Extraction of Coronary Vessels in Fluoroscopic X-Ray Sequences Using Vessel Correspondence Optimization

TL;DR

This paper introduces a novel method for extracting coronary vessels from fluoroscopic X-ray sequences by using hierarchical search and Markov random field optimization to improve vessel correspondence accuracy.

Contribution

The paper presents a new hierarchical search scheme and a Markov random field framework for vessel correspondence, enhancing extraction accuracy in fluoroscopic sequences.

Findings

Effective vessel extraction demonstrated on 18 sequences

Improved vessel correspondence accuracy over existing methods

Successful extraction of newly visible vessel branches

Abstract

We present a method to extract coronary vessels from fluoroscopic x-ray sequences. Given the vessel structure for the source frame, vessel correspondence candidates in the subsequent frame are generated by a novel hierarchical search scheme to overcome the aperture problem. Optimal correspondences are determined within a Markov random field optimization framework. Post-processing is performed to extract vessel branches newly visible due to the inflow of contrast agent. Quantitative and qualitative evaluation conducted on a dataset of 18 sequences demonstrates the effectiveness of the proposed method.