This paper develops a new vascular respiratory motion compensation algorithm, Motion-Related Compensation (MRC), to conduct vascular respiratory motion compensation by extrapolating the correlation between invisible vascular and visible non-vascular. Robot-assisted vascular intervention can significantly reduce the radiation exposure of surgeons. In robot-assisted image-guided intervention, blood vessels are constantly moving/deforming due to respiration, and they are invisible in the X-ray images unless contrast agents are injected. The vascular respiratory motion compensation technique predicts 2D vascular roadmaps in live X-ray images. When blood vessels are visible after contrast agents injection, vascular respiratory motion compensation is conducted based on the sparse Lucas-Kanade feature tracker. An MRC model is trained to learn the correlation between vascular and non-vascular motions. During the intervention, the invisible blood vessels are predicted with visible tissues and the trained MRC model. Moreover, a Gaussian-based outlier filter is adopted for refinement. Experiments on in-vivo data sets show that the proposed method can yield vascular respiratory motion compensation in 0.032 sec, with an average error 1.086 mm. Our real-time and accurate vascular respiratory motion compensation approach contributes to modern vascular intervention and surgical robots.

翻译：本文提出一种新的血管呼吸运动补偿算法——运动相关补偿（MRC），通过外推不可见血管与可见非血管组织之间的相关性，实现血管呼吸运动补偿。机器人辅助血管介入手术能显著减少医生的辐射暴露。在机器人辅助图像引导介入过程中，血管因呼吸运动而持续移动/变形，且在未注射造影剂时，X光图像中无法显示血管。血管呼吸运动补偿技术可在实时X光图像中预测二维血管路径图。当注射造影剂后血管可见时，基于稀疏的Lucas-Kanade特征跟踪器进行血管呼吸运动补偿。训练MRC模型以学习血管与非血管运动之间的相关性。介入过程中，利用可见组织与训练好的MRC模型预测不可见血管。此外，采用基于高斯分布的异常值滤波器进行精化。在活体数据集上的实验表明，所提方法可在0.032秒内完成血管呼吸运动补偿，平均误差为1.086毫米。我们的实时高精度血管呼吸运动补偿方法为现代血管介入手术及手术机器人提供了有力支持。