Improved image quality and diagnostic performance of coronary computed tomography angiography-derived fractional flow reserve with super-resolution deep learning reconstruction.

ABSTRACT:

Background

Super-resolution deep learning reconstruction (SR-DLR) algorithm has emerged as a promising image reconstruction technique for improving the image quality of coronary computed tomography angiography (CCTA) and ensuring accurate CCTA-derived fractional flow reserve (CT-FFR) assessments even in problematic scenarios (e.g., the presence of heavily calcified plaque and stent implantation). Therefore, the purposes of this study were to evaluate the image quality of CCTA obtained with SR-DLR in comparison with conventional reconstruction methods and to investigate the diagnostic performances of different reconstruction approaches based on CT-FFR.

Methods

Fifty patients who underwent CCTA and subsequent invasive coronary angiography (ICA) were retrospectively included. A

SUBMITTER: Zou LM 

PROVIDER: S-EPMC12397712 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature