IEEE Transactions on Biomedical Engineering最新文献

{"title":"Ultrafast Simultaneous T₁, T₂, T₂*, PD, ΔB₀, and B₁ Mapping via Longitudinal Magnetization Controlled MOLED Acquisition.","authors":"Weikun Chen, Taishan Kang, Qing Lin, Xinyu Guo, Jian Wu, Simin Li, Yuchen Zheng, Jianzhong Lin, Liangjie Lin, Jiazheng Wang, Xiaobo Qu, Zhong Chen, Shuhui Cai, Congbo Cai","doi":"10.1109/TBME.2025.3590286","DOIUrl":"https://doi.org/10.1109/TBME.2025.3590286","url":null,"abstract":"<p><strong>Objective: </strong>Multi-parametric quantitative mag- netic resonance imaging (mqMRI) provides comprehensive and accurate information about tissue microstructure and holds significant clinical value for the diagnosis and treatment of diseases. However, conventional methods require long scan time, leading to registration errors and physiological variability between different sequence acqui- sitions. This study aims to propose an advanced imaging method that addresses these limitations.</p><p><strong>Methods: </strong>A novel approach called longitudinal magnetization controlled multiple overlapping-echo detachment (LMC-MOLED) imaging was proposed. LMC-MOLED leverages a deep neural network trained on synthetic data generated from Bloch simulation, incorporating non-ideal factors such as B0 and B1 inhomogeneities to efficiently reconstruct parametric maps.</p><p><strong>Results: </strong>LMC-MOLED enables simulta- neous quantification of T1, T2, T2*, proton density (PD), ΔB0, and B1 parameters in approximately 1.2 seconds per slice. Validation experiments using numerical brain, phantom, and human brains demonstrate its excellent performance, particularly in terms of acquisition speed, image quality, and robustness. Additionally, LMC-MOLED effectively corrects distortions introduced by long echo train acquisition.</p><p><strong>Conclusion and significance: </strong>LMC-MOLED offers a rapid, robust solution for mqMRI, providing multi-parametric mapping in a single scan with signify- cantly reduced acquisition time. It holds potential to improve diagnostic accuracy and alleviate patient burden.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663898","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":"IEEE Engineering in Medicine and Biology Society Publication Information","authors":"","doi":"10.1109/TBME.2025.3580126","DOIUrl":"https://doi.org/10.1109/TBME.2025.3580126","url":null,"abstract":"","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 8","pages":"C2-C2"},"PeriodicalIF":4.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11084965","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657394","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":"IEEE Transactions on Biomedical Engineering Handling Editors Information","authors":"","doi":"10.1109/TBME.2025.3580130","DOIUrl":"https://doi.org/10.1109/TBME.2025.3580130","url":null,"abstract":"","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 8","pages":"C4-C4"},"PeriodicalIF":4.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11084999","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657463","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":"Knowledge-augmented Patient Network Embedding-based Dynamic Model Selection for Predictive Analysis of Pediatric Drug-induced Liver Injury.","authors":"Linjun Huang, Zixin Shi, Fei Tang, Haolin Wang","doi":"10.1109/TBME.2025.3590149","DOIUrl":"https://doi.org/10.1109/TBME.2025.3590149","url":null,"abstract":"<p><strong>Objective: </strong>To address the challenges of developing machine learning frameworks for Electronic Health Records (EHRs)-based predictive tasks, such as the intricate occurrence mechanism of clinical events, patient diversity, and the inherent limitations of real-world data like data incompleteness and class imbalance, we propose the Knowledge-augmented Patient Network embedding-based Dynamic model Selection (KPNDS) framework, focusing on two key aspects: dynamically selecting the most suitable model for each individual and integrating biomedical knowledge into the framework.</p><p><strong>Methods: </strong>KPNDS utilizes graph machine learning algorithms to generate patient embeddings from a knowledge-augmented network which integrates data from a diverse range of data sources including EHRs, drug-related information, toxicogenomics data and other relevant information to enrich the understanding of patients. A meta-learning based framework is adopted to dynamically select the optimal classifiers based on the latent patient representations to perform individualized risk prediction. Multi-Layer Perceptron, Transformer and Kolmogorov-Arnold Networks are used as meta-classifiers to enhance the selection of the optimal classifiers for each patient.</p><p><strong>Results: </strong>The KPNDS framework was validated for the early prediction of drug-induced liver injury (DILI) in pediatric patients. Experimental results show that it outperforms common baseline models and dynamic ensemble selection methods.</p><p><strong>Conclusion: </strong>The KPNDS framework effectively integrates domain knowledge, graph-based machine learning and dynamic model selection strategies, thereby enhancing predictive performance.</p><p><strong>Significance: </strong>The KPNDS framework seamlessly integrates knowledge-augmented networks with dynamic model selection techniques, which has the potential to enable more accurate risk assessment and personalized medicine in complex scenarios, highlighting a novel approach to integrating external knowledge with data-driven models.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144659057","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":"TFANet: A Time-Frequency Aware Network With Joint Entropy Coding for High-Ratio EEG Compression.","authors":"Xiangcun Wang, Xi Wu, Yuan Li, Xia Wu, Jiacai Zhang","doi":"10.1109/TBME.2025.3590270","DOIUrl":"https://doi.org/10.1109/TBME.2025.3590270","url":null,"abstract":"<p><strong>Objective: </strong>The transmission and storage of large-scale EEG data require high-ratio EEG compression. However, existing EEG compression methods struggle to achieve high compression efficiency while preserving reconstruction quality due to statistical redundancy and the loss of high-frequency information at extreme compression ratios.</p><p><strong>Methods: </strong>To address these limitations, we propose TFANet, a novel high-ratio EEG compression framework that integrates autoencoder learning with entropy coding to optimize the latent space distribution, effectively reducing redundancy and maximizing compression efficiency. To address the issue of high-frequency information loss in existing methods, which leads to significant detail degradation at high compression ratios, we propose the frequency attention block (FAB) and the time-frequency enhancement block (TFEB). FAB leverages fast fourier transform for global frequency-aware compression, while TFEB integrates discrete wavelet transform with channel attention to preserve fine-grained time-frequency features. By utilizing global frequency awareness to guide local feature extraction, our approach ensures more effective retention of critical EEG details.</p><p><strong>Results: </strong>Experiments on public EEG datasets show that TFANet achieves an unprecedented 333× compression ratio while maintaining superior reconstruction quality, significantly outperforming existing methods.</p><p><strong>Conclusion: </strong>These results highlight TFANet's potential for large-scale EEG applications, enabling efficient data transmission and storage while preserving critical neural information.</p><p><strong>Significance: </strong>TFANet reduces the storage and transmission costs of large-scale EEG data, laying the foundation for its practical applications in medical diagnosis and remote monitoring.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144659059","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":"BDEC: Brain Deep Embedded Clustering Model for Resting State fMRI Group-Level Parcellation of the Human Cerebral Cortex.","authors":"Jianfei Zhu, Xiaoxiao Ma, Baichun Wei, Zhicai Zhong, Hui Zhou, Feng Jiang, Haiqi Zhu, Chunzhi Yi","doi":"10.1109/TBME.2025.3590258","DOIUrl":"https://doi.org/10.1109/TBME.2025.3590258","url":null,"abstract":"<p><strong>Objective: </strong>To develop a robust group-level brain parcellation method using deep learning based on resting-state functional magnetic resonance imaging (rs-fMRI), aiming to release the model assumptions made by previous approaches.</p><p><strong>Methods: </strong>We proposed Brain Deep Embedded Clustering (BDEC), a deep clustering model that employs a loss function designed to maximize inter-class separation and enhance intra-class similarity, thereby promoting the formation of functionally coherent brain regions.</p><p><strong>Results: </strong>Compared to ten widely used brain parcellation methods, the BDEC model demonstrates significantly improved performance in various functional homogeneity metrics. It also showed favorable results in parcellation validity, downstream tasks, task inhomogeneity, and generalization capability.</p><p><strong>Conclusion: </strong>The BDEC model effectively captures intrinsic functional properties of the brain, supporting reliable and generalizable parcellation outcomes.</p><p><strong>Significance: </strong>BDEC provides a useful parcellation for brain network analysis and dimensionality reduction of rs-fMRI data, while also contributing to a deeper understanding of the brain's functional organization.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144659056","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":"Respiratory Signal Estimation for Free-hand 2D Ultrasound Image via Heterogeneous Alignment and Conditional Generative Learning.","authors":"Jingshu Li, Tianyu Fu, Hong Song, Jingfan Fan, Danni Ai, Deqiang Xiao, Ying Gu, Jian Yang","doi":"10.1109/TBME.2025.3590271","DOIUrl":"https://doi.org/10.1109/TBME.2025.3590271","url":null,"abstract":"<p><strong>Objective: </strong>2D ultrasound sequence captures respiration-induced morphological variation of organs and is the most widely used for estimating the signal that represents the respiratory motion. However, the spatial motion introduced by clinical free-hand ultrasound acquisition mixes with respiratory motion, reducing the estimation accuracy. This study proposes an unsupervised respiratory signal estimation method based on heterogeneous information alignment to address spatial motion interference caused by free-hand acquisition.</p><p><strong>Methods: </strong>A heterogeneous graph of ultrasound slices is created to isolate organ respiratory motion and ultrasound probe spatial motion. The hierarchical attention aggregation is employed to learn respiratory and spatial correlation independently, mapping slices into a unified respiratory feature space. Then, the task of respiratory signal estimation is reframed as a mapping learning problem, translating the image into the unified respiratory motion feature space through conditional generative learning. The respiratory signal is the feature of the slice in this unified space.</p><p><strong>Results: </strong>The correlation between the estimated results of the proposed method and the ground truth in various free-hand acquisition modes exceeded 93%, reaching up to 97%. The time required by the model to estimate respiratory signal for a single slice is roughly 2 ms.</p><p><strong>Conclusion: </strong>The results demonstrate that the proposed method has higher accuracy and robustness than other methods.</p><p><strong>Significance: </strong>The proposed unsupervised method, without the constraint of fixed image acquisition positions, is better suited to ultrasound imaging and provides the foundation for real-time 4D respiratory ultrasound imaging.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144659058","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":"The Second Skin: A Wearable Sensor Suite that Enables Real-Time Human Biomechanics Tracking Through Deep Learning.","authors":"Ryan T F Casey, Christoph P O Nuesslein, Felicia Davenport, Jason Wheeler, Anirban Mazumdar, Gregory Sawicki, Aaron J Young","doi":"10.1109/TBME.2025.3589996","DOIUrl":"https://doi.org/10.1109/TBME.2025.3589996","url":null,"abstract":"<p><strong>Objective: </strong>Real-time determination of human kinematics and kinetics could advance biomechanics research and enable valuable applications of biofeedback and generalizable exoskeleton control. This work aims to investigate a taskindependent, user-independent method for obtaining precise realtime joint state estimation across lower-body joints during a wide variety of tasks.</p><p><strong>Methods: </strong>We developed a generalizable sensing approach using a suit comprised of inertial measurement units (IMUs) and pressure insoles. With the suit, we collected a dataset of 33 tasks commonly performed during construction and hazardous waste cleanup (N = 10). We then trained deep learning user-independent, task-agnostic models to estimate joint lowerbody kinematics and dynamics using only worn sensor data. We likewise computed joint kinematics and dynamics analytically from sensor data to serve as a comparison tool for model results.</p><p><strong>Results: </strong>Our models achieved overall angle estimation root-meansquared-errors (RMSE) of 6.56±.92°, 8.60±1.01°, 7.58±.89°, and 6.00±.73° compared to 13.9±.1.3°, 15.31±1.0°, 10.76±.70°, and 7.56±.48° via analytical methods at the lower back, hip, knee, and ankle, respectively. Likewise, our models achieved overall normalized moment estimation RMSEs of .207±.069 Nm/kg, .242±.044 Nm/kg, .202±.038 Nm/kg, and .193±.034 Nm/kg compared to .306±.036 Nm/kg, .407±.021 Nm/kg, 1.18 ±.022 Nm/kg, and 1.73±.071 Nm/kg via analytical methods at the lower back, hip, knee, and ankle, respectively.</p><p><strong>Conclusion: </strong>These results are comparable to other state-of-the-art wearable sensing systems, establishing deep learning as a viable sensing approach that generalizes to new users and tasks.</p><p><strong>Significance: </strong>This work shows promise for enabling accurate real-world biomechanical data collection and enhancement of biofeedback systems and wearable robot control.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144649398","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":"Bayesian Temporal Prediction: A Robust Algorithm for Real-time EEG Phase-dependent Brain Stimulation.","authors":"Sina Shirinpour, Ivan Alekseichuk, Malte R Guth, Zachary Haigh, Miles Wischnewski, Alexander Opitz","doi":"10.1109/TBME.2025.3589970","DOIUrl":"https://doi.org/10.1109/TBME.2025.3589970","url":null,"abstract":"<p><strong>Objective: </strong>Real-time estimation of brain state is essential for efficient brain stimulation. Specifically, the electroencephalography (EEG) oscillation phase arose as a promising biomarker for instantaneous brain excitability, making it ideal for state-dependent brain stimulation. Current methods for real-time EEG phase extraction lose accuracy in the presence of non-stationary noise, motivating the development of a more robust and accurate algorithm. Here, we propose and validate Bayesian Temporal Prediction (BTP) as an effective method for EEG phase detection in real-time.</p><p><strong>Methods: </strong>BTP utilizes a short pre-session EEG recording and learning of the personalized prediction parameters, enabling subsequent high-precision real-time phase detection. We experimentally validate BTP in humans and compare its performance to a strong benchmark algorithm.</p><p><strong>Results: </strong>BTP demonstrates accurate EEG oscillation phase detection across a broad range of conditions and target oscillations, facilitating personalized brain stimulation.</p><p><strong>Conclusion: </strong>This study introduces BTP as a robust, computationally efficient, and accurate method for EEG state-dependent stimulation.</p><p><strong>Significance: </strong>The widespread adoption of BTP in research and clinical settings has the potential to enhance treatment efficacy and minimize inter- and intra-individual variability in brain stimulation interventions.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144649396","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":"Dynamic Evaluation of Colitis-Associated Colorectal Cancer Using Multimodal US-OCT-NIRF System.","authors":"Ruiming Kong, Cuixia Dai, Bing Wang, Zhuoquan Chen, Zonghai Sheng, Hairong Zheng, Teng Ma","doi":"10.1109/TBME.2025.3589599","DOIUrl":"https://doi.org/10.1109/TBME.2025.3589599","url":null,"abstract":"<p><strong>Objective: </strong>Colitis-associated colorectal cancer (CAC) poses a significant clinical challenge due to its poor prognosis and difficulty in distinguishing malignancy from inflammation with current imaging methods. This study aims to evaluate a multimodal endoscopic imaging system combining optical coherence tomography (OCT), ultrasonography (US), and near-infrared fluorescence (NIRF) to improve the detection of CAC.</p><p><strong>Methods: </strong>The proposed imaging system integrates OCT, US, with NIRF imaging enhanced through the administration of BSA-ICG nanocomplexes. The NIRF component enables visualization of capillary networks within the intestinal wall, while OCT and US assess morphological changes during CAC progression. The system applied in a CAC mice model to monitor both vascular and structural changes from inflammation to tumor formation.</p><p><strong>Results: </strong>The system successfully detected early morphological and vascular changes associated with CAC, including alterations in capillary networks, tissue thickening, and tumor formation. Fluorescence imaging provided high-resolution visualization of the smallest capillaries in the colon, while OCT and US offered valuable insights into the progression from inflammation to malignancy.</p><p><strong>Conclusion: </strong>The system shows strong potential for early and accurate detection of CAC by simultaneously visualizing vascular and morphological changes in vivo. This approach enables dynamic monitoring of disease progression and offers valuable insights into the inflammatory mechanisms underlying carcinogenesis.</p><p><strong>Significance: </strong>This study presents an imaging technique that could improve early diagnosis of CAC, ultimately leading to better clinical outcomes. By enhancing our ability to detect tumor-related changes at an early stage, this multimodal system may help guide therapeutic interventions and improve patient management.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144649397","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}