An Ultrasound-Guided System for Autonomous Marking of Tumor Boundaries During Robot-Assisted Surgery

IF 3.4 Q2 ENGINEERING, BIOMEDICAL IEEE transactions on medical robotics and bionics Pub Date : 2024-09-26 DOI:10.1109/TMRB.2024.3468397
Nils Marahrens;Dominic Jones;Nikita Murasovs;Chandra Shekhar Biyani;Pietro Valdastri
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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 $\mathbf {0.84}$ and false detection rates below $\mathbf {0.12}$ compared to a tracked reference for each calculation and execution of the marking trajectory for dummy and ex-vivo experiments.
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在机器人辅助手术中自主标记肿瘤边界的超声引导系统
虽然只有少数手术在机器人引导手术过程中可以使用图像引导,但这些手术仍然需要外科医生手动将获得的扫描结果与组织表面的预测位置进行比对。虽然外科医生可以通过电外科手术在器官表面标记边界,但肿瘤周围的精确边缘很可能仍然是可变的,在病理分析之前无法保证。本文首次尝试在组织表面以指定边缘自主提取和标记肿瘤边界。它提出了通过惯性测量单元(IMU)传感器融合和电外科单元(ESU)电信号接触检测进行工具-组织交互控制的首个概念,不需要力传感。我们在具有解剖表面几何形状的超声(US)模型上开发并评估了我们的方法,比较了将肿瘤投射到表面的不同策略,并在重复试验中评估了其准确性。最后,我们证明了将该方法应用于体外猪肝的可行性。在假人和体外实验中,与每次计算和执行标记轨迹的跟踪参考相比,我们实现了高于 $\mathbf {0.84}$ 的平均真阳性率和低于 $\mathbf {0.12}$ 的误检率。
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Table of Contents IEEE Transactions on Medical Robotics and Bionics Society Information Guest Editorial Special section on the Hamlyn Symposium 2023—Immersive Tech: The Future of Medicine IEEE Transactions on Medical Robotics and Bionics Publication Information IEEE Transactions on Medical Robotics and Bionics Information for Authors
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