IEEE Transactions on Biomedical Engineering最新文献

{"title":"SemSTNet: Medical EEG Semantic Metric Learning with Class Prototypes Generated by Pretrained Language Model.","authors":"Quanlin Chen, Chunjin Ye, Rui Xiao, Jiahui Pan, Jingcong Li","doi":"10.1109/TBME.2025.3620754","DOIUrl":"https://doi.org/10.1109/TBME.2025.3620754","url":null,"abstract":"<p><p>Electroencephalography (EEG) feature learning is crucial for brain-machine interfaces and medical diagnostics. Existing deep learning models for classification often overlook the intrinsic semantic relationships between different EEG classes and rely on overly complex models with a large number of parameters. To address these challenges, we propose SemSTNet, a novel and lightweight framework for EEG analysis. Firstly, we designed an e ficient, lightweight convolutional architecture that decouples spatial and temporal feature extraction. Then we propose a framework which introduces a novel semantic metric learning paradigm that uses class prototypes generated by a pretrained language model to better capture inter-class relationships and enhance intra-class compactness. These prototypes are extracted and stored offline, requiring no additional inference from the language model during training or deployment. This design significantly reduces model complexity, resulting in a model with only 23K parameters-over 100 times fewer than common Transformer-based models. Exten sive experiments demonstrate that SemSTNet outperforms state of-the-art approaches on tasks such as epilepsy classification and sleep staging, highlighting its effectiveness and efficiency. Our work demonstrates that integrating semantic knowledge with a purpose-built lightweight architecture provides a highly effective and efficient solution.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145286025","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":"Automated muscle path calibration with gradient-specified optimization based on moment arm.","authors":"Ziyu Chen, Tingli Hu, Sami Haddadin, David W Franklin","doi":"10.1109/TBME.2025.3620626","DOIUrl":"https://doi.org/10.1109/TBME.2025.3620626","url":null,"abstract":"<p><strong>Objective: </strong>Muscle path modeling is more than just routing a cable that visually represents the muscle, but rather it defines how moment arms vary with different joint configurations. The muscle moment arm is the factor that translates muscle force into joint moment, and this property has an impact on the accuracy of musculoskeletal simulations. However, it is not easy to calibrate muscle paths based on a desired moment arm, because each path is configured by various parameters while the relations between moment arm and both the parameters and joint configuration are complicated.</p><p><strong>Methods: </strong>We tackle this challenge in the simple fashion of optimization, but with an emphasis on the gradient; when specified in its analytical form, optimization speed and accuracy are improved.</p><p><strong>Results: </strong>We explain in detail how to differentiate the enormous cost function and how our optimization is configured, then we demonstrate the performance of this method by fast and accurate replication of muscle paths from a state-of-the-art shoulder-arm model.</p><p><strong>Conclusion and significance: </strong>As long as the muscle is represented as a cable wrapping around obstacles, our method overcomes difficulties in path calibration, both for developing generic models and for customizing subject-specific models. This allows efficient enhancement of simulation accuracy for applications such as rehabilitation planning, surgical outcome prediction, and athletic performance analysis.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145286070","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":"Effect of Performance Feedback Timing on Motor Learning for a Surgical Training Task.","authors":"Mary Kate Gale, Kailana Baker-Matsuoka, Ilana Nisky, Allison M Okamura","doi":"10.1109/TBME.2025.3621106","DOIUrl":"https://doi.org/10.1109/TBME.2025.3621106","url":null,"abstract":"<p><strong>Objective: </strong>Robot-assisted minimally invasive surgery (RMIS) has become the gold standard for a variety of surgical procedures, but the optimal method of training surgeons for RMIS is unknown. We hypothesized that real-time, rather than post-task, error feedback would better increase learning speed and reduce errors.</p><p><strong>Methods: </strong>Forty-two surgical novices learned a virtual version of the ring-on-wire task, a canonical task in RMIS training. We investigated the impact of feedback timing with multi-sensory (haptic and visual) cues in three groups: (1) real-time error feedback, (2) trial replay with error feedback, and (3) no error feedback.</p><p><strong>Results: </strong>Participant performance was evaluated based on the accuracy of ring position and orientation during the task. Participants who received real-time feedback outperformed other groups in ring orientation. Additionally, participants who received feedback in replay outperformed participants who did not receive any error feedback on ring orientation during long, straight path sections. There were no significant differences between groups for ring position overall, but participants who received real-time feedback outperformed the other groups in positional accuracy on tightly curved path sections.</p><p><strong>Conclusion: </strong>The addition of real-time haptic and visual error feedback improves learning outcomes in a virtual surgical task over error feedback in replay or no error feedback at all.</p><p><strong>Significance: </strong>This work demonstrates that multi-sensory error feedback delivered in real time leads to better training outcomes as compared to the same feedback delivered after task completion. This novel method of training may enable surgical trainees to develop skills with greater speed and accuracy.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145286080","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-speed Intra-body Communication System Through Fat Tissue Using Wearable Antennas for Health Monitoring.","authors":"Tarakeswar Shaw, Bappaditya Mandal, Johan Engstrand, Roger L Karlsson, Thiemo Voigt, Robin Augustine","doi":"10.1109/TBME.2025.3621087","DOIUrl":"https://doi.org/10.1109/TBME.2025.3621087","url":null,"abstract":"<p><p>In this article, the design of a non-invasive wearable antenna-based fat intra-body communication (Fat-IBC) system is presented for biomedical applications. The Fat-IBC system is used for uninterrupted communication between various wearable, implanted, and semi-implanted devices, facilitating the exchange of data and information within body area networks (BAN). Herein, to eliminate the design complexity, a simple planar-loop antenna is considered to establish the Fat-IBC link. For the numerical analysis, a three-layer human body tissue model (skin, fat, and muscle) is considered to optimize the antenna. A polydimethylsiloxane (PDMS) coating layer is deposited around the wearable antenna to eliminate direct contact with the human body. In addition, the antenna has also been shielded by a ferrite substrate and copper tape to reduce the loss of energy in undesired directions and stop the surface wave propagation over the skin tissue. The Fat-IBC system is constructed by using two identical wearable antennas that act as transmitting (Tx) and receiving (Rx) elements. These antennas have been placed on the three-layer human body tissue models at different distances to demonstrate the data transmission. The concept of the proposed wearable antenna-based Fat-IBC system has been established by numerical simulations and validated by experimental studies using phantoms. The proposed data transmission link was characterized using scattering parameters and the IEEE 802.11n wireless communication standard with combinations of on-skin wearable antennas. To achieve high in-body data rate using on-body antennas through the fat layer, a wireless LAN at the 2.4 GHz band was tested using low-cost Raspberry Pi single-board computers. The phantoms are utilized for measurement purposes to emulate the human body. For the proposed Fat-IBC system, a maximum link speed of 93 Mb/s is achieved using the 40 MHz bandwidth provided by the IEEE 802.11n standard at the frequency of 2.4 GHz. The obtained results demonstrate that the proposed Fat-IBC system, utilizing low-cost off-the-shelf hardware and established IEEE 802.11 wireless communication, can achieve high-speed data communication through three-layer phantom tissue.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145286028","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":"Self-Supervised Denoising with Noise Propagation Model: Improving Material Decomposition in Photon-Counting CT.","authors":"Qianyu Wu, Xu Ji, Xiaoxue Lei, Xiaopeng Yu, Mengqing Su, Wenhui Qin, Yikun Zhang, Wenying Wang, Yanyan Liu, Guotao Quan, Gouenou Coatrieux, Jean-Louis Coatrieux, Xiaochun Lai, Yang Chen","doi":"10.1109/TBME.2025.3620135","DOIUrl":"https://doi.org/10.1109/TBME.2025.3620135","url":null,"abstract":"<p><p>The inherent spectral properties of photon-counting computed tomography (PCCT) allow detailed material identification through decomposition techniques, but these methods often amplify image noise and artifacts. Current denoising approaches mainly focus on improving already degraded images, ignoring the fundamental noise caused by random variations in photon detection. To tackle these issues, we combine a physics-based noise analysis with deep learning to control noise during the material decomposition process. Our work has three key parts: (1) A noise analysis model that explains how random photon-count variations in the detector affect the noise levels in different materials after decomposition. This model connects the Poisson-distributed detector noise to material-specific noise patterns. (2) A self-supervised training method that combines the noise model with neural networks using probability-based optimization, allowing the system to learn from limited training data without needing high-quality data. (3) A flexible image improvement system that adapts to different body structures and noise conditions, ensuring reliable results across various scanning scenarios. Tests using real patient scan data show our method better preserves material accuracy and produces cleaner virtual monochromatic images compared to traditional approaches. Importantly, our solution works effectively with small training datasets and can be practically used in hospital settings without slowing down workflows. This research bridges the gap between theoretical noise analysis and clinical medical imaging needs, offering a balanced approach to improving PCCT technology.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145274415","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":"An electromagnetic navigation surgical robotic system (ENSRS) for transthoracic puncture of small pulmonary nodules.","authors":"Chunxia Qin, Huajie Zhang, Lei Tang, Qikang Hu, Xiaofeng Chen, Huali Hu, Fenglei Yu, Muyun Peng","doi":"10.1109/TBME.2025.3619056","DOIUrl":"https://doi.org/10.1109/TBME.2025.3619056","url":null,"abstract":"<p><strong>Objective: </strong>To address the limitations of traditional CT-guided pulmonary nodule interventions, such as excessive radiation exposure, prolonged procedure times, and limited precision, we developed an electromagnetic navigation surgical robotic system (ENSRS) to enhance accuracy, efficiency, and safety in percutaneous procedures.</p><p><strong>Methods: </strong>The ENSRS integrates artificial intelligence to automate the segmentation of pulmonary nodules and surrounding anatomical structures, generating a detailed surgical environment. A customized path-planning algorithm facilitates minimally invasive access, whereas submillimeter localization using fiducial markers ensures precise coordinate registration. Adaptive multicalibration strategies and robust safety protocols enhance procedural reliability. System performance was evaluated through phantom and animal experiments, with comparisons to traditional CTguided techniques.</p><p><strong>Results: </strong>The ENSRS achieved a groove localization error of 0.51 ± 0.27 mm across 63 patches and a classification accuracy of 100%. In phantom studies, it demonstrated significantly reduced puncture error (0.81 ± 0.98 mm vs. 3.50 ± 2.88 mm, p < 0.0001), required fewer CT scans (1.02 ± 0.25 vs. 1.53 ± 0.92) and shortened puncture times (39.01 ± 29.71 s). In animal experiments, ENSRS achieved improved accuracy (0.33 ± 0.74 mm vs. 1.86 ± 0.99 mm, p = 0.015). The safety outcomes were comparable between the groups, with one pneumothorax reported each.</p><p><strong>Conclusion: </strong>ENSRS improves the precision, efficiency, and safety of pulmonary nodule interventions, outperforming traditional CT-guided methods in phantom and animal models.</p><p><strong>Significance: </strong>This system offers a promising approach to pulmonary interventions by combining robotic precision with intelligent planning and tracking, potentially enhancing outcomes in minimally invasive procedures.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145244511","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":"Tunable Metasurfaces for Switchable Magnetic Field Enhancement Regions in 1.5 T MRI.","authors":"Guoquan Chen, Xia Xiao, Yu Liu, Xiangzheng Kong, Jiannan Zhou","doi":"10.1109/TBME.2025.3618475","DOIUrl":"https://doi.org/10.1109/TBME.2025.3618475","url":null,"abstract":"<p><p>Metasurfaces have been reported to boost the signal-to-noise ratio (SNR) of magnetic resonance imaging (MRI) through their magnetic field enhancement capabilities. The varying region-of-interest (ROI) sizes in clinical imaging limit metasurfaces from realizing their potential, since the field enhancement regions of the most metasurfaces are fixed after fabrication. In this paper, a tunable metasurface (TMS) is proposed to significantly boost SNR of 1.5 T MRI in regions with different sizes. The TMS is composed of unit cells that contain a planar metal spiral loaded with a variable capacitor and placed on a square dielectric substrate. The feasibility of switching between a wide enhancement region (WER) mode and a narrow enhancement region (NER) mode by adjusting the capacitance is validated through simulations and experiments. The enhancement of the magnetic field by the WER mode of TMS boosts the SNR of the human brain voxel model by a maximum of 14.01 times, reaching a higher value of 22.81 times with the NER mode. This work offers an effective approach that can change the region sizes of field enhancement flexibly, enabling metasurfaces to adapt for various MRI scenarios such as disease detection in the large region or therapeutic monitoring in the specific small region.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145244483","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 Inductance Plethysmography to Quantify Changes in Ventilation in Obstructive Sleep Apnea.","authors":"Eysteinn Finnsson, Eydis Arnardottir, Kristofer Montazeri, Brendan T Keenan, Richard J Schwab, Thorarinn Gislason, Allan I Pack, Andrew Wellman, Anna S Islind, Jon S Agustsson, Scott A Sands","doi":"10.1109/TBME.2025.3618403","DOIUrl":"https://doi.org/10.1109/TBME.2025.3618403","url":null,"abstract":"<p><strong>Background and objective: </strong>The study aims to determine whether respiratory inductance plethysmography (RIP) signals can be used to quantify changes in ventilation and provide advanced obstructive sleep apnea (OSA) severity metrics. This approach seeks to address limitations in current airflow-based OSA measures, particularly those relying on nasal pressure, which may be compromised by oral breathing.</p><p><strong>Methods: </strong>Adult patients with OSA (N = 89, 68Male:21Female) completed in-laboratory polysomnography (PSG) allowing for RIP-based ventilation estimates to be compared against a gold standard oronasal-pneumotach (normalized ventilation %eupnea). Concordance was assessed on three levels: 1) individual breath ventilation, 2) individual respiratory event depth (percentage reduction in ventilation from local average), and 3) patient-specific OSA severity in terms of average event depth and ventilatory burden (average event depth x average event duration x event rate). To address overestimation of RIP ventilation during obstruction, we developed and applied a calibration and linearization method (\"RIP correction\"). Concordance analysis evaluated median bias for both small (130%eupnea), along with bias and intraclass correlation coefficient (ICC) calculation for events and patient-specific measures.</p><p><strong>Results: </strong>For individual breaths (N = 495,631), RIP correction reduced overestimation bias for small breaths from 12 to 2%eupnea. For individual events (N = 34,497), RIP correction reduced mean bias for event depth estimates from 9 to 1%eupnea. For patient-specific analysis underestimation of average event depth was attenuated from 9 to 4%eupnea and for ventilatory burden, from 275 to 116%eupnea min/hr. Additionally, RIP correction improved ICC for event depth and patient-level traits.</p><p><strong>Conclusion: </strong>RIP signals, with appropriate processing, enable quantification of advanced ventilation-based OSA metrics, addressing concerns that airflow-based measures may be affected by breathing route.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145244544","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":"External Validation Complexities: A Comparative Study of Late-onset Sepsis Prediction Models Across Multiple Clinical Environments.","authors":"Zheng Peng, Janno S Schouten, Demi Silvertand, Xi Long, Douglas E Lake, H Rob Taal, Hendrik J Niemarkt, Peter Andriessen, Brynne Sullivan, Carola van Pul","doi":"10.1109/TBME.2025.3618080","DOIUrl":"https://doi.org/10.1109/TBME.2025.3618080","url":null,"abstract":"<p><strong>Objective: </strong>Neonatal late-onset sepsis (LOS) is a life-threatening condition in preterm infants in neonatal intensive care units (NICUs), with early detection being crucial for improving outcomes. Despite advancements in data-driven prediction models, their generalizability remains uncertain due to a lack of independent validation, particularly on national and international scales. This study evaluates the performance of two LOS prediction models on multiple validation datasets to assess their reliability for clinical implementation.</p><p><strong>Methods: </strong>Two models were validated: (1) a multi-channel feature-based extreme gradient boosting model (MC-XGB) and (2) a deep neural network using raw RR intervals (RR-DNN). Validation was conducted on three NICU datasets: an internal dataset (68 LOS, 100 controls) from the model-development hospital in the Netherlands, a national external dataset (20 LOS, 20 controls) from another Dutch hospital, and an international external dataset (17 LOS, 17 controls) from a U.S. hospital. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) across multiple prediction time windows, with an hourly risk analysis.</p><p><strong>Results: </strong>Both models achieved a peak AUC of 0.82 in the internal dataset, their predictive performance demonstrates variable declines in external datasets. The respective AUCs for RR-DNN and MC-XGB were 0.80 and 0.72 in the national dataset, and 0.69 and 0.60 in the international dataset. This may result from variations in clinical practices, patient demographics, and monitoring technologies.</p><p><strong>Conclusion: </strong>Model performance declined in external validations, highlighting the challenges of implementing predictive models across diverse clinical settings.</p><p><strong>Significance: </strong>This study emphasizes the need for standardized guidelines and improved data sharing to enhance model development and facilitate reliable integration into NICU workflow for improved LOS management.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145238465","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 3D Numerical Model of Ultrasonic Transthoracic Propagation for Cardiac Focused Ultrasound Therapy.","authors":"Clara Magnier, Wojciech Kwiecinski, Daniel Suarez Escudero, Gauthier Amis, Guillaume Goudot, Elie Mousseaux, Emmanuel Messas, Mathieu Pernot","doi":"10.1109/TBME.2025.3618103","DOIUrl":"https://doi.org/10.1109/TBME.2025.3618103","url":null,"abstract":"<p><strong>Objective: </strong>Non-invasive focused ultrasound therapies of abdominal organs, including the heart and the liver, have emerged in the last decades. Transthoracic focusing of ultrasound poses challenges such as pressure loss and aberrations. Numerical models of ultrasonic propagation have been developed to study the focalization in heterogeneous tissues, particularly for transcranial applications. However, ribcage models were less studied than skull models, and no experimental validation of ribcage models has been performed so far.</p><p><strong>Methods: </strong>Both linear and nonlinear k-space simulations were used to model the ultrasonic propagation from a clinical system dedicated to transthoracic cardiac therapy. Tissue acoustic properties were determined from computed tomography scans. Experimental model validation was performed with hydrophone measurements of pressure fields through in vitro human ribs and in vitro porcine flail chest.</p><p><strong>Results: </strong>An excellent agreement of pressure distribution between the acquired and simulated pressure fields was found for the linear propagation model with a mean correlation coefficient between the measured and simulated pressure fields of R2 = 0.89±0.07. For the nonlinear propagation, the mean correlation coefficient was R2 = 0.91±0.06. The feasibility of the simulations through the human thorax was demonstrated on 9 patients who underwent non-invasive therapy of the aortic valve. The global attenuation estimated numerically was correlated withthe amplitude at the focus necessary to nucleate cavitation (R2 = 0.64).</p><p><strong>Conclusion: </strong>The numerical model of transthoracic ultrasound propagation was validated and used on a human patient's thorax.</p><p><strong>Significance: </strong>With further development, this model could be used as a treatment planning tool for non-invasive ultrasonic cardiac therapy.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145238517","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}