  The quantification of the coronary artery stenosis is of significant clinical
importance in coronary artery disease 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 indices estimation from multiple
features.
