IEEE Transactions on Biomedical Engineering最新文献

{"title":"EEGDM: Learning EEG Representation with Latent Diffusion Model.","authors":"Shaocong Wang, Tong Liu, Yihan Li, Ming Li, Kairui Wen, Pei Yang, Wenqi Ji, Minjing Yu, Yong-Jin Liu","doi":"10.1109/TBME.2026.3740193","DOIUrl":"https://doi.org/10.1109/TBME.2026.3740193","url":null,"abstract":"<p><strong>Objective: </strong>Recent advances in self-supervised learning for EEG representation have largely relied on masked reconstruction, where models are trained to recover randomly masked signal segments. While effective at modeling local dependencies, the training objective of masked reconstruction does not compel the model to capture global generative constraints essential for characterizing neural activity. To address this limitation, we propose EEGDM, a novel self-supervised framework that leverages latent diffusion models to generate EEG signals as an objective.</p><p><strong>Methods: </strong>Unlike masked reconstruction, diffusion-based generation progressively denoises signals from noise to realism, compelling the model to capture holistic temporal patterns and cross-channel relationships. Specifically, EEGDM incorporates an EEG encoder that distills raw signals and their channel augmentations into a compact representation, which serves as conditional information to guide the diffusion denoising process, thereby enabling the encoder and diffusion model to be jointly optimized through the generative objective.</p><p><strong>Results: </strong>Experimental results show that EEGDM (1) reconstructs high-quality EEG signals, (2) learns robust representations, and (3) achieves competitive performance across diverse downstream tasks.</p><p><strong>Conclusion: </strong>This design endows EEGDM with a compact latent space, which not only offers ample control over the generative process but also can be leveraged for downstream tasks.</p><p><strong>Significance: </strong>EEGDM thus explores a new direction for self-supervised EEG representation learning.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108773","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":"Improving RSVP EEG Decoding Performance under Auditory Perturbation: A Dual-View Model with Robust Distillation.","authors":"Jiachen Zhang, Fu Li, Boxun Fu, Xiaohui Cai, Yifan Zhao, Yang Li, Zhifu Zhao, Minghao Dong, Yuanfang Chen","doi":"10.1109/TBME.2026.3739839","DOIUrl":"https://doi.org/10.1109/TBME.2026.3739839","url":null,"abstract":"<p><strong>Objective: </strong>Decoding event-related potentials (ERPs) from Rapid Serial Visual Presentation (RSVP) EEG offers a promising approach for target recognition in brain-computer interfaces (BCIs). However, auditory noise in real-world environments degrades target-ERP decoding, substantially reducing accuracy and robustness.</p><p><strong>Methods: </strong>We propose a novel time-frequency dual-view network (TFDV-Net) with temporal and time-frequency branches. Cross-view global features guide local feature extraction to fully exploit complementary information in both views and obtain a more complete representation of task-relevant ERP information from auditory-perturbed EEG. Additionally, dual-stream knowledge distillation transfers feature representations and inter-layer processing patterns from a clean-EEG-trained teacher through feature-distribution alignment and inter-layer relational constraints, guiding the student preserve task-relevant information under auditory perturbations.</p><p><strong>Results: </strong>Experiments used an RSVP-EEG dataset collected from nine participants under clean, urban-noise, and conversational-noise conditions. The proposed method, comprising the TFDV-Net backbone and the proposed dual-stream knowledge-distillation strategy, achieved balanced accuracies of 83.02% and 80.78% under the two noise conditions, respectively, approaching the 84.54% achieved by the TFDV-Net backbone under the clean condition. With the same dual-stream knowledge-distillation strategy, TFDV-Net outperformed the state-of-the-art baseline, TSformer, by 3.27% and 4.36% in BA under the two noise conditions, respectively.</p><p><strong>Conclusion: </strong>The proposed method combines dual-view feature extraction with dual-stream knowledge distillation, effectively improving RSVP-EEG decoding accuracy and robustness under auditory perturbations.</p><p><strong>Significance: </strong>This work provides a robust RSVP-EEG decoding approach for noisy environments, showing potential for application in nonlaboratory environments and offering insights into noise-robust decoding in other BCI paradigms.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108761","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":"Phase-Guided Self-Supervised Latent Representation Learning for sEMG-Based Gait Decoding.","authors":"Chen Wang, Jiatong Cui, Liang Peng, Ziyun Ge, Guangyu Liang, Yong Huang, Zhuyi Ma, Yunfeng Zhang, Hongyi Shao, Yixin Zhou, An-Min Zou, Zeng-Guang Hou","doi":"10.1109/TBME.2026.3738914","DOIUrl":"https://doi.org/10.1109/TBME.2026.3738914","url":null,"abstract":"<p><p>Surface electromyography (sEMG) provides a non-invasive measure of neuromuscular activity and is widely used for gait decoding in wearable and rehabilitation systems. Existing sEMG-based approaches rely on end-to-end supervised regression with dense kinematic labels, limiting scalability, generalization, and interpretability. This paper proposes a phase-guided latent representation learning framework for multichannel sEMG signals. The framework reduces reliance on continuous joint-angle supervision during encoder training by constructing a continuous phase reference from sparse gait-event annotations. A Transformer-based phase estimator learns a circular phase embedding from sEMG, which is then frozen to condition feature encoding throughout the gait cycle. An anatomically informed graph constrains spatial interactions among sEMG channels, while reconstruction and latent-dynamics consistency objectives promote informative and temporally coherent representations. The learned representations are evaluated through post-hoc knee-angle decoding using a frozen encoder and lightweight probe. Our framework achieved post-hoc knee-angle decoding performance of $R^{2} = 0.93$ under small-sample settings and cross-subject generalization of $R^{2} = 0.88$ on the level-ground walking subset of a public dataset. Ablation experiments supported the contributions of phase conditioning, anatomical graph constraints, and latent-dynamics consistency. Phase-dependent channel analysis revealed activation patterns consistent with biomechanical principles, demonstrating potential for robot-assisted rehabilitation.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108854","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-frequency Bipolar Asymmetric Pulses Enhance Tissue Ablation Efficacy with Low Neuromuscular Stimulation.","authors":"Sizhe Xiang, Yancheng Wang, Lisheng Zhao, Chenguo Yao, Shoulong Dong","doi":"10.1109/TBME.2026.3738839","DOIUrl":"https://doi.org/10.1109/TBME.2026.3738839","url":null,"abstract":"<p><p>As a novel tumor ablation technology, irreversible electroporation (IRE) has gradually become one of the important research directions in the field of tumor therapy, owing to its advantages of non-thermal effect and tissue selectivity. However, its clinical application is limited by the key issue of muscle contractions induced by neural stimulation triggered by pulsed electric fields (PEF). This issue has a significant impact on patients' intraoperative treatment and postoperative rehabilita-tion. Although the newly proposed high-frequency irreversible electroporation (H-FIRE) can alleviate neuromuscular stimula-tion, its ablation efficacy is significantly reduced due to the constraint of the \"bipolar cancellation\" effect. Existing pulse optimization strategies struggle to balance the dual requirements of neuromuscular stimulation inhibition and ablation efficiency improvement. To address this core contradiction, this study proposes a high-frequency bipolar asymmetric pulse (H-FBAP) protocol. This protocol not only retains H-FIRE's advantage of low neuromuscular stimulation but also effectively suppresses the bipolar cancellation effect by optimizing the asymmetric configuration of pulses, thus enhancing ablation efficacy. Meanwhile, this study summarizes the conditions for H-FBAP to inhibit neural excitation and the rules for improving ablation efficiency. The outcomes regarding ablation efficacy and muscle contraction were validated via simulation, in vitro experiments, and animal studies. The results demonstrated that, compared with H-FIRE, H-FBAP increased the ablation area by up to 88.77%, while its muscle contraction intensity was only 19.8% of that of IRE at the same amplitude. This study provides a novel technical solution for addressing the core bottleneck in PEF ablation technology.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108743","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":"Bilateral Latent Synergy-Informed Residual sEMG Reconstruction for Incomplete Hand Gesture Recognition in Hemiparetic Patients.","authors":"Kexiang Li, Ye Sun, Jianhua Zhang, Chuang Li, Li Wang, Dan Wang","doi":"10.1109/TBME.2026.3739304","DOIUrl":"https://doi.org/10.1109/TBME.2026.3739304","url":null,"abstract":"<p><p>Accurate decoding of incomplete hand gestures from residual paretic surface electromyography (sEMG) remains challenging after stroke because voluntary myoelectric activity is often weak, non-stationary, and contaminated by pathological co-activation. This study proposes a Latent Synergy-Informed Purification Network (LSP-Net) for reference-guided reconstruction of residual paretic sEMG. Specifically, LSP-Net jointly models synchronous paretic- and non-paretic-side sEMG, using the non-paretic signal as both bilateral reference information and a paired waveform-level supervision target for latent residual compensation, without assuming it to represent the physiological ground truth of the paretic side. The reconstructed sEMG representations are then evaluated through a wavelet-integrated CNN-Transformer for incomplete hand gesture intention recognition. Nine participants with post-stroke hemiplegia at Brunnstrom stage III or higher performed nine bilateral hand and wrist movements. Under subject-specific calibration, LSP-Net achieved an average Pearson correlation coefficient of 0.955 and an NRMSE of 0.016, indicating close reconstruction consistency with the paired non-paretic supervision targets. Furthermore, the reconstructed representations achieved an average gesture-intention recognition accuracy of 93.62%. These results suggest that latent synergy-informed reconstruction can provide more discriminative and stable sEMG representations for subject-specific post-stroke intention decoding.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108416","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":"Local brain tissue pressure monitoring based on subharmonic-assisted pressure estimation combined with shear wave elastography.","authors":"Huimin Lu, Yonghao Wen, Shi Chen, Qi Cheng, Yun Wang, Jianfeng Duan, Gang Wang, Ming Chen, Laixin Huang, Deyu Li, Weibao Qiu, Minhua Cheng, Wenkui Yu, Fei Li","doi":"10.1109/TBME.2026.3738498","DOIUrl":"https://doi.org/10.1109/TBME.2026.3738498","url":null,"abstract":"<p><strong>Objective: </strong>Traumatic brain injury (TBI) is the leading cause of mortality and disability among young people. There is an urgent clinical need for a noninvasive method that can accurately measure the local pressure in damaged brain tissue, which is of great significance for real-time monitoring of TBI progression, timely intervention, and improving the survival rate of TBI patients.</p><p><strong>Methods: </strong>We proposed a noninvasive local brain tissue pressure (BTP) measurement technique combining subharmonic-assisted pressure estimation (SHAPE) and shear wave elastography (SWE) in a craniectomy rat model. Nonlinear subharmonic scattering signals from microbubbles and Young's modulus within the injury region were acquired using a customized ultrasound scanner. Three random forest models (SHAPE alone, SWE alone, and their combination) were built to predict local BTPs, and the receiver operating characteristic (ROC) curves were used to evaluate the models' diagnostic value for elevated BTPs.</p><p><strong>Results: </strong>The SHAPE combined SWE model demonstrated optimal predictive performance, with a root-mean square error (RMSE) of merely 2.794 mmHg, and a sensitivity of 100%, specificity of 84.42%, accuracy of 84.38% for predicting BTP > 20 mmHg.</p><p><strong>Conclusion: </strong>The SHAPE+SWE combination outperforms either modality alone, suggesting that BTP is jointly determined by local blood flow pressure and the stiffness of the local brain tissue.</p><p><strong>Significance: </strong>This study represents a proof-of concept method of assessing local BTP following TBI by combining SHAPE with SWE. It can hold promise as a novel, minimally invasive, and precise alternative approach for measuring local BTP in TBI patients after further validation of clinical transcranial application.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108783","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":"Rapid Stroke Classification and Prognosis Monitoring with High-Spatiotemporal Resolution Microwave-Induced Thermoacoustic Imaging.","authors":"Yichao Fu, Yu Wang, Xinghua Wang, Bohan Zhang, Famin Huang, Huan Qin","doi":"10.1109/TBME.2026.3738546","DOIUrl":"https://doi.org/10.1109/TBME.2026.3738546","url":null,"abstract":"<p><strong>Objective: </strong>Rapid differentiation of hemorrhagic and ischemic strokes and dynamic monitoring of stroke evolution are essential for timely intervention and prognosis assessment. However, current imaging approaches face challenges in achieving both rapid stroke differentiation and dynamic monitoring. This study aims to develop a high-spatiotemporal-resolution microwave induced thermoacoustic imaging (HR-MTAI) technique for rapid stroke differentiation and monitoring of stroke evolution.</p><p><strong>Methods: </strong>We developed an HR-MTAI system with a B-mode imaging frame rate of 100 Hz and a lateral resolution of 200 μm by exploiting thermoacoustic contrast associated with tissue electrical properties and pathological changes. Rodent models of hemorrhagic and ischemic stroke were established to evaluate the capability of HR-MTAI for stroke differentiation and dynamic assessment.</p><p><strong>Results: </strong>HR-MTAI revealed opposite thermoacoustic contrast patterns between hemorrhagic and ischemic lesions, supporting rapid differentiation of the two stroke types. The system further provided sub millimeter three-dimensional localization and quantitative assessment of lesion regions. In ischemic stroke models, HR-MTAI captured the temporal evolution of both permanent and transient cerebral ischemia models.</p><p><strong>Conclusion: </strong>HR-MTAI enables rapid, high-resolution, and quantitative visualization of stroke pathology, providing a comprehensive approach for stroke classification and dynamic monitoring.</p><p><strong>Significance: </strong>HR-MTAI provides a noninvasive approach for rapid stroke classification and prognostic monitoring in preclinical models, offering a potential imaging strategy for investigating stroke dynamics and facilitating future development of rapid stroke assessment technologies.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108848","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":"SARM: Benchtop Evaluation of a Task-Driven Two-Stage Deep Learning Framework for Stimulation Artifact Removal in Peripheral Neural Decoding.","authors":"Brian Z H Lim, Markus Drealan, Anh Tuan Nguyen, Byeongchan Jeong, Maral Daneshyan, Xianyu Chen, Qi Zhao, Zhi Yang","doi":"10.1109/TBME.2026.3737488","DOIUrl":"10.1109/TBME.2026.3737488","url":null,"abstract":"<p><strong>Objective: </strong>This manuscript introduces a novel two-stage training framework and deep-learning model for recovering peripheral neural signals contaminated by electrical stimulation artifacts. The approach is designed to preserve motor-decoding accuracy during simultaneous neural recording and stimulation.</p><p><strong>Methods: </strong>In this work, We combine supervised learning with policy-gradient reinforcement learning based on REINFORCE using paired signals generated by a custom in-house analog data-synthesis system. A Convolutional Long Short-Term Memory (ConvLSTM) network captures spatial and temporal dependencies across multi-channel recordings. The model first undergoes supervised signal-reconstruction training and is then fine-tuned using Self-Critical Sequence Training (SCST) with task-driven rewards derived from motor-decoding performance. This strategy enables optimization despite non-differentiable downstream signal processing and feature extraction.</p><p><strong>Results: </strong>Using previously recorded peripheral nerve signals from an amputee participant with experimentally injected artifacts across multiple stimulation amplitudes, the proposed framework improved artifact suppression and signal recovery compared with conventional blanking and linear interpolation. It restored signal integrity across multiple channels and improved motor-decoding accuracy, particularly under high-amplitude stimulation.</p><p><strong>Conclusion: </strong>These results demonstrate the feasibility of task-driven artifact removal for peripheral neural decoding in a controlled benchtop setting. The proposed hybrid training approach provides a practical means of optimizing deep neural networks using decoding-aligned objectives. However, validation used replayed neural recordings and experimentally injected artifacts. Further in vivo closed-loop evaluation is needed to account for physiological electrode-tissue interactions, biological artifacts, and real-time sensorimotor feedback. This work provides a foundation for developing robust bidirectional peripheral neural interfaces that combine motor decoding with sensory feedback.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149060601","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 Modular Wireless Pressure Sensing Framework with Surface-Dependent Calibration for Data-Guided Pressure Injury Monitoring.","authors":"Tse-Hua Huang, Hui-Ling Hsieh, Chung-Tao Huang, Yi-Ling Hou, Dian-Ru Li","doi":"10.1109/TBME.2026.3737148","DOIUrl":"https://doi.org/10.1109/TBME.2026.3737148","url":null,"abstract":"<p><strong>Objective: </strong>Pressure injury (PI) prevention requires continuous assessment of localized tissue loading, yet conventional repositioning protocols are labor-intensive and do not account for inter-subject or support-surface variability. This study developed and evaluated a modular wireless sensing framework, the Modular Pressure-Responsive Embedded Sensing System (M-PRESS), for data-guided PI monitoring using localized load sensing, surface-dependent calibration, and threshold-guided intervention.</p><p><strong>Methods: </strong>M-PRESS is a site-configurable platform designed for anatomically high-risk regions. A surface-dependent calibration method was implemented to compensate for rigid and compliant support conditions. In Phase I, healthy participants across body mass index (BMI) groups were studied to characterize load-skin response relationships and establish BMI-informed alert thresholds. In Phase II, a pilot quasi-experimental study compared M-PRESS-guided monitoring with standard repositioning care in high-risk inpatients.</p><p><strong>Results: </strong>Phase I identified distinct BMI-dependent sustained-loading patterns, supporting alert thresholds of 0.7 kgf for underweight participants and 0.9 kgf for overweight participants. In Phase II, no new PIs developed in either group during the 4-h observation period. Compared with standard care, M-PRESS-guided monitoring was associated with a 93% shorter nurse-assisted intervention time (0.63 vs. 9.32 min), and operational variability was significantly lower (Moses test, P < 0.001).</p><p><strong>Conclusions: </strong>M-PRESS demonstrated preliminary feasibility as a modular load-based sensing framework for data-guided PI monitoring in high-risk inpatients.</p><p><strong>Significance: </strong>By integrating site-specific sensing, surface-dependent calibration, BMI-informed thresholding, and targeted local load relief, the proposed system provides a feasible monitoring-to-intervention framework for data-guided PI monitoring and future patient-specific intervention studies.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149038395","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":"Integrating Low-loss High-permittivity Ceramic Helmet with RF Head Array Coil to Largely Enhance Coil Sensitivity and SNR for Human Brain MRI at 7T.","authors":"Soo Han Soon, Xin Li, Xiao-Hong Zhu, Qing X Yang, Michael T Lanagan, Kamil Ugurbil, Wei Chen","doi":"10.1109/TBME.2026.3737200","DOIUrl":"https://doi.org/10.1109/TBME.2026.3737200","url":null,"abstract":"<p><p>Use of proper high-permittivity material could improve radiofrequency (RF) coil efficiency and imaging signal-to-noise ratio (SNR) for human MRI applications. We designed, constructed, and evaluated RF transmit and receive head array coils integrated with a low-loss high permittivity ceramic (HPC) helmet with the relative permittivity (ϵ_r) of 200 and four-segment helmet structure for human brain MRI application at 7 Tesla (T). Both electromagnetic simulation and experimental MRI results consistently demonstrated that the novel HPC helmet largely increased the RF reception (B₁^-) and transmission (B₁⁺) magnetic field strength by 82% and 19%, respectively, and net imaging SNR gain by 64% across the entire human brain compared to the baseline condition using the same RF coil configuration without the HPC helmet. The findings conclude that the new HPC helmet effectively enhances SNR primarily by boosting the RF coil detection sensitivity while introducing only a small noise increase (11%). The HPC helmet moderately reduced coil to-coil coupling of the receive array elements. Compared to the baseline condition, HPC helmet demonstrated an approximate 10 dB reduction in noise correlation and 2% to 7% increase in the 97^th percentile of 1/g-factors with various acceleration factors, ultimately facilitating high-quality parallel imaging reconstruction. The four-segment HPC helmet showed a nearly 50% lower specific absorption rate (SAR) than the monolithic HPC helmet. This work demonstrates the advancement of the integrated HPC helmet and RF coil technology to largely improve the imaging quality at 7T for whole human brain MRI applications, and potentially for translation.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149038379","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}