A Marker-Free 2D Image-Guided Method for Robot-Assisted Fracture Reduction Surgery
A Marker-Free 2D Image-Guided Method for Robot-Assisted Fracture Reduction Surgery
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DOI：
10.1007/s10846-021-01453-8
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发表时间：
2021-11
影响因子：
3.3
通讯作者：
Shijie Zhu;Zhe Zhao;Yu Chen;Jiuzheng Deng;Bicong Zhang;Jianjin Zhu;Yitong Chen;Da-wei He;Yongwei Pan;G. Zheng
中科院分区：
文献类型：
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作者：
Shijie Zhu;Zhe Zhao;Yu Chen;Jiuzheng Deng;Bicong Zhang;Jianjin Zhu;Yitong Chen;Da-wei He;Yongwei Pan;G. Zheng
闭合性骨折整复是长骨骨折微创治疗的第一步，但由于体力负担大、放射暴露过多、准确性差等原因，仍是一项具有挑战性的任务。机器人辅助骨折复位(RAFR)显示出解决这些问题的潜力，而现有的大多数方法依赖于3D图像、光学跟踪设备和侵入性机器人骨骼固定。这些方法通常具有繁琐的手术流程、复杂的硬件要求和医源性损伤风险，这阻碍了它们的临床应用。本文利用基于图像的视觉伺服技术，提出了一种新的无标记RAFR方法。首先，定义了基于二维术中图像的图像特征向量，用于路径规划和反馈控制，并使用在线估计的雅可比矩阵将图像变化映射到机器人的运动。其次，提出了一种基于几何的无探索性运动的雅可比初始化方法。第三，根据骨折整复任务的特点，提出了一种新的在线雅可比估计的状态相关加权最小二乘(SD-WLS)算法。在提出的RAFR框架中，避免了对3D图像、光学标记和僵硬的机器人骨骼固定的要求。对具有高度柔性的机器人-骨固定装置的骨折模型进行了实验。所有测试的断裂案例都在可接受的步骤内得到了很好的还原，表明了该方法的可行性。
The closed fracture reduction is the first step of the minimally invasive treatment for long bone fractures, while it remains a challenging task due to heavy physical burden, excessive radiation exposure and poor accuracy. The robot-assisted fracture reduction (RAFR) shows potential to address these problems, while most existing methods rely on 3D images, optical tracking equipment and invasive robot-bone fixations. Such methods usually have a tedious surgery workflow, complicated hardware requirements, and iatrogenic injury risks, which prevent them from clinical applications. In this paper, we proposed a novel marker-free RAFR method using the image-based visual servoing technique. Firstly, an image feature vector based on 2D intraoperative images are defined for path planning and feedback control, and an online estimated Jacobian matrix is used to map the image variation to robot motion. Secondly, a geometry-based method is developed for Jacobian initialization without exploratory movements. Thirdly, a novel state-dependent weighted least square (SD-WLS) algorithm for online Jacobian estimation is proposed according to the characteristic of fracture reduction tasks. In the proposed RAFR framework, requirements for 3D images, optical markers and rigid robot-bone fixations are avoided. Experiments on fracture models with highly flexible robot-bone fixations are performed. All the tested fracture cases were well reduced within acceptable steps, which shows the feasibility of the proposed method.