Optical flow-based vascular respiratory motion compensation

TL;DR

This paper introduces a real-time vascular respiratory motion compensation algorithm that predicts invisible blood vessel movements during interventions by learning correlations with visible tissues, enhancing robotic vascular procedures.

Contribution

The paper proposes the Motion-Related Compensation (MRC) algorithm that predicts vascular motion from non-vascular tissue movements, enabling real-time compensation during interventions.

Findings

Achieves vascular motion prediction in 0.032 seconds

Average prediction error of 1.086 mm

Effective in in-vivo experiments

Abstract

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.