IEEE Transactions on Biomedical Engineering最新文献

{"title":"Cardiac Microanatomy Imaging Using Forward-Viewing Optical Coherence Tomography Endoscope.","authors":"David L Vasquez, Franziska Einmuller, Ines Latka, Kanchan Kulkarni, Nestor Pallares-Lupon, Marion Constantin, James Marchant, Virginie Loyer, Stephane Bloquet, Dounia El Hamrani, Jerome Naulin, Wolfgang Drexler, Jurgen Popp, Angelika Unterhuber, Marco Andreana, Richard D Walton, Iwan W Schie","doi":"10.1109/TBME.2025.3616493","DOIUrl":"10.1109/TBME.2025.3616493","url":null,"abstract":"<p><strong>Objective: </strong>Due to limitations in current imaging technologies detecting subtle cardiac microstructural changes that can lead to sudden cardiac death is a significant clinical challenge. To address this problem, we developed a forward-viewing optical coherence tomography (OCT) endoscope for the detection of relevant cardiac microstructures in the subendocardium, including Purkinje fibers, scar tissue, surviving myocytes, and adipose tissue.</p><p><strong>Methods: </strong>An endoscopic probe based on the scanning fiber principle was developed for OCT measurements in contact. The probe was evaluated in freshly excised ovine hearts exhibiting chronic myocardial infarction. Relevant regions within the cardiac chamber were measured, and distinctive microstructures were identified, characterized, and subsequently corroborated using Masson's trichrome staining. The volumetric imaging data were used to train a convolutional neural network (CNN) to detect Purkinje fibers, enabling the reconstruction of their 3D morphology.</p><p><strong>Results: </strong>We were able to distinguish between healthy myocardium, fibrotic remodeling, and critical elements of the cardiac conduction system. Our findings demonstrate the capability of this technology to provide detailed images of cardiac microstructures in large mammal hearts.</p><p><strong>Conclusion: </strong>A novel application of forward-viewing endoscopic OCT in cardiology is demonstrated by visualizing cardiac microstructures within the subendocardium at depths accessible by optical imaging modalities.</p><p><strong>Significance: </strong>By enhancing visualization at the cellular level, this method may contribute to a better understanding of cardiac physiology and pathology, potentially extending future diagnostic and therapeutic strategies.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":"1852-1862"},"PeriodicalIF":4.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145206313","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":"Detecting Beta-Amyloid Plaque via Low Rank Based Orthogonal Projection and Spatial-Spectrum Detector Using High-Resolution Quantitative Susceptibility Mapping for Preclinical Studies.","authors":"Jie Chen, Xinyue Han, Zhuoheng Liu, Chengqian Zhou, Rui Hu, Saira Tabassam, Season K Wyatt-Johnson, Adrian L Oblak, Randy R Brutkiewicz, Mingquan Lin, Nian Wang","doi":"10.1109/TBME.2025.3614233","DOIUrl":"10.1109/TBME.2025.3614233","url":null,"abstract":"<p><p>Detecting beta-amyloid (A$beta$) plaques at different stages is crucial for accurate assessment and effective intervention in Alzheimer's disease (AD). In this study, we developed a novel method for reliably identifying A$beta$ plaques, characterized by sparse negative susceptibility values, in preclinical studies using high-resolution quantitative susceptibility mapping (QSM), named QSM-PLAQUE A$beta$ Detector. This approach decomposes a high-resolution QSM MRI image into three components: L (representing the background subspace), S (representing the signals subspace), and N (representing the noise). Subsequently, we established an orthogonal subspace based on L to eliminate the background from the sum of L and S. Finally, a plaque detection process was conducted, where A$beta$ plaques were identified based on the neighbor spectrum (NS) of a voxel being tested rather than just analyzing the voxel itself alone. Experiments demonstrated that the proposed method effectively detects A$beta$ plaques of varying shapes and intensities across the entire mouse brain. It shows robust performance across histology, high-resolution QSM MRI, and synthesized datasets, without requiring training samples. The QSM-PLAQUE A$beta$ Detector provides a practical framework for identifying and visualizing A$beta$ plaques in preclinical studies, offering a new strategy for quantitative assessment of A$beta$ plaques and may guide the development of advanced techniques for preclinical AD research.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":"1734-1745"},"PeriodicalIF":4.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12469857/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145137312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Referenceless Proton Resonance Frequency Thermometry Using Deep Learning with Self-Attention.","authors":"Yueran Zhao, Chang-Sheng Mei, Nathan J McDannold, Meiyun Wang, Guofeng Shen, Shenyan Zong","doi":"10.1109/TBME.2026.3689263","DOIUrl":"https://doi.org/10.1109/TBME.2026.3689263","url":null,"abstract":"<p><p>Proton resonance frequency (PRF) MR thermometry provides temperature feedback for MR-guided focused ultrasound (FUS), but conventional baseline-referenced thermometry is vulnerable to inter-scan motion and time-varying background phase, which can destabilize temperature monitoring. Referenceless approaches mitigate these issues by estimating the background phase from each frame; however, in transcranial applications they remain sensitive to susceptibility-induced phase discontinuities, often leading to reduced accuracy in heated regions and false-positive temperature elevation in surrounding tissue. This study developed and evaluated a deep learning-based referenceless PRF thermometry method that reconstructs background complex MR images to improve heated-region accuracy while maintaining stable background estimation. In this retrospective single-center study, 32 patients with essential tremor undergoing transcranial FUS (87 sonications, 1416 images) were included. Data from 28 patients were used for training and validation, and 4 patients (13 sonications, 146 images) formed the test set. A complex-valued self-attention-augmented U-Net was designed for background complex reconstruction. In heated regions, the proposed method achieved MAE = 0.64°C, Std = 0.80°C, and RMSE = 0.82°C, outperforming established referenceless techniques. The Dice coefficient of the 43°C isotherm reached 0.76, and background temperature change remained close to 0°C (MAE = 0.20°C), indicating strong suppression of spurious heating. Bland-Altman analysis showed limits of agreement from -1.37°C to +1.77°C with bias 0.20°C and R² = 0.99. These results demonstrate improved accuracy and stability of referenceless PRF thermometry for transcranial FUS monitoring, supporting more reliable intra-procedural temperature control, with potential applicability to motion-prone anatomies where baseline referenced thermometry is less reliable.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147814598","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":"EMBC Special Issue: Neural Style Transfer-Based Denoising of Seismocardiogram Signals Under Dynamic Conditions.","authors":"Dunya Moradi, Berke Kizir, Beren Semiz","doi":"10.1109/TBME.2026.3688416","DOIUrl":"https://doi.org/10.1109/TBME.2026.3688416","url":null,"abstract":"<p><p>Seismocardiogram (SCG) signals capture the mechanical dynamics of cardiac activity, but their clinical utility is severely limited by motion artifacts during ambulatory monitoring. To overcome this challenge, we propose a neural style transfer (NST)-based denoising framework that converts motion-contaminated SCG recordings into morphology-preserving, rest-like representations. Our method leverages time-frequency spectrograms obtained from continuous wavelet transforms and a pre-trained convolutional neural network (VGG19) to suppress exercise-induced distortions while maintaining physiologically relevant timing and morphology. Across 20 participants, the proposed approach substantially enhanced signal fidelity, improving signal-to-noise ratio and peak signal-to-noise ratio by 176.7% and 152.1%, respectively, and reducing mean squared error and mean absolute error by 95.1% and 83.6%. Structural similarity increased by 70.3%, and correlation with the resting reference more than doubled. Heart rate estimated from denoised signals showed excellent agreement with electrocardiogram measurements, yielding an average error of only 0.89 beats per minute (0.73%). Furthermore, comparative evaluation demonstrated that the proposed restyling and denoising approach consistently outperformed state-of-the-art denoising techniques-including empirical mode decomposition variants, variational mode decomposition, Savitzky-Golay filtering, moving-average filtering, and wavelet-based reconstruction-achieving the lowest heart rate estimation error (0.89 bpm RMSE). Additionally, a controlled simulation confirmed the framework's restyling capability under known ground-truth conditions, yielding a heart rate estimation error of only 0.15 bpm relative to the true reference. These results demonstrate that neural style transfer enables physiology-consistent reconstruction of cardiac mechanical signals in dynamic conditions and represents a highly promising direction toward motion-resilient wearable cardiac monitoring.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147814533","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":"Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models.","authors":"Yihe Wang, Zhiqiao Kang, Bohan Chen, Yu Zhang, Xiang Zhang","doi":"10.1109/TBME.2026.3686229","DOIUrl":"https://doi.org/10.1109/TBME.2026.3686229","url":null,"abstract":"<p><p>Event-related potential (ERP), a specialized paradigm of electroencephalographic (EEG), reflects neurological responses to external stimuli or events, generally associated with the brain's processing of specific cognitive tasks. ERP plays a critical role in cognitive analysis, the detection of neurological diseases, and the assessment of psychological states. Recent years have seen substantial advances in deep learning-based methods for spontaneous EEG and other non-time-locked task-related EEG signals. However, their effectiveness on ERP data remains underexplored, and many existing ERP studies still rely heavily on manually extracted features. In this paper, we conduct a comprehensive benchmark study that systematically compares traditional manual features (followed by a linear classifier), deep learning models, and pre-trained EEG foundation models for ERP analysis. We establish a unified data preprocessing and training pipeline and evaluate these approaches on two representative tasks, ERP stimulus classification and ERP-based brain disease detection, across 12 publicly available datasets. Furthermore, we investigate various token-embedding strategies within advanced Transformer architectures to identify embedding designs that better suit ERP data. Our study provides a landmark framework to guide method selection and tailored model design for future ERP analysis.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147814563","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":"Exploration of the Pitch Shift Reflex using High-Speed Videolaryngoscopy and Electroencephalography.","authors":"T Schelhorn, B Peschel, M Dollinger, M Bingold, R Uhl, D Abur, U Hoppe","doi":"10.1109/TBME.2026.3685149","DOIUrl":"https://doi.org/10.1109/TBME.2026.3685149","url":null,"abstract":"<p><strong>Objective: </strong>Propose and validate a pitch shift reflex (PSR) experiment setup that includes high-speed videolaryngoscopy (HSV). Investigate the impact of nasal laryngoscopy on PSR and voice parameters.</p><p><strong>Methods: </strong>Through synchronous measurements of acoustic voice signals, electroencephalography (EEG) signals and high-speed video recordings of the vocal folds, additional features of responses to sudden pitch shifts in voice feedback can be acquired. A setup for investigation of the PSR was proposed. A total of 27 adults with typical hearing and physiological voices participated in frequency acuity tests and PSR experiments.</p><p><strong>Results: </strong>The magnitude of average responses to the pitch shift stimulus was found to have a significant correlation with participant age (ρ = 0.398, p = 0.04). Although there was a high rate of responses following the direction of the pitch shift (15 out of 27), the peak parameters in audio and EEG were aligned with reference literature. The impact of nasal laryngoscopy on voice stability and PSR responses was found to be minimal.</p><p><strong>Conclusion: </strong>The proposed setup and paradigm represent a valid method to investigate the PSR, expanding the breadth of collected parameters by direct metrics of vocal fold motion Significance: HSV can be employed in future PSR studies and is currently underrepresented in PSR research.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147770062","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":"Pro-Tuning: Prototype Tuning of Foundation Models for Volumetric Medical Image Segmentation.","authors":"Youyong Kong, Hongxuan Li, Sheng Zhang, Minheng Chen","doi":"10.1109/TBME.2026.3688074","DOIUrl":"https://doi.org/10.1109/TBME.2026.3688074","url":null,"abstract":"<p><p>Accurate volumetric medical image segmentation plays a crucial role in identifying and analyzing human organs, tissues, or areas of lesions. It serves as a critical foundation for clinical diagnosis and treatment planning, guides surgical procedures, and facilitates early disease intervention. Recently, foundation models have been extensively implemented in the field of volumetric medical image segmentation, achieving remarkable outcomes. Nevertheless, neither the direct application of foundation models nor fine-tuning them with point or box prompts for specific medical segmentation tasks has yielded satisfactory results. In this paper, we propose a simple yet efficient method named Pro-Tuning for tuning medical foundation models. By utilizing a pre-trained Prototype Insight Network, prototype features are extracted at the semantic level of the target organ without introducing additional prompts. Furthermore, to overcome the observed limitations in the applicability of Prototype Insight Network and adapt them to specific tasks in areas with densely populated multiple target organs, we introduce a Prototype Projection Network that employs target position-encoded image embeddings to predict two projection parameters to tailor prototype features. Without additional prompts, our method greatly improves the tuning performance of medical foundation models in specific volumetric segmentation tasks. We validate the performance of our framework on 13 medical datasets covering brain, neck, chest, and abdomen regions. Our method surpasses other foundation model fine-tuning methods on ten major organs in the dataset, achieving an average Dice score of 83.26%.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147770155","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":"X-ray Source Motion Deblurring Framework for Fast Scan in Digital Breast Tomosynthesis.","authors":"Subong Hyun, Seoyoung Lee, Ilwoong Choi, Choul Woo Shin, Seungryong Cho","doi":"10.1109/TBME.2026.3688683","DOIUrl":"https://doi.org/10.1109/TBME.2026.3688683","url":null,"abstract":"<p><strong>Objective: </strong>Accelerate wide-angle digital breast tomosynthesis (DBT) by reducing X-ray source motion blur while accounting for ripple artifacts inherent to limited-angle acquisition.</p><p><strong>Methods: </strong>We model the in-plane point-spread function (PSF) as a depth-dependent 1D kernel and perform non-blind, slice-wise post-reconstruction deblurring. The framework has two components. A High-Attenuation Artifact Reduction (HAR) module segments high-attenuation (HA) regions and suppresses their ripple artifacts. A Ripple Artifact-Considered Deblurring (RAD) module alternates analytical data-fitting with a convolutional neural network (CNN)-based regularizer. RAD takes multiple initial deblurred estimates to implicitly handle ripple artifacts arising from soft tissue and avoid secondary ringing while restoring in-plane sharpness.</p><p><strong>Results: </strong>On both numerical and physical phantom data, the method visually enhances lesion visibility and preserves textures without introducing ringing artifacts, while quantitatively showing promising results across evaluation metrics.</p><p><strong>Conclusion: </strong>A ripple-aware deblurring pipeline enables faster wide-angle DBT by allowing higher tube speeds without compromising image quality.</p><p><strong>Significance: </strong>The proposed approach offers a practical path to shorten compression time and improve clinical throughput by jointly suppressing HA-induced artifacts and source motion blur while maintaining perceptual fidelity.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147770240","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":"Reducing the physical effort of wearing a walking boot using a passive hip exosuit through enforced exploration training.","authors":"Inigo Sanz-Pena, Sabrina Sullivan, Prakyath Kantharaju, Jaeha Yang, Dimuthu K Arachchige, Giuk Lee, Myunghee Kim","doi":"10.1109/TBME.2026.3684355","DOIUrl":"https://doi.org/10.1109/TBME.2026.3684355","url":null,"abstract":"<p><strong>Background: </strong>Ankle injuries can be detrimental due to their long- recovery times. A walking boot (WB) is the most prescribed treatment. However, they disrupt gait biomechanics, increasing the metabolic cost of walking and the risk of further injuries.</p><p><strong>Research question: </strong>Does using a hip exosuit minimize the adverse effects of wearing a WB, improving gait spatio-temporal parameters, and reduce energy expenditure?</p><p><strong>Methods: </strong>We investigated the effects of a passive hip exosuit and enforced exploration training in users wearing a WB. Subjects wore a WB to simulate the effects of ankle injuries during recovery.</p><p><strong>Results: </strong>The results indicate the benefits of wearing the hip exosuit and training. Metabolic cost reductions of 6.2 ± 1.5% (p < 0.001) between the post training and pre-training and 3.5 ± 2.8% (p = 0.03) between the post training and no exosuit conditions were found. Training with the exosuit resulted in positive gait modifications in patients with ankle injuries associated with gait retraining. The effects of enforced exploration training in gait kinematics wearing the exosuit resulted in an increased maximum hip flexion of 6.04 ± 3.56° and 6.59 ± 5.16° for the boot and free leg, respectively, compared to not wearing the exosuit. Spatiotemporal parameter modifications were adopted after training, resulting in metabolic reductions. Some subjects varied their step frequency, while others varied their step length and width.</p><p><strong>Significance: </strong>The outcomes from this study show the potential benefits that hip exosuits could have in clinical sports rehabilitation of ankle injuries wearing a WB.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147770207","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 Smart Projective Imaging and Navigation System for Oral and Maxillofacial Surgery.","authors":"Alston Zhang, Yi Wang, Hui Peng, Qingdong Zhang, Mingzhai Sun, Peng Yao, Shuwei Shen, Ru Zhang, Min Ye, Chuanjun Chen, Ronald X Xu","doi":"10.1109/TBME.2026.3687733","DOIUrl":"https://doi.org/10.1109/TBME.2026.3687733","url":null,"abstract":"<p><strong>Objective: </strong>In oral and maxillofacial surgery (OMS), surgical outcomes often depend significantly on the subjective experience of the surgeon. While conventional navigation systems, such as those using guide plates or fiducial markers, offer some guidance, they are hampered by instability, obtrusive hardware, and limited accuracy. Markerless navigation presents a promising alternative, but current approaches are constrained by a reliance on specific textures, scarce training data, and inefficient visualization.</p><p><strong>Methods: </strong>This paper introduces a Smart Projective Imaging and Navigation System (SPIN) that integrates synthetic data-driven learning, coaxial augmented-reality (AR) projection, and real-time structured-light 3D scanning for markerless dynamic surgical navigation. Our approach utilizes no more than two teeth as natural markers to enable high-precision pose estimation, even in low-texture regions. To overcome the data scarcity challenge, we develop a preoperative platform that generates a diverse synthetic dataset for robust AI model training, eliminating the dependency on real surgical imagery.</p><p><strong>Results: </strong>Experiments on dental phantoms, patient-specific phantoms, and representative clinical cases demonstrate that the SPIN system achieves submillimeter accuracy and exhibits strong robustness.</p><p><strong>Conclusion: </strong>The proposed SPIN system provides an accurate, intuitive, and markerless navigation solution for OMS.</p><p><strong>Significance: </strong>This work establishes a novel and clinically viable framework for dynamic surgical navigation, demonstrating significant potential for broad adoption in real-world surgical environments.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147770114","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}