IEEE Transactions on Biomedical Engineering最新文献

{"title":"Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI.","authors":"Zi Wang, Min Xiao, Yirong Zhou, Chengyan Wang, Naiming Wu, Yi Li, Yiwen Gong, Shufu Chang, Yinyin Chen, Liuhong Zhu, Jianjun Zhou, Congbo Cai, He Wang, Xianwang Jiang, Di Guo, Guang Yang, Xiaobo Qu","doi":"10.1109/TBME.2025.3574090","DOIUrl":"https://doi.org/10.1109/TBME.2025.3574090","url":null,"abstract":"<p><strong>Objective: </strong>Dynamic magnetic resonance imaging (MRI) plays an indispensable role in cardiac diagnosis. To enable fast imaging, the k-space data can be undersampled but the image reconstruction poses a great challenge of high-dimensional processing. This challenge necessitates extensive training data in deep learning reconstruction methods. In this work, we propose a novel and efficient approach, leveraging a dimension-reduced separable learning scheme that can perform exceptionally well even with highly limited training data.</p><p><strong>Methods: </strong>We design this new approach by incorporating spatiotemporal priors into the development of a Deep Separable Spatiotemporal Learning network (DeepSSL), which unrolls an iteration process of a 2D spatiotemporal reconstruction model with both temporal lowrankness and spatial sparsity. Intermediate outputs can also be visualized to provide insights into the network behavior and enhance interpretability.</p><p><strong>Results: </strong>Extensive results on cardiac cine datasets demonstrate that the proposed DeepSSL surpasses stateof-the-art methods both visually and quantitatively, while reducing the demand for training cases by up to 75%. Additionally, its preliminary adaptability to unseen cardiac patients has been verified through a blind reader study conducted by experienced radiologists and cardiologists. Furthermore, DeepSSL enhances the accuracy of the downstream task of cardiac segmentation and exhibits robustness in prospectively undersampled real-time cardiac MRI.</p><p><strong>Conclusion: </strong>DeepSSL is efficient under highly limited training data and adaptive to patients and prospective undersampling.</p><p><strong>Significance: </strong>This approach holds promise in addressing the escalating demand for high-dimensional data reconstruction in MRI applications.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-Quality CEST Mapping With Lorentzian-Model Informed Neural Representation.","authors":"Chu Chen, Yang Liu, Se Weon Park, Jizhou Li, Kannie W Y Chan, Jianpan Huang, Jean-Michel Morel, Raymond H Chan","doi":"10.1109/TBME.2025.3574238","DOIUrl":"https://doi.org/10.1109/TBME.2025.3574238","url":null,"abstract":"<p><p>Chemical Exchange Saturation Transfer (CEST) MRI has demonstrated its remarkable ability to enhance the detection of macromolecules and metabolites with low concentrations. While CEST mapping is essential for quantifying molecular information, conventional methods face critical limitations: model-based approaches are constrained by limited sensitivity and robustness depending heavily on parameter setups, while data-driven deep learning methods lack generalizability across heterogeneous datasets and acquisition protocols. To overcome these challenges, we propose a Lorentzian-model Informed Neural Representation (LINR) framework for high-quality CEST mapping. LINR employs a self-supervised neural architecture embedding the Lorentzian equation - the fundamental biophysical model of CEST signal evolution - to directly reconstruct high-sensitivity parameter maps from raw z-spectra, eliminating dependency on labeled training data. Convergence of the self-supervised training strategy is guaranteed theoretically, ensuring LINR's mathematical validity. The superior performance of LINR in capturing CEST contrasts is revealed through comprehensive evaluations based on synthetic phantoms and in-vivo experiments (including tumor and Alzheimer's disease models). The intuitive parameter-free design enables adaptive integration into diverse CEST imaging workflows, positioning LINR as a versatile tool for non-invasive molecular diagnostics and pathophysiological discovery.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Left Atrial Positioning System to Enable Follow-Up and Cohort Studies.","authors":"Noah J Mehringer, Elliot R McVeigh","doi":"10.1109/TBME.2025.3574158","DOIUrl":"https://doi.org/10.1109/TBME.2025.3574158","url":null,"abstract":"<p><strong>Objective: </strong>We present a new algorithm to automatically convert 3-dimensional left atrium surface meshes into a standard 2-dimensional space: a Left Atrial Positioning System (LAPS).</p><p><strong>Methods: </strong>Forty-five contrast-enhanced 4- dimensional computed tomography datasets were collected from 30 subjects. The left atrium volume was segmented using a trained neural network and converted into a surface mesh. LAPS coordinates were calculated on each mesh by computing lines of longitude and latitude on the surface of the mesh with reference to the center of the posterior wall and the mitral valve. LAPS accuracy was evaluated with one-way transfer of coordinates from a template mesh to a synthetic ground truth, which was created by registering the template mesh and pre-calculated LAPS coordinates to a target mesh. The Euclidian distance error was measured between each test node and its ground truth location.</p><p><strong>Results: </strong>The median point transfer error was 2.13 mm between follow-up scans of the same subject (n = 15) and 3.99 mm between different subjects (n = 30). The left atrium was divided into 24 anatomic regions and represented on a 2D square diagram.</p><p><strong>Conclusion: </strong>The Left Atrial Positioning System is fully automatic, accurate, robust to anatomic variation, and has flexible visualization for mapping data in the left atrium.</p><p><strong>Significance: </strong>This provides a framework for comparing regional LA surface data values in both follow-up and cohort studies.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144158136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ECG-SMART-NET: A Deep Learning Architecture for Precise ECG Diagnosis of Occlusion Myocardial Infarction.","authors":"Nathan T Riek, Murat Akcakaya, Zeineb Bouzid, Tanmay Gokhale, Stephanie Helman, Karina Kraevsky-Philips, Rui Qi Ji, Ervin Sejdic, Jessica K Zegre-Hemsey, Christian Martin-Gill, Clifton W Callaway, Samir Saba, Salah Al-Zaiti","doi":"10.1109/TBME.2025.3573581","DOIUrl":"https://doi.org/10.1109/TBME.2025.3573581","url":null,"abstract":"<p><strong>Objective: </strong>In this paper we develop and evaluate ECG-SMART-NET for occlusion myocardial infarction (OMI) identification. OMI is a severe form of heart attack characterized by complete blockage of one or more coronary arteries requiring immediate referral for cardiac catheterization to restore blood flow to the heart. Two thirds of OMI cases are difficult to visually identify from a 12-lead electrocardiogram (ECG) and can be potentially fatal if not identified quickly. Previous works on this topic are scarce, and current state-of-the-art evidence suggests both feature-based random forests and convolutional neural networks (CNNs) are promising approaches to improve ECG detection of OMI.</p><p><strong>Methods: </strong>While the ResNet architecture has been adapted for use with ECG recordings, it is not ideally suited to capture informative temporal features within each lead and the spatial concordance or discordance across leads. We propose a clinically informed modification of the ResNet-18 architecture. The model first learns temporal features through temporal convolutional layers with 1xk kernels followed by a spatial convolutional layer, after the residual blocks, with 12x1 kernels to learn spatial features.</p><p><strong>Results: </strong>ECG-SMART-NET was benchmarked against the original ResNet-18 and other state-of-the-art models on a multisite real-word clinical dataset that consists of 10,393 ECGs from 7,397 unique patients (rate of OMI = 7.2%). ECG-SMART-NET outperformed other models in the classification of OMI with a test AUC of 0.953 [0.921, 0.978].</p><p><strong>Conclusion and significance: </strong>ECG-SMART-NET can outperform the state-of-the-art random forest for OMI prediction and is better suited for this task than the original ResNet-18 architecture.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global Maxwell Tomography Using the Volume-Surface Integral Equation for Improved Estimation of Electrical Properties.","authors":"Ilias I Giannakopoulos, Jose E Cruz Serralles, Jan Paska, Martijn A Cloos, Ryan Brown, Riccardo Lattanzi","doi":"10.1109/TBME.2025.3572800","DOIUrl":"https://doi.org/10.1109/TBME.2025.3572800","url":null,"abstract":"<p><strong>Objective: </strong>Global Maxwell Tomography (GMT) is a noninvasive inverse optimization method for the estimation of electrical properties (EP) from magnetic resonance (MR) measurements. GMT uses the volume integral equation (VIE) in the forward problem and assumes that the sample has negligible effect on the coil currents. Consequently, GMT calculates the coil's incident fields with an initial EP distribution and keeps them constant for all optimization iterations. This can lead to erroneous reconstructions. This work introduces a novel version of GMT that replaces VIE with the volume-surface integral equation (VSIE), which recalculates the coil currents at every iteration based on updated EP estimates before computing the associated fields.</p><p><strong>Methods: </strong>We simulated an 8-channel transceiver coil array for 7 T brain imaging and reconstructed the EP of a realistic head model using VSIE-based GMT. We built the coil, collected experimental MR measurements, and reconstructed EP of a two-compartment phantom.</p><p><strong>Results: </strong>In simulations, VSIE-based GMT outperformed VIE-based GMT by at least 12% for both EP. In experiments, the relative difference with respect to probe-measured EP values in the inner (outer) compartment was 13% (26%) and 17% (33%) for the permittivity and conductivity, respectively.</p><p><strong>Conclusion: </strong>The use of VSIE over VIE enhances GMT's performance by accounting for the effect of the EP on the coil currents.</p><p><strong>Significance: </strong>VSIE-based GMT does not rely on an initial EP estimate, rendering it more suitable for experimental reconstructions compared to the VIE-based GMT.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image-free Tumor Segmentation of Soft Tissue using a Minimally Invasive Robotic Palpation System.","authors":"Yun-Jeong Lee, Sang-Won Bang, Jeong-Bin Hong, Sukho Park","doi":"10.1109/TBME.2025.3573666","DOIUrl":"https://doi.org/10.1109/TBME.2025.3573666","url":null,"abstract":"<p><p>Tumor segmentation is crucial for surgical planning and precise tumor resection for effective treatment. Traditionally, tumor localization has been performed using medical imaging techniques such as CT and MRI or through direct palpation by surgeons. However, in minimally invasive robotic surgery (MIS), these methods have limitations, including registration errors with imaging and inaccuracies caused by the subjectivity of palpation by surgeons. In this study, we introduce a robotic palpation system and an image-free process for MIS tumor segmentation using a robot. Our proposed system enables precise tumor shape differentiation through direct robotic palpation. For this, the robotic palpation system collects surface shape information through the proposed process, allowing tissue palpation at specific depths according to surface curvature. Additionally, it visualizes stiffness maps, enabling image-free tumor segmentation. In experiments using this system, evaluation of planar and curved phantom models demonstrates precise segmentation at targeted sites, with sensitivities of 0. 9634 and 0. 9729, and specificities of 0. 9646 and 0. 9878, respectively. Validation on ex-vivo porcine liver models further confirms the efficacy of our approach.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prior Image Guided Ring Artifact Correction for Photon-Counting Detector Data in Hybrid Spectral CT.","authors":"Xin Lu, Xinran Yu, Yi Du, Yunsong Zhao","doi":"10.1109/TBME.2025.3573882","DOIUrl":"https://doi.org/10.1109/TBME.2025.3573882","url":null,"abstract":"<p><strong>Objective: </strong>Photon-counting detector (PCD) is an advanced and innovative X-ray detector, that offers significant advantages such as improved spatial resolution and higher dose efficiency. However, as a new X-ray detection device, PCD faces technical challenges, particularly the non-uniformity among detector units, which can lead to ring artifacts in reconstructed CT images. To address this challenge, we propose a novel image-domain post-processing method to effectively correct ring artifacts in PCD-reconstructed images.</p><p><strong>Method: </strong>The method is specifically designed for a self-developed hybrid spectral CT system equipped with both a PCD and an energy integration detector (EID). The ring artifact-free EID-reconstructed images are used as prior images to guide the correction of ring artifacts in the PCD-reconstructed images. In addition, a carefully designed weight is introduced in the optimization model to prevent the degradation of boundary details in the target images caused by blurred edges in prior images.</p><p><strong>Results: </strong>The effectiveness of the proposed method is validated on both simulated data and real data, demonstrating that the proposed method can efficiently and effectively correct ring artifacts in the reconstructed images of PCD.</p><p><strong>Significance: </strong>This confirms that the proposed method provides a practical solution for hybrid spectral CT imaging.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Joint Geometric Topological Analysis Network (JGTA-Net) for Detecting and Segmenting Intracranial Aneurysms.","authors":"Xinyue Zhang, Zonghan Lyu, Yang Wang, Bo Peng, Jingfeng Jiang","doi":"10.1109/TBME.2025.3572837","DOIUrl":"https://doi.org/10.1109/TBME.2025.3572837","url":null,"abstract":"<p><strong>Objective: </strong>The rupture of intracranial aneurysms leads to subarachnoid hemorrhage. Detecting intracranial aneurysms before rupture and stratifying their risk is critical in guiding preventive measures. Point-based aneurysm segmentation provides a plausible pathway for automatic aneurysm detection. However, challenges in existing segmentation methods motivate the proposed work.</p><p><strong>Methods: </strong>We propose a dual-branch network model (JGTANet) for accurately detecting aneurysms. JGTA-Net employs a hierarchical geometric feature learning framework to extract local contextual geometric information from the point cloud representing intracranial vessels. Building on this, we integrated a topological analysis module that leverages persistent homology to capture complex structural details of 3D objects, filtering out short-lived noise to enhance the overall topological invariance of the aneurysms. Moreover, we refined the segmentation output by quantitatively computing multi-scale topological features and introducing a topological loss function to preserve the correct topological relationships better. Finally, we designed a feature fusion module that integrates information extracted from different modalities and receptive fields, enabling effective multi-source information fusion.</p><p><strong>Results: </strong>Experiments conducted on the IntrA dataset demonstrated the superiority of the proposed network model, yielding state-of-the-art segmentation results (e.g., Dice and IOU are approximately 0.95 and 0.90, respectively). Our IntrA results were confirmed by testing on two independent datasets: One with comparable lengths to the IntrA dataset and the other with longer and more complex vessels.</p><p><strong>Conclusions: </strong>The proposed JGTA-Net model outperformed other recently published methods (> 10% in DSC and IOU), showing our model's strong generalization capabilities.</p><p><strong>Significance: </strong>The proposed work can be integrated into a large deep-learning-based system for assessing brain aneurysms in the clinical workflow.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144132048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Polarized Microwave-induced Thermoacoustic Imaging for Detection of Dilated Cardiomyopathy in vivo.","authors":"Zhiyuan Jin, Xue Li, Yu Wang, Bohan Zhang, Yichao Fu, Huan Qin","doi":"10.1109/TBME.2025.3573326","DOIUrl":"https://doi.org/10.1109/TBME.2025.3573326","url":null,"abstract":"<p><strong>Objective: </strong>Altered myocardial fiber arrangement is a hallmark feature in the early stages of dilated cardiomyopathy (DCM). However, current medical imaging modalities have limitations in resolving microstructural changes within the myocardium. In this study, we introduce a high-spatiotemporal-resolution polarized microwave-induced thermoacoustic imaging (P-MTAI) technique for in vivo detection of myocardial fiber rearrangement in DCM.</p><p><strong>Methods: </strong>Leveraging the anisotropic arrangement and orientation of myocardial fibers, the technique analyzes thermoacoustic signals generated by stimulating the myocardium with linearly polarized pulsed microwaves from four orthogonal directions, enabling the assessment of its spatial microstructure. To mitigate motion artifacts induced by cardiac contraction, the system acquires thermoacoustic images at a frame rate of 100 Hz. The end-diastolic phase, corresponding to maximal cardiac relaxation, is identified from consecutively acquired frames across multiple cardiac cycles, and frames from this phase are selected for polarization analysis. A derived parameter, the degree of microwave absorption anisotropy (DOMA), is employed to quantify the transition of myocardial fiber arrangement from an organized to a disorganized state.</p><p><strong>Results: </strong>The efficacy of P-MTAI was validated in a rabbit model of DCM. Results indicate a statistically significant reduction in myocardial DOMA values in DCM-affected rabbits compared to healthy controls.</p><p><strong>Conclusion: </strong>These results demonstrating the potential of P-MTAI for early-stage DCM detection.</p><p><strong>Significance: </strong>This study provides a novel approach for the early detection of dilated cardiomyopathy, with significant clinical translational potential and application prospects.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144132140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrical Pathways Through the Intricate Network of Skeletal Muscle Fibres: Insights From MRI-Validated Numerical Modelling.","authors":"Rok Smerc, Marko Strucic, Matej Kranjc, Igor Sersa, Damijan Miklavcic, Samo Mahnic-Kalamiza","doi":"10.1109/TBME.2025.3572353","DOIUrl":"https://doi.org/10.1109/TBME.2025.3572353","url":null,"abstract":"<p><strong>Objective: </strong>Skeletal muscles exhibit pronounced anisotropy due to their highly oriented fibre structure, a property that significantly influences the spatial distribution of tissue mechanical and electrical properties. Understanding this anisotropy is critical for advancing biomedical applications such as electrical stimulation, bioelectric impedance analysis, and novel therapeutic interventions such as pulsed field ablation (PFA).</p><p><strong>Methods: </strong>We developed a numerical model incorporating realistic skeletal muscle fibre geometry at the microscale to elucidate the origins of the experimentally observed anisotropy at the bulk tissue level. To validate the model, we evaluated the skeletal muscle anisotropy using current density imaging (CDI), a magnetic resonance-based technique.</p><p><strong>Results: </strong>The developed numerical model identifies the origins of the observed anisotropy in bulk tissue. Experimental CDI measurements validate the model, confirming that the observed current anisotropy arises from the intrinsic properties of individual muscle fibres and their organization within the tissue. Remarkably, this anisotropy persists several - even up to 48 - hours post-mortem, suggesting a structural basis that transcends the level of muscle cell membranes.</p><p><strong>Conclusion: </strong>The integration of CDI with advanced modelling provides a powerful framework for understanding and leveraging skeletal muscle anisotropy in both imaging and therapeutic applications.</p><p><strong>Significance: </strong>Our study provides an experimentally validated model of skeletal muscle that is relevant to biomedical applications involving electrical treatments. It also invites further experimentation using tissues immediately after harvesting, demonstrating potential use of ex vivo tissues as models of in vivo tissue, reducing the need for experimentation with live animals and the associated ethical burden.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144127404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}