Phase Analysis Based Vessel Recompletion with Incomplete DSA for Dynamic Coronary Roadmap

Digital subtraction angiography(DSA) is a technique used extensively in interventional operation, which visualizes blood vessels and catheters through contrast agent. However, DSA image displays only local vessels due to the decreasing concentration of the contrast through blood flow. The rest of the image tend to be either unclear or unnoticeable. To solve the issues above, Dynamic Coronary Roadmap are presented aiming to create a dynamic and complete map of vessels, which is helpful for surgeons or surgical robots in clinical surgery. In this paper, we propose a simple and effective method, utilizing a segmentation network and an optical flow network to obtain a mask of the complete vessels for each frame in a DSA sequence. The segmentation model can be any of the common networks, such as Unet. For the optical flow model, this paper proposes a self-supervised training methodology to address the problem of insufficient training data for elastic deformation models. The results of this experiment indicates that the proposed algorithm is able to reconstruct complete vessels in a high recall.

I. Introduction

Interventional operation is one of the most common treatments for coronary artery diseases (CAD) [1]. Image guidance system (IGS) plays an important role in diagnosis or the planning for the interventional surgery. Digital subtraction angiography(DSA) is one of the most commonly used imaging technologies in IGS. The ability to develop DSA images is dependent on the amount of X-ray absorbed by the contrast medium. Due to the flow of contrast medium in the blood vessels over time, single frame DSA image tends not to show the complete vasculature. The intention of the paper is therefore to recomplete the incomplete vessels and present them to practitioners in a complete and continuous manner, facilitating their clinical judgements.