Percutaneous coronary intervention is widely applied for the treatment of coronary artery disease under the guidance of X-ray coronary angiography (XCA) image. However, the projective nature of XCA causes the loss of 3D structural information, which ...
OBJECTIVE: This study aimed to investigate whether a deep learning reconstruction (DLR) method improves the image quality, stent evaluation, and visibility of the valve apparatus in coronary computed tomography angiography (CCTA) when compared with f...
European journal of nuclear medicine and molecular imaging
Sep 14, 2022
PURPOSE: Deep learning (DL) models have been shown to outperform total perfusion deficit (TPD) quantification in predicting obstructive coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, previously published methods have ...
Acta radiologica (Stockholm, Sweden : 1987)
Aug 17, 2022
BACKGROUND: The demand for homogeneous and higher vascular contrast enhancement is critical to provide an appropriate interpretation of abnormal vascular findings in coronary computed tomography angiography (CTA).
IEEE journal of biomedical and health informatics
Aug 11, 2022
Automatic coronary artery segmentation is of great value in diagnosing coronary disease. In this paper, we propose an automatic coronary artery segmentation method for coronary computerized tomography angiography (CCTA) images based on a deep convolu...
AJR. American journal of roentgenology
Aug 10, 2022
Coronary CTA with hybrid iterative reconstruction (HIR) is prone to false-positive results for in-stent restenosis due to stent-related blooming artifact. The purpose of this study is to assess the impact of deep learning reconstruction (DLR), subt...
Revista portuguesa de cardiologia : orgao oficial da Sociedade Portuguesa de Cardiologia = Portuguese journal of cardiology : an official journal of the Portuguese Society of Cardiology
May 26, 2022
INTRODUCTION AND OBJECTIVES: Although automatic artificial intelligence (AI) coronary angiography (CAG) segmentation is arguably the first step toward future clinical application, it is underexplored. We aimed to (1) develop AI models for CAG segment...
Optical flow is widely used in medical image processing, such as image registration, segmentation, 3D reconstruction, and temporal super-resolution. However, high-precision optical flow training datasets for medical images are challenging to produce....
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.