Direct Quantification for Coronary Artery Stenosis Using Multiview Learning

Abstract

The quantification of the coronary artery stenosis is of significant clinical importance in coronary artery diseases diagnosis and intervention treatment. It aims to quantify the morphological indices of the coronary artery lesions such as minimum lumen diameter, reference vessel diameter, lesion length and these indices are the reference of the interventional stent placement. In this study, we propose a direct multiview quantitative coronary angiography (DMQCA) model as an automatic clinical tool to quantify the coronary artery stenosis from X-ray coronary angiography images. The proposed DMQCA model consists of a multiview module with two attention mechanisms, a key-frame module and a regression module, to achieve direct accurate multiple-index estimation. The multi-view module comprehensively learns the spatio-temporal features of coronary arteries through a three-dimensional convolution. The attention mechanisms of each view focus on the subtle feature of the lesion region and capture the important context information. The key-frame module learns the subtle features of the stenosis through successive dilated residual blocks. The regression module finally generates the indice estimation from multiple features. We evaluate the proposed model over 2100 X-ray coronary angiography images collected from 105 subjects from two viewpoints. Compared to other direct quantification methods, our DMQCA model achieves more accurate quantification, enabling to provide a patient-specific assessment of coronary artery stenosis.