Super-resolution deep learning reconstruction to improve image quality of coronary CT angiography.

ABSTRACT:

Purpose

To compare the objective and subjective image quality and diagnostic performance for coronary stenosis of normal-dose model-based iterative reconstruction and reduced-dose super-resolution deep learning reconstruction in coronary CT angiography.

Materials and methods

This single-center retrospective study included 52 patients (mean age, 68 years ± 10 [SD]; 41 men) who underwent serial coronary CT angiography and subsequent invasive coronary angiography between January and November 2022. The first 25 patients were scanned with a standard dose using model-based iterative reconstruction. The last 27 patients were scanned with a reduced dose using super-resolution deep learning reconstruction. Per-patient objective and subjective image qualities were compared. Diagnostic

SUBMITTER: Tomizawa N 

PROVIDER: S-EPMC12428329 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature