Ravigopal Sharan R, 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 30332 USA.
IEEE Trans Med Robot Bionics. 2021 Nov;3(4):928-935. doi: 10.1109/tmrb.2021.3122351. Epub 2021 Oct 22.
Mitral regurgitation (MR) is a condition caused by a deformity in the mitral valve leading to the backflow of blood into the left atrium. MR can be treated through a minimally invasive procedure and our lab is currently developing a robot that could potentially be used to treat MR. The robot would carry a clip that latches onto the valve's leaflets and closes them to minimize leakage. The robot's accurate localization is needed to navigate the clip to the leaflets successfully. This paper discusses algorithms used to track the clip's position and orientation under real-time using C-arm fluoroscopy. The positions are found through a deep learning semantic segmentation framework and the pose is found by calculating its bending and rotational angles. The robot's bending angle and the clip's rotational angle is found through an equivalent ellipse algorithm and an SVM classifier, respectively, and were validated by comparing orientations obtained from an electromagnetic tracker. The bending angle calculation has an average error of 7.7° and the rotational angle calculation is 76% for classifying them into five classes. Execution times are within 100ms and hence this could be a promising approach in real-time pose estimation.
二尖瓣反流（MR）是一种由二尖瓣畸形导致血液回流至左心房的病症。MR可通过微创手术进行治疗，我们的实验室目前正在研发一种有可能用于治疗MR的机器人。该机器人将携带一个夹子，该夹子可扣在瓣膜小叶上并将其闭合，以尽量减少渗漏。为了成功地将夹子导航至小叶，需要对机器人进行精确的定位。本文讨论了用于在实时情况下使用C形臂荧光透视法跟踪夹子位置和方向的算法。通过深度学习语义分割框架找到位置，并通过计算其弯曲和旋转角度来确定姿态。机器人的弯曲角度和夹子的旋转角度分别通过等效椭圆算法和支持向量机分类器找到，并通过比较从电磁跟踪器获得的方向进行了验证。弯曲角度计算的平均误差为7.7°，旋转角度计算将其分类为五个类别的准确率为76%。执行时间在100毫秒以内，因此这可能是一种用于实时姿态估计的有前景的方法。
