Coronary Tree Extraction Using Motion Layer Separation

Fluoroscopic images contain useful information that is difficult to comprehend due to the collapse of the 3D information into 2D space. Extracting the informative layers and analyzing them separately could significantly improve the task of understanding the image content. Traditional Digital Subtraction Angiography (DSA) is not applicable for coronary angiography because of heart beat and breathing motion. In this work, we propose a layer extraction method for separating transparent motion layers in fluoroscopic image sequences, so that coronary tree can be better visualized.. The method is based on the fact that different anatomical structures possess different motion patterns, e.g., heart is beating fast, while lung is breathing slower. A multiscale implementation is used to further improve the efficiency and accuracy. The proposed approach helps to enhance the visibility of the vessel tree, both visually and quantitatively.