X Ray Coronary Angiography Image

X-ray coronary angiography (XCA) images are crucial for diagnosing coronary artery disease, but their 2D nature limits visualization of the complex 3D vessel network. Current research focuses on improving automated 3D reconstruction from multiple XCA views, employing deep learning techniques like graph convolutional networks and efficient U-Net architectures for vessel segmentation and stenosis detection. These advancements aim to improve the accuracy, speed, and reproducibility of analysis, ultimately leading to more efficient and reliable diagnosis and treatment of coronary heart disease. Robust principal component analysis and tensor-based methods are also being explored to enhance vessel extraction and image quality, particularly in challenging scenarios with noisy backgrounds and low contrast.