goal of MMUS thrombosis elastometry, and simulation files are provided online for broader experimentation.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143472106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EffNet: an efficient one-dimensional convolutional neural networks for efficient classification of long-term ECG fragments.","authors":"Bilal Ashraf, Husan Ali, Muhammad Aseer Khan, Fahad R Albogamy","doi":"10.1088/2057-1976/adb58a","DOIUrl":"10.1088/2057-1976/adb58a","url":null,"abstract":"<p><p>Early Diagnosis of Cardiovascular disease (CVD) is essential to prevent a person from death in case of a cardiac arrhythmia. Automated ECG classification is required because manual classification by cardiologists is laborious, time-consuming, and prone to errors. Efficient ECG classification has been an active research problem over the past few decades. Earlier ECG classification techniques didn't perform satisfactorily with greater accuracy and efficiency. An efficient 12-layer deep One-Dimensional Convolutional Neural Network (1D-CNN) titled EffNet is proposed in this research paper to automatically classify five distinct categories of heartbeats present in ECG signals. A unique collection of five different PhysioNet databases with ECG recordings of five different classes is created to enhance the dataset. These databases are segmented into ECG Fragments (long-term ECG signals of length 10 s) to capture the ECG features between successive beats effectively. These ECG fragments are then concatenated to form a merged dataset. Initially, sampling of the merged dataset is done. The Synthetic Minority Oversampling Technique (SMOTE) is used to balance the dataset. Afterwards, 1D-CNN is employed with different sets of hyperparameters for the efficient classification of the ECG dataset. Classification of ECG of five different classes is also done through two deep Convolutional Neural Networks (CNNs), namely GoogLeNet and SqueezeNet, and Support Vector Machines (SVM). The statistical results obtained proved the dominance of EffNet over the transfer learning techniques (SqueezeNet and GoogLeNet) and SVM. Furthermore, a comparison is also made with the existing literature work carried out for ECG classification, and the statistical results dominated over all others in terms of performance metrics.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143413303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the Feasibility of an Online Brain-Computer Interface-based Neurofeedback Game for Enhancing Attention and Working Memory in Stroke and Mild Cognitive Impairment Patients.","authors":"Suhail T A, Subasree Ramakrishnan, A P Vinod, Suvarna Alladi","doi":"10.1088/2057-1976/adb8ef","DOIUrl":"https://doi.org/10.1088/2057-1976/adb8ef","url":null,"abstract":"<p><strong>Background: </strong>Neurofeedback training (NFT) using Electroencephalogram-based Brain Computer Interface (EEG-BCI) is an emerging therapeutic tool for enhancing cognition. &#xD;Methods: We developed an EEG-BCI-based NFT game for enhancing attention and working memory of stroke and Mild cognitive impairment (MCI) patients. The game involves a working memory task during which the players memorize locations of images in a matrix and refill them correctly using their attention levels. The proposed NFT was conducted across fifteen subjects (6 Stroke, 7 MCI, and 2 healthy). The effectiveness of the NFT was evaluated using the percentage of correctly filled matrix elements and EEG-based attention score. EEG varitions during working memory tasks were also investigated using EEG topographs and EEG-based indices.</p><p><strong>Results: </strong>The EEG-based attention score showed an enhancement ranging from 4.29-32.18% in the Stroke group from the first session to the third session, while in the MCI group, the improvement ranged from 4.32% to 48.25%. We observed significant differences in EEG band powers during working memory operation between the stroke and MCI groups.</p><p><strong>Significance: </strong>The proposed neurofeedback game operates based on attention and aims to improve multiple cognitive functions, including attention and working memory, in patients with stroke and MCI.</p><p><strong>Conclusions: </strong>The experimental results on the effect of NFT in patient groups demonstrated that the proposed neurofeedback game has the potential to enhance attention and memory skills in patients with neurological disorders. A large-scale study is needed in the future to prove the efficacy on a wider population.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143472107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simultaneous Reduction of Radiation Dose and Scatter-to-Primary Ratio using a Truncated Detector and Advanced Algorithms for Dedicated Cone-Beam Breast CT.","authors":"Hsin Wu Tseng, Srinivasan Vedantham, Zhiyang Fu","doi":"10.1088/2057-1976/adb8f1","DOIUrl":"https://doi.org/10.1088/2057-1976/adb8f1","url":null,"abstract":"<p><strong>Objective: </strong>To determine the minimum detector width along the fan-angle direction in offset-detector cone-beam breast CT for multiple advanced reconstruction algorithms and to investigate the effect on radiation dose, scatter, and image quality.</p><p><strong>Approach: </strong>Complete sinograms (m × n = 1024 × 768 pixels) of 30 clinical breast CT datasets previously acquired on a clinical-prototype cone-beam breast CT system were reconstructed using Feldkamp-Davis-Kress (FDK) algorithm and served as the reference. Complete sinograms were retrospectively truncated to varying widths to understand the limits of four image reconstruction algorithms - FDK with redundancy weighting (FDK-W), compressed-sensing based FRIST, fully-supervised MS-RDN, and self-supervised AFN. Upon determining the truncation limits, numerical phantoms generated by segmenting the reference reconstructions into skin, adipose, and fibroglandular tissues were used to determine the radiation dose and scatter-to-primary ratio (SPR) using Monte Carlo simulations.</p><p><strong>Main results: </strong>FDK-W, FRIST, and MS-RDN showed artifacts when m < 596, whereas AFN reconstructed images without artifacts for m>=536. Reducing the detector width reduced signal-difference to noise ratio (SDNR) for FDK-W, whereas FRIST, MS-RDN and AFN maintained or improved SDNR. Reference reconstruction and AFN with m=536 had similar quantitative measures of image quality.</p><p><strong>Significance: </strong>For the 30 cases, AFN with m=536 reduced the radiation dose and SPR by 37.85% and 33.46%, respectively, compared to the reference. Qualitative and quantitative image quality indicate the feasibility of AFN for offset-detector cone-beam breast CT. Radiation dose and SPR were simultaneously reduced with a 536 ×768 detector and when used in conjunction with AFN algorithm had similar image quality as the reference reconstruction.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143472108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility study of synchronously increasing dose of multi-shell structure to improve stereotactic ablation radiotherapy central dose of large volume locally advanced gastrointestinal stromal tumors using cyberKnife.","authors":"Hui Xu, Zhen Jia, Xiongfei Li, Mingzhu Li, Hongyu Lin, Yunfei Bian, Wei Wang, Lian Zhang, Ying Li","doi":"10.1088/2057-1976/adb434","DOIUrl":"10.1088/2057-1976/adb434","url":null,"abstract":"<p><p><i><b>Purpose</b></i>. Increasing the central dose for large, locally advanced, drug-resistant gastrointestinal stromal tumors (LADR-GISTs) has consistently been a significant challenge. This study explores the feasibility of using multiple shell structures within the tumor to enhance the central ablation dose of large LADR-GIST by increasing the shell doses.<i><b>Methods and Materials</b></i>. This study involved five patients with large LADR-GIST who were treated with CyberKnife. The gross tumor volume (GTV) was delineated as a multi-shell structure. Five dose escalation plans (SIB-SBRT) were created for each patient, varying the dose escalation ratios. The radiation doses for the center of the GTV (GTV center) in these plans ranged from 49 Gy to 70 Gy. Parameter evaluations were conducted comparing the SIB-SBRT plans with conventional SBRT plans (Con-SBRT), focusing on equivalent uniform dose (EUD), relative equivalent uniform dose (rEUD), dose volume parameters, conformal index (CI), new conformal index (nCI), gradient index (GI), and monitor unit (MU). The Friedman Test was employed to determine statistical differences (<i>P</i>< 0.05), followed by pairwise comparisons.<i><b>Results</b></i>. When the dose escalation ratios reached 25% of the prescribed dose, the average rEUD increased to 6.92, and the proportion of the GTV volume with Biologically Equivalent Dose (BED)> 100 Gy increased to 30.69%. At dose escalation ratios of 30% of the prescribed dose, the rEUD stabilized, but the radiation dose received by the bladder, colon, and duodenum significantly increased. Except for the SIB₂₅-SBRT and SIB₃₀-SBRT groups, no statistically significant differences were observed between the other SIB-SBRT groups and the Con-SBRT group across various evaluation metrics.<i><b>Conclusions</b></i>. The method of synchronously increasing the dose using a multi-shell structure is feasible for stereotactic ablation in the treatment of LADR-GISTs using CyberKnife. The results indicate that dose escalation ratios of 25% of the prescribed dose can provide a satisfactory ablation dose (BED > 100 Gy), covering 31% of the large tumor volume.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143390055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based video-level view classification of two-dimensional transthoracic echocardiography.","authors":"Hanlin Cheng, Zhongqing Shi, Zhanru Qi, Xiaoxian Wang, Guanjun Guo, Aijuan Fang, Zhibin Jin, Chunjie Shan, Ruiyang Chen, Yue Du, Sunnan Qian, Shouhua Luo, Jing Yao","doi":"10.1088/2057-1976/adb493","DOIUrl":"10.1088/2057-1976/adb493","url":null,"abstract":"<p><p>In recent years, deep learning (DL)-based automatic view classification of 2D transthoracic echocardiography (TTE) has demonstrated strong performance, but has not fully addressed key clinical requirements such as view coverage, classification accuracy, inference delay, and the need for thorough exploration of performance in real-world clinical settings. We proposed a clinical requirement-driven DL framework, TTESlowFast, for accurate and efficient video-level TTE view classification. This framework is based on the SlowFast architecture and incorporates both a sampling balance strategy and a data augmentation strategy to address class imbalance and the limited availability of labeled TTE videos, respectively. TTESlowFast achieved an overall accuracy of 0.9881, precision of 0.9870, recall of 0.9867, and F1 score of 0.9867 on the test set. After field deployment, the model's overall accuracy, precision, recall, and F1 score for view classification were 0.9607, 0.9586, 0.9499, and 0.9530, respectively. The inference time for processing a single TTE video was 105.0 ± 50.1 ms on a desktop GPU (NVIDIA RTX 3060) and 186.0 ± 5.2 ms on an edge computing device (Jetson Orin Nano), which basically meets the clinical demand for immediate processing following image acquisition. The TTESlowFast framework proposed in this study demonstrates effective performance in TTE view classification with low inference delay, making it well-suited for various medical scenarios and showing significant potential for practical application.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143397671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel Multiple Focal Point Technique for Laser-Induced Shear Wave Generation in Deep Tissue: Simulation Insights.","authors":"Reza Bahrami Gorji, Mohammad Mohammadi, Bahador Makkiabadi","doi":"10.1088/2057-1976/adb755","DOIUrl":"https://doi.org/10.1088/2057-1976/adb755","url":null,"abstract":"<p><strong>Purpose: </strong>&#xD;Laser applications in biomedical imaging have several decades of history; however, some unexplored corners remain for study. While previous studies contain massive data on photoacoustic imaging, optical coherence imaging/elastography, and surface acoustic waves, the generation of shear waves in bulk by laser remained rarely investigated. Here, we study the applicability of multipoint laser exposure to generate deep tissue shear waves, which have potential applications in dynamic elastography.&#xD;Method:&#xD;Previous studies used single shots of laser to induce shear waves and create weak waves. Based on this, we suggest a multipoint approach to enhancing the amplitude of the shear wave in bulk. These approaches contain supersonic exposure, overlay Mach 1, and comb-push exposure in a finite element simulation environment.&#xD;Result:&#xD;Although the results showed a linear relationship between laser power and shear wave amplitude, the supersonic and overlay exposure increased the amplitude from 15 nm to over 60 nm and 230 nm, respectively.&#xD;Conclusion:&#xD;Our approaches showed a potentially successful increase in shear wave amplitude in the simulation environment. However, experimental data still need to be investigated before these techniques can be suggested for laser-induced shear wave elastography in the deep medium.&#xD.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143447645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magnetically shielded high-resolution visual stimulation for OPM-MEG applications.","authors":"P Anders, M Brickwedde, J Voigt, T Grent-'t-Jong, P Krüger, J Haueisen, P J Uhlhaas, T Sander","doi":"10.1088/2057-1976/adb1eb","DOIUrl":"10.1088/2057-1976/adb1eb","url":null,"abstract":"<p><p>Many magnetoencephalography (MEG) experiments require visual stimulation (VS) inside a magnetically shielded room (MSR). For conventional MEG utilizing superconducting quantum interference devices (SQUIDs), the participant's head must stay within the semi-spherical surface of a cryogenic storage Dewar. This design allows to have many SQUID sensors as close as possible to the head in order to achieve good signal quality. Because Dewars have very restricted mobility, VS is usually realized using a projector outside of the MSR, some optical elements and a back-projection screen in the line of sight of the participant.Recently, the feasibility of MEG using optically pumped magnetometers (OPMs) was demonstrated. These sensors can be attached directly to the head because they operate near room temperature. OPM-MEG therefore offers more experimental freedom including different postures, movements or hyperscanning, creating the need for a more flexible kind of VS setup.In this paper, we present a compact, high-resolution VS setup which is enclosed by a portable magnetic shield with an opening for the projection. The VS setup is based on a single-board computer which acts as experiment control device to create visual stimuli, process inputs, log participant activity and set off trigger signals. This setup supports the new possibilities of OPM-MEG and can be easily installed into any MSR. We investigate if the shielded VS inside the MSR generates distortion signals above the noise floor of the OPMs. We also show that visual cortex activity can be evoked with our setup and recorded with a custom-made OPM-MEG cap. By applying two well-established visual stimulation paradigms, we demonstrate the ability of our setup to elicit brain activity in different frequency ranges.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143187977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}