IEEE Transactions on Biomedical Engineering最新文献_第10页

A Human-Prosthesis Coupled Musculoskeletal Model for Transtibial Amputees. 经胫骨截肢者的人体-假肢耦合肌肉骨骼模型。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-01-20 DOI: 10.1109/TBME.2025.3531408

Yuwen Lu, Yan Huang, Rui Yang, Yong Wang, Yosuke Ikegami, Yoshihiko Nakamura, Qining Wang

{"title":"A Human-Prosthesis Coupled Musculoskeletal Model for Transtibial Amputees.","authors":"Yuwen Lu, Yan Huang, Rui Yang, Yong Wang, Yosuke Ikegami, Yoshihiko Nakamura, Qining Wang","doi":"10.1109/TBME.2025.3531408","DOIUrl":"https://doi.org/10.1109/TBME.2025.3531408","url":null,"abstract":"In this paper, we present a human-prosthesis coupled full-body musculoskeletal model that integrates the dynamics of the muscle-driven human body and a motor-driven robotic prosthesis. This model can be used to perform the inverse kinematics and dynamics calculation based on measurements for amputees wearing a force-controlled or position-controlled prosthesis. As a result, we can analyze the impacts of prostheses on amputee kinetic states, such as joint torques and muscle forces. To verify the proposed model, we conducted experiments involving four transtibial amputees wearing passive prostheses and our self-developed robotic prostheses. We estimated the joint angles, joint torques, and muscle forces on the intact side and on the residual side of the subjects. The indexes reflecting the symmetry and magnitude of muscle forces were introduced to evaluate the effects of different prostheses on transtibial amputees. The indexes of muscle force magnitude indicate that the posterior thigh muscles of the residual limb exhibit significant compensation during walking. And the indexes of muscle force symmetry indicate that active prostheses with higher damping rates work better for fast walking speeds, while those with lower damping rates are more suitable for slow walking speeds. The proposed approach may offer a novel method for evaluating prostheses that considers muscle-level kinetics, thus enhancing understanding of the impact of different prostheses on the movements of amputees.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541772","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}

引用次数: 0

Marker Data Enhancement for Markerless Motion Capture.

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-01-17 DOI: 10.1109/TBME.2025.3530848

Antoine Falisse, Scott D Uhlrich, Akshay S Chaudhari, Jennifer L Hicks, Scott L Delp

{"title":"Marker Data Enhancement for Markerless Motion Capture.","authors":"Antoine Falisse, Scott D Uhlrich, Akshay S Chaudhari, Jennifer L Hicks, Scott L Delp","doi":"10.1109/TBME.2025.3530848","DOIUrl":"https://doi.org/10.1109/TBME.2025.3530848","url":null,"abstract":"Objective: Human pose estimation models can measure movement from videos at a large scale and low cost; however, open-source pose estimation models typically detect only sparse keypoints, which leads to inaccurate joint kinematics. OpenCap, a freely available service for researchers to measure movement from videos, mitigates this issue using a deep learning model-the marker enhancer-that transforms sparse video keypoints into dense anatomical markers. However, OpenCap performs poorly on movements not included in the training data. Here, we create a much larger and more diverse training dataset and develop a more accurate and generalizable marker enhancer.Methods: We compiled marker-based motion capture data from 1176 subjects and synthesized 1433 hours of video keypoints and anatomical markers to train the marker enhancer. We evaluated its accuracy in computing kinematics using both benchmark movement videos and synthetic data representing unseen, diverse movements.Results: The marker enhancer improved kinematic accuracy on benchmark movements (mean error: 4.1, max: 8.7) compared to using video keypoints (mean: 9.6, max: 43.1) and OpenCap's original enhancer (mean: 5.3, max: 11.5). It also better generalized to unseen, diverse movements (mean: 4.1, max: 6.7) than OpenCap's original enhancer (mean: 40.4, max: 252.0).Conclusion: Our marker enhancer demonstrates both improved accuracy and generalizability across diverse movements.Significance: We integrated the marker enhancer into OpenCap, thereby offering its thousands of users more accurate measurements across a broader range of movements.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541760","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}

引用次数: 0

Motion-Compensated Interpolation in Echocardiography: A Lie Advection-Based Approach 超声心动图中的运动补偿插值：一种基于平流的方法

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-01-16 DOI: 10.1109/TBME.2024.3440838

Hani Nozari Mirar;Sten Roar Snare;Anne H. Schistad Solberg

{"title":"Motion-Compensated Interpolation in Echocardiography: A Lie Advection-Based Approach","authors":"Hani Nozari Mirar;Sten Roar Snare;Anne H. Schistad Solberg","doi":"10.1109/TBME.2024.3440838","DOIUrl":"https://doi.org/10.1109/TBME.2024.3440838","url":null,"abstract":"To better understand cardiac structures and dynamics via echocardiography, it is essential to have cardiac image sequences with sufficient spatio-temporal resolution. However, in echocardiography, there is an inherent tradeoff between temporal and spatial resolution, which limits the ability to acquire images with both high temporal and spatial resolution simultaneously. Motion-compensated interpolation, a post-acquisition technique, enhances the temporal resolution without compromising the spatial resolution. This paper introduces a novel motion-compensated interpolation algorithm based on the advection equation in fluid mechanics. Considering the incompressibility of cardiac tissue, we derive a solution in terms of Lie series for the advection problem. Subsequently, we construct a bidirectional advection energy model to estimate the optimal velocity fields that can simultaneously advect two cardiac images towards each other. The process continues until they converge at a midpoint where the image similarity peaks. To preserve the topology of the cardiac structures and ensure that image deformations are diffeomorphic, the advection process is carried out gradually with a smooth velocity field. To reduce the contribution of the blood signal in optimizing for the best tissue advection velocity, a nonlocal regularization pre-processing is applied to echocardiography data. Our algorithm, tested on 2D and 3D echocardiography, outperforms existing motion-compensated interpolation algorithms in estimating cardiac motions. It preserves cardiac topology during image deformations and reduces interpolation artifacts, especially in low frame rate recordings. By training a neural network on the data generated by our algorithm, we achieved over 75 times faster computation without compromising image quality.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 1","pages":"123-136"},"PeriodicalIF":4.4,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993236","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}

引用次数: 0

Personalized Model Identification for Glucose Dynamics from Clinical Data with Incomplete Inputs.

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-01-16 DOI: 10.1109/TBME.2025.3530711

Basak Ozaslan, Eleonora M Aiello, Emilia Fushimi, Francis J Doyle, Eyal Dassau

{"title":"Personalized Model Identification for Glucose Dynamics from Clinical Data with Incomplete Inputs.","authors":"Basak Ozaslan, Eleonora M Aiello, Emilia Fushimi, Francis J Doyle, Eyal Dassau","doi":"10.1109/TBME.2025.3530711","DOIUrl":"https://doi.org/10.1109/TBME.2025.3530711","url":null,"abstract":"Objective: A common challenge in model identification with clinical data is incomplete and sometimes imprecise information. In this work, we provide a method to reconstruct the corrupted input data in a clinical dataset and, jointly identify the person-specific parameters of a metabolic model describing meal-insulin-glucose-dynamics for people with type 1 diabetes (T1D).Method: The proposed method is an algorithm that iterates between nonlinear least-squares and mixed-integer quadratic programming to optimize model parameters in conjunction with sparse corrections to the input data. In order to handle long stretches of data, the optimization problem is designed to be i) computationally tractable, and ii) robust against the potential presence of significant inaccuracies corrupting a data portion. Moreover, since the pattern of the inaccuracies is specific to each person, we propose a personalized hyperparameter tuning approach. The method is applied on clinical data from 13 people with T1D. Identified model performance is compared to the performance of model identified with standard least squares (LS) method.Results: Compared to LS, identifying corrections in conjunction with model parameters on training data lead to an improvement in the model prediction capabilities on unseen data with an average 2.2% improvement in MARD for two-hour prediction horizon (p-value = 0.0006).Conclusions: The proposed method is effective in model identification for clinical data with unknown inaccuracies in the inputs.Significance: Personalized models with high accuracy can inform treatment decisions and lead to better glucose control outcomes in people with T1D.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541055","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}

引用次数: 0

Dual-Uncertainty Guided Multimodal MRI-Based Visual Pathway Extraction.

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-01-15 DOI: 10.1109/TBME.2025.3529870

Alou Diakite, Cheng Li, Yousuf Babiker M Osman, Zan Chen, Yiang Pan, Jiawei Zhang, Tao Tan, Hairong Zheng, Shanshan Wang

{"title":"Dual-Uncertainty Guided Multimodal MRI-Based Visual Pathway Extraction.","authors":"Alou Diakite, Cheng Li, Yousuf Babiker M Osman, Zan Chen, Yiang Pan, Jiawei Zhang, Tao Tan, Hairong Zheng, Shanshan Wang","doi":"10.1109/TBME.2025.3529870","DOIUrl":"https://doi.org/10.1109/TBME.2025.3529870","url":null,"abstract":"Objective: This study aims to accurately extract the visual pathway (VP) from multimodal MR images while minimizing reliance on extensive labeled data and enhancing extraction performance.Method: We propose a novel approach that incorporates a Modality-Relevant Feature Extraction Module (MRFEM) to effectively extract essential features from T1-weighted and fractional anisotropy (FA) images. Additionally, we implement a mean-teacher model integrated with dual uncertainty-aware ambiguity identification (DUAI) to enhance the reliability of the VP extraction process.Results: Experiments conducted on the Human Connectome Project (HCP) and Multi-Shell Diffusion MRI (MDM) datasets demonstrate that our method reduces annotation efforts by at least one-third compared to fully supervised techniques while achieving superior extraction performance over six state-of-the-art semi-supervised methods.Conclusion: The proposed label-efficient approach alleviates the burdens of manual annotation and enhances the accuracy of multimodal MRI-based VP extraction.Significance: This work contributes to the field of medical imaging by facilitating more efficient and accurate visual pathway extraction, thereby improving the analysis and understanding of complex brain structures with reduced reliance on expert annotation.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541645","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}

引用次数: 0

IEEE Engineering in Medicine and Biology Society Information IEEE医学与生物工程学会信息

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-01-15 DOI: 10.1109/TBME.2024.3503455

引用次数: 0

IEEE Transactions on Biomedical Engineering Handling Editors Information IEEE生物医学工程学报编辑信息处理

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-01-15 DOI: 10.1109/TBME.2024.3503459

引用次数: 0

IEEE Transactions on Biomedical Engineering Information for Authors IEEE生物医学工程信息汇刊作者

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-01-15 DOI: 10.1109/TBME.2024.3503457

引用次数: 0

Synchronous Rotation-based Knot Tying on Mini-Incisions Using Dual-Arm Nanorobot. 利用双臂纳米机器人在微型切口上同步旋转打结

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-01-15 DOI: 10.1109/TBME.2025.3528465

Yujie Jiang, Chengxi Zhong, Chao Qin, Zhenhuan Sun, Song Liu

{"title":"Synchronous Rotation-based Knot Tying on Mini-Incisions Using Dual-Arm Nanorobot.","authors":"Yujie Jiang, Chengxi Zhong, Chao Qin, Zhenhuan Sun, Song Liu","doi":"10.1109/TBME.2025.3528465","DOIUrl":"https://doi.org/10.1109/TBME.2025.3528465","url":null,"abstract":"Knot tying is a critical task in robotic surgery, which is considerably important for surgical success and postoperative recovery. Despite of the well-established protocols and significant progress using medical robots at macro scale, the need for automatedly tying mechanically robust knots on mini-incisions remains largely unmet, particularly with relieved suture deformation, avoided suture slippage, reduced workspace consumption, and enhanced precision and biomechanical compatibility. Here, we propose an innovative dual-arm nanorobotic system featured by stereo microscope and additional rotation degree of freedom (DOF) mounted on each arm, enabling automated, precise, and controllable knot tying on mini-incisions. With this system, a synchronous rotation-based knot tying (SRKT) trajectory is designed to mitigate undesired suture deformation, suture slippage, and minimize workspace consumption. The theoretical and simulation analysis of the topology and tension force distribution on suture validated the efficacy of SRKT trajectory. The experiments demonstrated that the dual-arm nanorobotic system with SRKT trajectory can be used for tying various knot types, using different micro-sutures, conducting on mini-incisions of different tissues, especially keeping suture slippage avoidance, minimal workspace, and robust mechanical strength of the tied knots. The proposed dual-arm nanorobotic system and SRKT trajectory render a significant potential for various practical medical applications.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541647","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}

引用次数: 0

Cutting Skill Assessment by Motion Analysis Using Deep Learning and Spatial Marker Tracking.

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-01-15 DOI: 10.1109/TBME.2025.3529500

Bai-Quan Su, Xu-Dong Ma, Weihan Li, Zi-Ao Kuang, Yi Gong, Gang Wang, Qingqian Zhang, Wenyong Liu, Changsheng Li, Li Gao, Junchen Wang

{"title":"Cutting Skill Assessment by Motion Analysis Using Deep Learning and Spatial Marker Tracking.","authors":"Bai-Quan Su, Xu-Dong Ma, Weihan Li, Zi-Ao Kuang, Yi Gong, Gang Wang, Qingqian Zhang, Wenyong Liu, Changsheng Li, Li Gao, Junchen Wang","doi":"10.1109/TBME.2025.3529500","DOIUrl":"https://doi.org/10.1109/TBME.2025.3529500","url":null,"abstract":"The assessment of surgical skill is crucial for indicating a surgeon's proficiency. While motion analysis of surgical tools is widely used in endoscopic surgery, it is not commonly applied to open surgery. Instead, open surgery skill assessment relies on observing the trajectory of surgical tools on tissue. This observation-based method often lacks clear standards, leading to inaccurate assessments. This paper presents a method for evaluating cutting skill in open surgery through scalpel motion analysis. A 3D multiple-facet ArUco code cube is designed, and a dataset of tip coordinate system poses for various scalpels in the ArUco code coordinate system (ACS) is established using the pivot calibration method. The YOLOv8 model and an image dataset of different scalpels are used to identify the scalpel type and select its tip position. The tip position is then transformed from ACS to a binocular camera coordinate system (BCS), representing the incision curve made by the scalpel. Five assessment metrics are proposed to quantify the surgeon's cutting skill: average incision curvature deviation, incision length difference, incision endpoint deviation, average incision deviation, and average cutting jerk. Experiments involving twenty expert and novice surgeons performing four common incisions (straight line, polyline, semicircle, and cross line) demonstrate the metrics' effectiveness. The metrics provide a clear, objective display of individual cutting skills, and a combined ranking reveals comparative skill levels. This study offers a precise method for evaluating surgeons' cutting skills with a scalpel in open surgery.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541714","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}

引用次数: 0