Diagnostic value of deep learning reconstruction-based subtraction CT-FFR in patients with calcified-related stenosis or stent implantation.

Background: The application of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) is limited due to severe coronary calcium burden or stent implantation. This study aimed to explore the diagnostic value of subtraction CT-FFR with deep learning reconstruction (DLR) or hybrid iterative reconstruction (HIR) in detecting calcified-related hemodynamically significant stenosis, and the feasibility in the application of coronary stents.

Methods: Between March 2020 and January 2022, consecutive patients with calcified-related stenosis or previous stent treatment who had undergone subtraction coronary computed tomography angiography (CTA) and invasive fractional flow reserve (FFR) were included in this prospective study.

Ultra-low-dose coronary CT angiography via super-resolution deep learning reconstruction: impact on image quality, coronary plaque, and stenosis analysis.

Objectives: To exploit the capability of super-resolution deep learning reconstruction (SR-DLR) to save radiation exposure from coronary CT angiography (CCTA) and assess its impact on image quality, coronary plaque quantification and characterization, and stenosis severity analysis.

Materials And Methods: This prospective study included 50 patients who underwent low-dose (LD) and subsequent ultra-low-dose (ULD) CCTA scans. LD CCTA images were reconstructed with hybrid iterative reconstruction (HIR) and ULD CCTA images were reconstructed with HIR and SR-DLR.