IEEE transactions on medical robotics and bionics最新文献_第3页

Prototype-Enhanced Experience Replay for Domain Incremental Sleep Stage Classification 面向领域增量睡眠阶段分类的原型增强体验回放

IF 4.3

IEEE transactions on medical robotics and bionics Pub Date : 2026-08-01 Epub Date: 2026-06-02 DOI: 10.1109/TMRB.2026.3699224

Leyuan Huang;Zhiwei Deng;Xun Chen;Chang Li

{"title":"Prototype-Enhanced Experience Replay for Domain Incremental Sleep Stage Classification","authors":"Leyuan Huang;Zhiwei Deng;Xun Chen;Chang Li","doi":"10.1109/TMRB.2026.3699224","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3699224","url":null,"abstract":"Subject variability in EEG signals induces significant domain shifts and triggers catastrophic forgetting (CF) in sleep staging. Meanwhile, in cross-subject learning, retraining from scratch for all subjects is costly, whereas sequentially fine-tuning a pre-trained model on incoming subjects typically leads to CF. This motivates a continual learning (CL) perspective. Under CL paradigm, subjects arrive sequentially and we treat arriving subjects as a new domain. To address the lack of domain incremental learning methods in sleep staging, we propose prototype-enhanced experience replay (PER), which enables the network to learn from new domains while retaining previously acquired knowledge, thereby mitigating forgetting. PER builds on experience replay: we summarize class semantics into a set of prototypes, update a global prototype memory cumulatively, and align features with their class prototypes to stabilize classification. We further incorporate knowledge distillation (KD) to support learning across domains. On Sleep-EDF and SHHS with AttnSleep, TinySleepNet and DeepSleepNet, PER consistently improves performance and retention: raising kappa by an absolute 4.3% and macro-F1 by 3.2% over replay and regularisation baselines, while reducing the average forgetting rate by a relative 71%, and approaching the joint-learning upper bound. These results indicate that lightweight global prototypes effectively stabilise cross-subject representations in continual sleep staging.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 3","pages":"1110-1121"},"PeriodicalIF":4.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148748570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Shaft-Integrated Force Sensing With Transformer-Based Dynamics Compensation for Telesurgery 基于变压器动态补偿的远程手术轴集成力传感

IF 4.3

IEEE transactions on medical robotics and bionics Pub Date : 2026-08-01 Epub Date: 2026-06-02 DOI: 10.1109/TMRB.2026.3699214

Shuyuan Yang;Grant Boone;Timo Markert;Sebastian Matich;Andreas Theissler;Martin Atzmueller;Zonghe Chua

{"title":"Shaft-Integrated Force Sensing With Transformer-Based Dynamics Compensation for Telesurgery","authors":"Shuyuan Yang;Grant Boone;Timo Markert;Sebastian Matich;Andreas Theissler;Martin Atzmueller;Zonghe Chua","doi":"10.1109/TMRB.2026.3699214","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3699214","url":null,"abstract":"Robot-Assisted Minimally Invasive Surgery (RAMIS) enhances surgeon dexterity, with newer platforms leveraging haptic feedback to further improve performance. Such force information has broader potential to inform performance assessment, tactile localization, and surgical autonomy. This motivates the need for accessible approaches to integrating force sensing into RAMIS tools. This work presents a method for integrating a six-axis commercial force sensor into the distal end of a standard cable-driven surgical instrument, enabling end-effector force measurement while preserving the original mechanical functionality of the device. The proposed design emphasizes reproducibility and accessibility for research applications, requiring no specialized manufacturing tools. A transformer neural network integrates force sensor measurements with robot state information to aid estimation of applied forces at the end-effector, compensating for internal cable forces arising from actuation. Our proposed approach achieved normalized errors below 6%, and generalized to unseen conditions better than purely proximal data-driven sensing approaches. High internal cable forces caused sensor saturation and reduced axial force observability, which can degrade performance along the tool’s major axis and under higher load conditions. Given current levels of performance, the balance of system integrability and performance enables applications and research into timely topics of haptic feedback, skill assessment, and force-informed autonomy in RAMIS. Videos and code are available at <uri>https://enhanced-telerobotics.github.io/shaft_force_sensing</uri>","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 3","pages":"970-981"},"PeriodicalIF":4.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148749244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimized Mappings From Biological Hip Moment Estimates to Exoskeleton Torque Can Personalize Assistance Across Users and Generalize Across Tasks 从生物髋关节力矩估计到外骨骼扭矩的优化映射可以跨用户个性化帮助并推广跨任务

IF 4.3

IEEE transactions on medical robotics and bionics Pub Date : 2026-08-01 Epub Date: 2026-07-13 DOI: 10.1109/TMRB.2026.3712823

Justine C. Powell;Ethan B. Schonhaut;Dean D. Molinaro;Aaron J. Young

{"title":"Optimized Mappings From Biological Hip Moment Estimates to Exoskeleton Torque Can Personalize Assistance Across Users and Generalize Across Tasks","authors":"Justine C. Powell;Ethan B. Schonhaut;Dean D. Molinaro;Aaron J. Young","doi":"10.1109/TMRB.2026.3712823","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3712823","url":null,"abstract":"Recent advancements in data-driven methods have enabled real-time estimation of biomechanical states for exoskeleton control. While biological joint moments can be directly used to scale exoskeleton assistance, this approach is often suboptimal. An optimized mapping between biological joint moments and exoskeleton assistance could enhance end-to-end controllers based on the user’s physiological state. We introduce a flexible parametrization of biological moment-based control using delay, scaling, and shaping terms to transform joint moment estimates into commanded torque. We performed human-in-the-loop optimization, using metabolic cost to evaluate each iteration’s controller parameters, for 9 subjects across three ambulation modes: level walking at 1.1 m/s, 1.5 m/s, and 5° inclined walking. We evaluated three methods of exoskeleton control: 1. Personalized/Task Dependent, 2. Theoretical Task Dependent/Non-personalized, and 3. Theoretical Multi-Task/Non-personalized. On average, our personalized approach provided the greatest benefit of 18.3% reduction in metabolic cost compared to walking without the exoskeleton, with the task-dependent and multi-task controllers producing similar reductions of 8.6% and 8.4%, respectively. Our results show that fully personalized exoskeleton control parameters can yield large metabolic reductions for cyclic ambulatory tasks when compared to a theoretical generalizable, multi-task controller, highlighting the value of personalizing exoskeleton assistance to users across ambulatory tasks.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 3","pages":"1218-1229"},"PeriodicalIF":4.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148749366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimating Gait Parameters Using a Smart Walker With Human–Robot Interaction and Machine Learning Models 基于人机交互和机器学习模型的智能步行者步态参数估计

IF 4.3

IEEE transactions on medical robotics and bionics Pub Date : 2026-08-01 Epub Date: 2026-07-16 DOI: 10.1109/TMRB.2026.3713989

Jinsai Cheng;Tao Shen

{"title":"Estimating Gait Parameters Using a Smart Walker With Human–Robot Interaction and Machine Learning Models","authors":"Jinsai Cheng;Tao Shen","doi":"10.1109/TMRB.2026.3713989","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3713989","url":null,"abstract":"The population of individuals with mobility impairments has been significantly increasing due to aging, diseases, or accidents. Gait analysis plays a crucial role in investigating musculoskeletal functions and gait parameters, enabling the assessment of mobility diseases, and providing valuable data for optimizing physical therapy treatments. Smart walkers can not only provide physical support to patients but also monitor the user’s walking gait. In this research, force sensors and built-in encoders are used to extract various gait parameters. Machine learning techniques, which have the ability to adapt to users with different gait patterns, are used to analyze users’ gait through interaction force. Three machine learning models, Support Vector Machine, Random Forest, and Neural Network are selected to perform gait analysis. Experiments with 5 subjects are conducted to evaluate the performance of the machine learning models in gait parameters estimation. Results from this pilot study show that the accuracies of the three machine learning models are all above 90% and the performance is much better than the traditional method and Random Forest model has slightly better accuracy among the small sample-size populations. In addition, SVM has the fastest response with 1.2 ms prediction time, which is suitable for online evaluation.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 3","pages":"1287-1298"},"PeriodicalIF":4.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148749321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toward a Virtual Gait Lab: Testing Prosthetics With Dynamically Simulated Users 迈向虚拟步态实验室：用动态模拟用户测试假肢

IF 4.3

IEEE transactions on medical robotics and bionics Pub Date : 2026-08-01 Epub Date: 2026-07-13 DOI: 10.1109/TMRB.2026.3712826

Balint K. Hodossy;Matteo Crotti;Anna Pace;Manuel G. Catalano;Oskar C. Aszmann;Antonio Bicchi;Dario Farina

{"title":"Toward a Virtual Gait Lab: Testing Prosthetics With Dynamically Simulated Users","authors":"Balint K. Hodossy;Matteo Crotti;Anna Pace;Manuel G. Catalano;Oskar C. Aszmann;Antonio Bicchi;Dario Farina","doi":"10.1109/TMRB.2026.3712826","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3712826","url":null,"abstract":"Walking is commonly seen as a simple and effortless activity. In reality, it arises from a complex and intricately coordinated neuromechanical system. To effectively assist or replicate this process, artificial devices must adapt seamlessly to the dynamic goals and environments of their users. Virtual environments provide an accessible and low-risk way to accelerate the early-stage development of these technologies. To capture the challenges inherent in non-steady-state locomotion, these simulations must reproduce the complexities of movements and environments. Here, we present a locomotion synthesis system that allows us to simulate non steady-state walking on uneven surfaces and obstacles with quantitative measures of kinematics, kinetics and stability. We applied this environment to trial a prototype compliant prosthetic foot, and compared the gait resulting from its use with that of a rigid foot. We contrasted our results with an experimental study with a real prosthesis user and corroborated potential benefits that compliance can provide during obstacle navigation. Our results demonstrate the application of simulated locomotion agents as subjects in a virtual gait lab. This approach could lower the participation burden on the patient population when iterating novel concepts for the design of prosthetic devices.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 3","pages":"1230-1241"},"PeriodicalIF":4.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11606461","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148749852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robotic Needle–Tissue Interaction: Real-Time Friction and Preoperative Stiffness Estimation 机器人针与组织的相互作用：实时摩擦和术前刚度估计

IF 4.3

IEEE transactions on medical robotics and bionics Pub Date : 2026-08-01 Epub Date: 2026-06-02 DOI: 10.1109/TMRB.2026.3699236

Bulbul Behera;M. Felix Orlando;R. S. Anand

{"title":"Robotic Needle–Tissue Interaction: Real-Time Friction and Preoperative Stiffness Estimation","authors":"Bulbul Behera;M. Felix Orlando;R. S. Anand","doi":"10.1109/TMRB.2026.3699236","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3699236","url":null,"abstract":"Measuring needle-tissue interaction forces has traditionally been essential for estimating the needle state and ensuring patient safety, particularly in procedures involving heterogeneous and multilayered tissues. These conditions more accurately reflect real-time clinical environments, where modeling becomes more complex. While various mechanics-based models have been proposed to describe needle-tissue interactions, capturing the dynamic nature of friction remains a significant challenge. Friction is inherently nonlinear, time-dependent, and has a profound impact on procedural accuracy. This study introduces a method for identifying the dynamic friction component during needle insertion using the Generalized Maxwell Slip (GMS) model, which captures the nonlinear and rate-dependent characteristics of friction. An Online Least Squares (OLS) algorithm is employed for real-time parameter estimation. The system’s total potential energy includes bending and tensile strain energies, along with a non-conservative virtual work term from the GMS model, resulting in a reduced-order system of coupled second-order differential equations. Supporting lemmas are developed to establish the existence and harmony of the solution under frictional loading. Additionally, the approach integrates Winkler foundation and Westergard theory to model localized tissue stiffness variations.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 3","pages":"994-1004"},"PeriodicalIF":4.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148747002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comprehensive General Model of Tendon-Actuated Concentric Tube Robots With Multiple Tubes and Tendons 具有多管多筋的肌腱驱动同心管机器人综合通用模型

IF 4.3

IEEE transactions on medical robotics and bionics Pub Date : 2026-08-01 Epub Date: 2026-06-02 DOI: 10.1109/TMRB.2026.3699204

Pejman Kheradmand;Behnam Moradkhani;Raghavasimhan Sankaranarayanan;Kent K. Yamamoto;Tanner J. Zachem;Patrick J. Codd;Nicholas Ahn;Yash Chitalia;Pierre E. Dupont

{"title":"A Comprehensive General Model of Tendon-Actuated Concentric Tube Robots With Multiple Tubes and Tendons","authors":"Pejman Kheradmand;Behnam Moradkhani;Raghavasimhan Sankaranarayanan;Kent K. Yamamoto;Tanner J. Zachem;Patrick J. Codd;Nicholas Ahn;Yash Chitalia;Pierre E. Dupont","doi":"10.1109/TMRB.2026.3699204","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3699204","url":null,"abstract":"Tendon-actuated concentric tube mechanisms combine the advantages of tendon-driven continuum robots and concentric tube robots while addressing their respective limitations. They overcome the restricted degrees of freedom often seen in tendon-driven designs and mitigate issues such as restricted curvatures associated with concentric tube robots. However, a complete and general mechanical model for these systems remains an open problem. In this work, we propose a Cosserat rod-based framework for modeling the general case of <inline-formula> <tex-math>$n$ </tex-math></inline-formula> concentric tubes, each actuated by <inline-formula> <tex-math>$m_{i}$ </tex-math></inline-formula> tendons, where <inline-formula> <tex-math>$i = {1, ldots, n}$ </tex-math></inline-formula>. The model allows each tube to twist and elongate while enforcing a shared centerline for bending. We validate the proposed framework through experiments with two-tube and three-tube assemblies under various tendon routing configurations, achieving tip prediction errors < 4% of the robot’s total length. We further demonstrate the model’s generality by applying it to existing robots in the field, where maximum tip deviations remain around 5% of the total length. This model provides a foundation for accurate shape estimation and control of advanced tendon-actuated concentric tube robots.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 3","pages":"1059-1071"},"PeriodicalIF":4.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148747787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Region Constraint-Based Recognition and Localization Network of Multi-Instrument for Robotic Laparoscope Holders 基于区域约束的机器人腹腔镜支架多仪器识别与定位网络

IF 4.3

IEEE transactions on medical robotics and bionics Pub Date : 2026-08-01 Epub Date: 2026-07-13 DOI: 10.1109/TMRB.2026.3712797

Jiayi Zhang;Rui Zhang;Wei Shi;Yechen Zhu;Suet To;Wenkui Wang;Yu Chen;Xin Gao

{"title":"Region Constraint-Based Recognition and Localization Network of Multi-Instrument for Robotic Laparoscope Holders","authors":"Jiayi Zhang;Rui Zhang;Wei Shi;Yechen Zhu;Suet To;Wenkui Wang;Yu Chen;Xin Gao","doi":"10.1109/TMRB.2026.3712797","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3712797","url":null,"abstract":"In laparoscopic surgery, robotic laparoscope holders can autonomously guide the laparoscope, replacing human camera assistants to provide a stable surgical field of view. However, real-time and accurate recognition and localization of surgical instruments are critical for autonomous laparoscope guidance, yet they are significantly hindered by challenges such as blood occlusion and specular reflection. To address this, our study proposes a region constraint-based multi-instrument recognition and localization network (RC-MRLNet), which employs a multi-task learning strategy. Our network framework integrates a multi-instance segmentation network (MSNet) and a saliency map regression network (SRNet) to simultaneously achieve multi-instrument recognition and localization. Additionally, a semantic segmentation network (SSNet) refines performance by extracting shaft and manipulator regions, while a novel two-stage multi-target cooperative optimization strategy enhances training coordination. Evaluated on public datasets from different laparoscopic surgeries, RC-MRLNet achieves real-time inference at 65 frames per second (FPS) with superior accuracy: a dice similarity coefficient (DSC) of 0.967 and saliency map similarity (SMS) of 0.365, outperforming existing methods by 1.5% and 20.6%, respectively. Cross-domain testing further demonstrates its strong generalization capability. This study presents an effective solution for real-time and accurate recognition and localization of surgical instruments, showcasing significant potential to advance robotic laparoscope assistance in minimally invasive surgery.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 3","pages":"1133-1142"},"PeriodicalIF":4.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148749689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and Evaluation of a Compliant Quasi-Direct Drive End-Effector for Safe Robotic Ultrasound Imaging 一种柔性准直接驱动末端执行器的设计与评价

IF 4.3

IEEE transactions on medical robotics and bionics Pub Date : 2026-08-01 Epub Date: 2026-06-01 DOI: 10.1109/TMRB.2026.3699216

Danyi Chen;Ravi Prakash;Vincent Y. Wang;Zacharias Chen;Sarah Dias;Daniel M. Buckland;Leila J. Bridgeman;Siobhan R. Oca;Brian P. Mann

{"title":"Design and Evaluation of a Compliant Quasi-Direct Drive End-Effector for Safe Robotic Ultrasound Imaging","authors":"Danyi Chen;Ravi Prakash;Vincent Y. Wang;Zacharias Chen;Sarah Dias;Daniel M. Buckland;Leila J. Bridgeman;Siobhan R. Oca;Brian P. Mann","doi":"10.1109/TMRB.2026.3699216","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3699216","url":null,"abstract":"Robot-assisted ultrasound scanning promises to advance autonomous and accessible medical imaging. However, ensuring patient safety and compliant human-robot interaction during probe contact poses a significant challenge. Most existing systems either have high mechanical stiffness or trade performance for compliance. This paper presents a novel compliant end-effector designed to mount on robotic arms for safe and accurate robotic ultrasound imaging, using a quasi-direct drive actuator to achieve passive mechanical compliance and precise active force control. To evaluate the end-effector’s performance, we developed an <italic>ex vivo</i> dynamic motion simulator platform for contact and scanning testing on tissue under simulated movements. The end-effector was evaluated against a UR3e robot arm using conventional force control strategies as a baseline. Single-point contact experiments from 2.5 N to 15 N show that the end-effector reduced force tracking RMS error by 80.1% on average. Trajectory scanning experiments at different speeds showed an average of 68.0% reduction in force tracking error. Statistically significant improvements were observed in four of six quantitative image quality and stability metrics using the end-effector. This work presents a novel approach for designing and evaluating compliant end-effectors, with the goal of improving safety and reliability in robotic ultrasound.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 3","pages":"1383-1395"},"PeriodicalIF":4.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148749712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic OCT Scanning of Moving Soft Tissue Using Continuum Endoscopic Robots 使用连续镜机器人对运动软组织进行自动OCT扫描

IF 4.3

IEEE transactions on medical robotics and bionics Pub Date : 2026-08-01 Epub Date: 2026-04-06 DOI: 10.1109/TMRB.2026.3680966

Guiqiu Liao;Sujit Kumar Sahu;Philippe Zanne;Benoit Rosa;Florent Nageotte;Michel de Mathelin;Diego Dall'Alba;Michalina J. Gora

{"title":"Automatic OCT Scanning of Moving Soft Tissue Using Continuum Endoscopic Robots","authors":"Guiqiu Liao;Sujit Kumar Sahu;Philippe Zanne;Benoit Rosa;Florent Nageotte;Michel de Mathelin;Diego Dall'Alba;Michalina J. Gora","doi":"10.1109/TMRB.2026.3680966","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3680966","url":null,"abstract":"Optical Coherence Tomography (OCT) offers real-time, high-resolution imaging of tissue internal structures, presenting a potential enhancement over conventional endoscopic examination of the digestive system surface. However, miniaturized OCT probes typically present challenges with a limited field of view and require precise positioning to optimize tissue visualization. This research addresses the challenge by leveraging robotizing a surgical endoscope and a steerable OCT probe to provide closed-loop scanning of larger areas in the presence of tissue motion. We propose a robotic multi-pullback technique, supported by real-time, tactile feedback from OCT images. Deep learning algorithms are employed for OCT image stabilization and segmentation, enabling autonomous robotic volumetric OCT scanning. Compared to previous works, our closed-loop control approach considers, in addition to the probe flexibility, also the movements and deformations of the tissue during the robotic scanning. For experimental validation, we designed a moving soft tissue phantom with force sensing and compared the proposed method with fixed-angle approaches. Our method consistently maintained minimal contact forces (around 50 mN) and clear tissue visibility for tissues with different stiffness and motion patterns. Resulting imaging quality was further validated using a multi-layer optical phantom under dynamic conditions.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 3","pages":"933-944"},"PeriodicalIF":4.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148747021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0