Nayar Namrata U, Desai Jaydev P
Medical Robotics and Automation (RoboMed) Laboratory, Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
IEEE Robot Autom Lett. 2024 Sep;9(9):7677-7684. doi: 10.1109/lra.2024.3426380. Epub 2024 Aug 10.
Transcatheter mitral valve repair (TMVr) is growing in popularity for non-surgical mitral regurgitation (MR) patients, but the manual operation of current TMVr devices increases radiation exposure and limits telesurgery feasibility. A robotically steerable delivery system can alleviate these problems, improving safety and precision while reducing staff fatigue. However, precise manipulation of a surgical robotic system requires system modeling and reliable external feedback. Ultrasound imaging provides visualization and guidance for precise instrument maneuvers within the body. Moreover, it is a readily available, safe, and cost-effective feedback modality, ideal for this procedure. Therefore, in this work, we use a previously derived model for the robotic transcatheter system and perform ultrasound-guided joint space control through real-time (algorithm run time: ~0.011 s) estimation of four joints simultaneously. The joints are estimated using kinematically-derived weight maps, a new technique, and a feature detection algorithm, with an accuracy of 3.19°, 2.76°, 2.41 mm, and 6.83° for the proximal bending, distal bending, prismatic motion, and distal torsion joints, respectively. This approach leverages existing knowledge about the system, demonstrating computational efficiency, intuitive comprehension, and independence from a training dataset, making it a versatile joint estimation technique. Experiments were conducted to compare the proposed method with currently employed joint estimation strategies. Additionally, real-time control was demonstrated using ultrasound feedback in a water bath, while subjecting the robotic transcatheter delivery system to similar tortuosity as encountered during a TMVr procedure.
经导管二尖瓣修复术(TMVr)在非手术二尖瓣反流(MR)患者中越来越受欢迎,但目前TMVr设备的手动操作增加了辐射暴露,并限制了远程手术的可行性。可机器人操纵的输送系统可以缓解这些问题,提高安全性和精确性,同时减轻工作人员的疲劳。然而,手术机器人系统的精确操作需要系统建模和可靠的外部反馈。超声成像为体内精确的器械操作提供可视化和引导。此外,它是一种随时可用、安全且具有成本效益的反馈方式,非常适合此手术。因此,在这项工作中,我们使用先前推导的机器人经导管系统模型,并通过同时实时(算法运行时间:约0.011秒)估计四个关节来执行超声引导的关节空间控制。使用运动学推导的权重图、一种新技术和一种特征检测算法来估计关节,近端弯曲、远端弯曲、棱柱形运动和远端扭转关节的估计精度分别为3.19°、2.76°、2.41毫米和6.83°。这种方法利用了关于系统的现有知识,展示了计算效率、直观的理解以及独立于训练数据集的特性,使其成为一种通用的关节估计技术。进行了实验以将所提出的方法与当前采用的关节估计策略进行比较。此外,在水浴中使用超声反馈进行了实时控制,同时使机器人经导管输送系统经历与TMVr手术期间遇到的类似曲折。