# Direct Quantification for Coronary Artery Stenosis Using Multiview Learning

## TL;DR

This paper introduces a novel multiview learning model, DMQCA, for automatic and accurate quantification of coronary artery stenosis from X-ray images, aiding diagnosis and treatment planning.

## Contribution

It presents a new deep learning framework with multiview, attention, and key-frame modules for direct estimation of multiple coronary stenosis indices.

## Key findings

- Achieves accurate estimation of stenosis indices.
- Utilizes attention mechanisms to focus on lesion features.
- Provides an automatic tool for clinical diagnosis.

## Abstract

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.