Evaluation of four computed tomography reconstruction algorithms using a coronary artery phantom.

ABSTRACT:

Background

Despite advancements in coronary computed tomography angiography (CTA), challenges in positive predictive value and specificity remain due to limited spatial resolution. The purpose of this experimental study was to investigate the effect of 2nd generation deep learning-based reconstruction (DLR) on the quantitative and qualitative image quality in coronary CTA.

Methods

A vessel model with stepwise non-calcified plaque was scanned using 320-detector CT. Image reconstruction was performed using four techniques: hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), DLR, and 2nd generation DLR. The luminal peak CT number, contrast-to-noise ratio (CNR), and edge rise slope (ERS) were quantitatively evaluated via profile curve analysis. Tw

SUBMITTER: Sawamura S 

PROVIDER: S-EPMC11007503 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature