# CDKD-w+: A Keyframe Recognition Method for Coronary Digital Subtraction Angiography Video Sequence Based on w+ Space Encoding

## TL;DR

This paper introduces CDKD-w+, a new method for identifying keyframes in coronary DSA videos to improve 3D heart modeling accuracy.

## Contribution

The novel CDKD-w+ method uses w+ space encoding and pSp encoder for heartbeat keyframe recognition in coronary DSA sequences.

## Key findings

- CDKD-w+ achieved 97% accuracy on a coronary DSA keyframe recognition dataset.
- It outperformed traditional metrics like L1, SSIM, and PSNR in keyframe localization.
- The method helps reduce errors in 3D coronary artery modeling caused by heartbeats.

## Abstract

Currently, various deep learning methods can assist in medical diagnosis. Coronary Digital Subtraction Angiography (DSA) is a medical imaging technology used in cardiac interventional procedures. By employing X-ray sensors to visualize the coronary arteries, it generates two-dimensional images from any angle. However, due to the complexity of the coronary structures, the 2D images may sometimes lack sufficient information, necessitating the construction of a 3D model. Camera-level 3D modeling can be realized based on deep learning. Nevertheless, the beating of the heart results in varying degrees of arterial vasoconstriction and vasodilation, leading to substantial discrepancies between DSA sequences, which introduce errors in 3D modeling of the coronary arteries, resulting in the inability of the 3D model to reflect the coronary arteries. We propose a coronary DSA video sequence keyframe recognition method, CDKD-w+, based on w+ space encoding. The method utilizes a pSp encoder to encode the coronary DSA images, converting them into latent codes in the w+ space. Differential analysis of inter-frame latent codes is employed for heartbeat keyframe localization, aiding in coronary 3D modeling. Experimental results on a self-constructed coronary DSA heartbeat keyframe recognition dataset demonstrate an accuracy of 97%, outperforming traditional metrics such as L1, SSIM, and PSNR.