IEEE transactions on medical robotics and bionics最新文献

{"title":"Telemanipulated Vascular Intervention System for Minimally Invasive Surgery","authors":"Siyi Wei;Zhiwei Wu;Jinhui Zhang;Shaomeng Gu;Zhanxin Geng;Jiahao Luo;Yueyang Gao;Zheng Li","doi":"10.1109/TMRB.2024.3473299","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3473299","url":null,"abstract":"Minimally invasive surgery, as a promising treatment method for coronary heart disease and intracranial aneurysm, has received extensive research interest due to its appealing characteristics, e.g., the little surgical trauma, short rehabilitation time, determined curative effect, and less pain. However, the accumulated X-ray radiation during the percutaneous coronary intervention (PCI) and neurovascular intervention (NVI) greatly increases the probability of medical staff suffering from cataracts and brain tumors. In this article, the telemanipulated vascular intervention (TVI) system is presented, a compact and versatile vascular interventional system. The TVI system comprised of a leader joystick, a follower delivery device, and a graphical user interface is designed for intravascular delivery during the robot-assisted PCI and robot-assisted NVI. The performance of the TVI system is evaluated by demonstrating its ability to achieve telemanipulated navigation in the real-sized 3D cardio-cerebrovascular model with coronary stenosis and intracranial aneurysms. The experimental results demonstrate that the TVI system can navigate to 3 types of coronary stenosis, 6 types of cerebral artery, and an intracranial aneurysm with a diameter of 8 mm. To further demonstrate the performance of the TVI system, the robot-assisted renal artery angioplasty is conducted in a rabbit model for preclinical evaluation. These promising results indicate that the TVI system is capable of precisely manipulating the guidewire remotely, mitigating the health risks associated with prolonged exposure to X-ray radiation for interventionists.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"6 4","pages":"1512-1525"},"PeriodicalIF":3.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600311","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}

{"title":"Design of a Cable-Suspended Robot for Early Stage Gait Rehabilitation","authors":"Giacomo Zuccon;Alberto Doria;Giulio Rosati;Christopher A. Johnson;Lee McEligot;Kohl Hertz;Kyle Fernan;Ishaq Khan;V. Reggie Edgerton;David J. Reinkensmeyer","doi":"10.1109/TMRB.2024.3468381","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3468381","url":null,"abstract":"Practicing walking motions while supine might help accelerate recovery after neurologic injury. This paper presents the design, modeling, and initial testing of a novel cable-driven device called AirStep that compensates for the weight of the legs, facilitating air-stepping practice while supine. AirStep integrates multiple mass-spring counterbalancing mechanisms to minimize the effect of gravity throughout the entire gait cycle such that patients can perform active or passive stepping motions in a near-zero gravity environment. Handles allow a rehabilitation therapist to manually assist leg motion through the cables as needed. Data acquired from an optical motion capture system validated the mathematical model of the AirStep, showing that the leg trajectories in air-stepping resembled those from running. In pilot testing, two individuals with spinal cord injury (SCI) required manual assistance at the hips from a physical therapist to achieve step-like motions through the AirStep interface. AirStep can apply low-forces, allow stepping in the supine position, and can quantify changes in patient-generated force production. Compared to other rehabilitation robots, AirStep offers the advantages of a low-cost mechanical structure, high acceptability by the patient and easy transportability aside a hospital bed, making the AirStep a good candidate for adoption in the early-stage gait rehabilitation.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"6 4","pages":"1616-1626"},"PeriodicalIF":3.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600361","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}

{"title":"Auto-CALM: Autonomous Computer-Assisted Laser Microsurgery","authors":"Shunlei Li;Ajay Gunalan;Muhammad Adeel Azam;Veronica Penza;Darwin G. Caldwell;Leonardo S. Mattos","doi":"10.1109/TMRB.2024.3468385","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3468385","url":null,"abstract":"This paper introduces a new controller for real-time dynamic laser ablation: the autonomous computer-assisted laser microsurgery system (Auto-CALM). Auto-CALM allows the surgeon to define the ablation area, which is then precisely ablated by the system while compensating for tissue motions and deformations. This is achieved based on three control blocks: target tracking, laser tracking, and ablation control algorithm. The ablation area, i.e., the target, is defined by the surgeon using a graphics tablet and graphics overlay on the surgical video. This target is then tracked in real-time using improved optical flow and a novel scaling strategy that makes the system robust against tissue deformations. Laser tracking is based on a pretrained Segment Anything Model that localizes the position of the laser in the surgical video. The ablation algorithm generates a trajectory to ablate the target given the dynamically updated laser position and target position. This enables motion compensation, which increases the accuracy of the system. Auto-CALM was validated through laser ablation experiments based on a porcine larynx fixed to a breathing motion simulation stage. The obtained results were also compared with those achieved under manual operation of CALM, and under autonomous ablation using the Track Anything Model as the target tracking algorithm. Furthermore, four different parts of the ex-vivo porcine larynx were tested to investigate different tracking features and the robustness of the system. Auto-CALM achieved a Dice Similarity Coefficient of 95.49% under the most challenging conditions (including tissue motion and no feature), reaching an ablation speed of \u0000<inline-formula> <tex-math>$1.43~mm^{2}/s$ </tex-math></inline-formula>\u0000. The accuracy and usability of the integrated platform bear potential for the accurate ablation of tissue volumes in clinical settings. Further ex-vivo and in-vivo animal studies shall help translate these findings to clinical use.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"6 4","pages":"1423-1435"},"PeriodicalIF":3.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600367","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}

{"title":"An Ultrasound-Guided System for Autonomous Marking of Tumor Boundaries During Robot-Assisted Surgery","authors":"Nils Marahrens;Dominic Jones;Nikita Murasovs;Chandra Shekhar Biyani;Pietro Valdastri","doi":"10.1109/TMRB.2024.3468397","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3468397","url":null,"abstract":"While only a limited number of procedures have image guidance available during robotically guided surgery, they still require the surgeon to manually reference the obtained scans to their projected location on the tissue surface. While the surgeon may mark the boundaries on the organ surface via electrosurgery, the precise margin around the tumor is likely to remain variable and not guaranteed before a pathological analysis. This paper presents a first attempt to autonomously extract and mark tumor boundaries with a specified margin on the tissue surface. It presents a first concept for tool-tissue interaction control via Inertial Measurement Unit (IMU) sensor fusion and contact detection from the electrical signals of the Electrosurgical Unit (ESU), requiring no force sensing. We develop and assess our approach on Ultrasound (US) phantoms with anatomical surface geometries, comparing different strategies for projecting the tumor onto the surface and assessing its accuracy in repeated trials. Finally, we demonstrate the feasibility of translating the approach to an ex-vivo porcine liver. We achieve mean true positive rates above \u0000<inline-formula> <tex-math>$mathbf {0.84}$ </tex-math></inline-formula>\u0000 and false detection rates below \u0000<inline-formula> <tex-math>$mathbf {0.12}$ </tex-math></inline-formula>\u0000 compared to a tracked reference for each calculation and execution of the marking trajectory for dummy and ex-vivo experiments.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"6 4","pages":"1699-1712"},"PeriodicalIF":3.4,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600199","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}

{"title":"Magnetic Ball Chain Robots for Cardiac Arrhythmia Treatment","authors":"Giovanni Pittiglio;Fabio Leuenberger;Margherita Mencattelli;Max McCandless;Edward O’Leary;Pierre E. Dupont","doi":"10.1109/TMRB.2024.3465828","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3465828","url":null,"abstract":"This paper introduces a novel magnetic navigation system for cardiac ablation. The system is formed from two key elements: a magnetic ablation catheter consisting of a chain of spherical permanent magnets; and an actuation system comprised of two cart-mounted permanent magnets undergoing pure rotation. The catheter design enables a large magnetic content with the goal of minimizing the footprint of the actuation system for easier integration with the clinical workflow. We present a quasi-static model of the catheter, the design of the actuation units, and their control modalities. Experimental validation shows that we can use small rotating magnets (119mm diameter) to reach cardiac ablation targets while generating clinically-relevant forces. Catheter control using a joystick is compared with manual catheter control. While total task completion time is similar, smoother navigation is observed using the proposed robotic system. We also demonstrate that the ball chain can ablate heart tissue and generate lesions comparable to the current clinical ablation catheters.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"6 4","pages":"1322-1333"},"PeriodicalIF":3.4,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600366","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}

{"title":"Automatic Focus Adjustment for Single-Spot Tissue Temperature Control in Robotic Laser Surgery","authors":"Nicholas E. Pacheco;Chaitanya S. Gaddipati;Siavash Farzan;Loris Fichera","doi":"10.1109/TMRB.2024.3464670","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3464670","url":null,"abstract":"This paper reports on a study whose goal is to control the tissue temperature at a specific spot during laser surgery, for the purpose of, inducing coagulation or sealing blood vessels. We propose a solution that relies on the automatic adjustment of the laser focus (and thus how concentrated the laser beam is), combined with the use of an infrared thermal camera for non-contact temperature monitoring. One of the main challenges in the control of thermal laser-tissue interactions is that these interactions can be hard to predict due to the inherent variability in the molecular composition of biological tissue. To tackle this challenge, we explore two different control approaches: (1) a model-less controller using a Proportional-Integral (PI) formulation, whose gains are set via a tuning procedure performed on laboratory-made tissue phantoms; and (2) a model-based controller using an adaptive formulation that makes it robust to tissue variability. We report on experiments, performed on four types of tissue specimens, showing that both controllers can consistently achieve temperature tracking with a Root-Mean-Square Error (RMSE) \u0000<inline-formula> <tex-math>$approx$ </tex-math></inline-formula>\u0000 1 °C.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"6 4","pages":"1386-1390"},"PeriodicalIF":3.4,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600330","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}

{"title":"SLAM-TKA: Simultaneously Localizing X-Ray Device and Mapping Pins in Conventional Total Knee Arthroplasty","authors":"Shuai Zhang;Liang Zhao;Shoudong Huang;Hua Wang;Qi Luo;Qi Hao;Danail Stoyanov","doi":"10.1109/TMRB.2024.3465565","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3465565","url":null,"abstract":"This paper presents a novel simultaneous localization and mapping (SLAM) technique, termed SLAM-TKA, for assisting total knee arthroplasty (TKA), a highly effective orthopaedic surgery that replaces arthritic or dysfunctional joint surfaces with knee prostheses. Our proposed SLAM algorithm uses information from a pre-operative tibia CT scan, intra-operative 2D X-ray images, and a trocar pin 3D mesh model to simultaneously localise the X-ray device and map the two trocar pins. Then, the estimated pins are used to evaluate the accuracy of the bone resection plane before the actual bone cutting, which plays a crucial role in precisely implanting the knee prostheses. To ensure high accuracy and robustness of the proposed SLAM algorithm, three energy terms are proposed and used together to align the edge observations of the tibia, fibula and pins on the intra-operative X-ray images and their corresponding pre-operative 3D mesh models in both 2D and 3D space. To enable the proposed iteration-based SLAM algorithm to be implemented in real-time such that the evaluation processing does not interrupt much on the workflow of TKA, the data association of edge correspondences matching and exhausted points-to-mesh distance calculation are pre-computed using the signed distance field method. Simulations are used to evaluate the accuracy and robustness of the proposed algorithm, and the experiments using in-vivo datasets from five patients demonstrate the high accuracy and efficiency in practice. The code and datasets are released at \u0000<uri>https://github.com/zsustc/SLAM-TKA</uri>\u0000.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"6 4","pages":"1526-1541"},"PeriodicalIF":3.4,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600395","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}

{"title":"Proximity Servoed Minimally Invasive Continuum Robot for Endoscopic Interventions","authors":"Christian Marzi;Maximilian Themistocli;Björn Hein;Franziska Mathis-Ullrich","doi":"10.1109/TMRB.2024.3464127","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3464127","url":null,"abstract":"Minimally invasive continuum robots face limitations in accessing environmental and spatial information on the situs. However, such information would often be necessary for control and automation features in surgical use. Centering an endoscopic system within a hollow organ can be such a feature, providing the benefit of reduced risk of injury and assistance for navigation. To leverage such an application, this work investigates a proximity servoed continuum robot. A sensorized tip combines capacitive electrodes, a camera, and illumination and uses capacitive proximity sensing to determine the enclosing environment’s center point. A controller is presented that uses this information to center the robot’s tip. The system is evaluated in a dynamic phantom, where an average accuracy of 10.0 mm could be demonstrated and contact to the phantom’s wall was avoided during 98% of the experiment time. In a second phantom experiment, it is demonstrated how this controller can be applied to follow the center line of a bent anatomical structure. Future work should focus on improving accuracy and versatility of the system, aiming for application in more challenging and irregular environments, such as ex vivo or in vivo organs.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"6 4","pages":"1738-1747"},"PeriodicalIF":3.4,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600298","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}

{"title":"A Dual-Branch Fusion Network for Surgical Instrument Segmentation","authors":"Lei Yang;Chenxu Zhai;Hongyong Wang;Yanhong Liu;Guibin Bian","doi":"10.1109/TMRB.2024.3464748","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3464748","url":null,"abstract":"Surgical robots have become integral to contemporary surgical procedures, with the precise segmentation of surgical instruments constituting a crucial prerequisite for ensuring their stable functionality. However, numerous factors continue to influence segmentation outcomes, including intricate surgical environments, varying viewpoints, diminished contrast between surgical instruments and surroundings, divergent sizes and shapes of instruments, and imbalanced categories. In this paper, a novel dual-branch fusion network, designated DBF-Net, is presented, which integrates both convolutional neural network (CNN) and Transformer architectures to facilitate automatic segmentation of surgical instruments. For addressing the deficiencies in feature extraction capacity in CNNs or Transformer architectures, a dual-path encoding unit is introduced to proficiently represent local detail features and global context. Meanwhile, to enhance the fusion of features extracted from the dual paths, a CNN-Transformer fusion (CTF) module is proposed, to efficiently merge features from the CNN and Transformer structures, contributing to the effective representation of both local detail features and global contextual features. Further refinement is pursued through an multi-scale feature aggregation (MFAG) module and a local feature enhancement (LFE) module, to refine local contextual features at each layer. In addition, an attention-guided enhancement (AGE) module is incorporated for feature refinement of local feature maps. Finally, an multi-scale global feature representation (MGFR) module is introduced, facilitating the extraction and aggregation of multi-scale features, and a progressive fusion module (PFM) culminates in the aggregation of full-scale features from the decoder. Experimental results underscore the superior segmentation performance of proposed network compared to other state-of-the-art (SOTA) segmentation models for surgical instruments, which have well validated the efficacy of proposed network architecture in advancing the field of surgical instrument segmentation.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"6 4","pages":"1542-1554"},"PeriodicalIF":3.4,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600396","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}

{"title":"Therapists’ Force-Profile Teach-and-Mimic Approach for Upper-Limb Rehabilitation Exoskeletons","authors":"Beatrice Luciani;Michael Sommerhalder;Marta Gandolla;Peter Wolf;Francesco Braghin;Robert Riener","doi":"10.1109/TMRB.2024.3464697","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3464697","url":null,"abstract":"In this work, we propose a framework enabling upper-limb rehabilitation exoskeletons to mimic the personalised haptic guidance of therapists. Current exoskeletons face acceptability issues as they limit physical interaction between clinicians and patients and offer only predefined levels of support that cannot be tuned during the movements, when needed. To increase acceptance, we first developed a method to estimate the therapist’s force contribution while manipulating a patient’s arm using an upper-limb exoskeleton. We achieved a precision of \u0000<inline-formula> <tex-math>$0.31Nm$ </tex-math></inline-formula>\u0000 without using direct sensors. Then, we exploited the Learning-by-demonstration paradigm to learn from the therapist’s interactions. Single-joint experiments on ANYexo demonstrate that our framework, applying the Vector-search approach, can record the joint-level therapist’s interaction forces during simple tasks, link them to the kinematics of the robot, and then provide support to the user’s limb. The support is coherent with what is learnt and changes with the real-time arm kinematic configuration of the robot, assisting whatever movement the patient executes in the end-effector space without the need for manual regulation. In this way, robotic therapy sessions can exploit therapists’ expertise while reducing their manual workload.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"6 4","pages":"1658-1665"},"PeriodicalIF":3.4,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10684729","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600102","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}