Systematic assessment of coronary calcium detectability and quantification on four generations of CT reconstruction techniques: a patient and phantom study.

ABSTRACT: In computed tomography, coronary artery calcium (CAC) scores are influenced by image reconstruction. The effect of a newly introduced deep learning-based reconstruction (DLR) on CAC scoring in relation to other algorithms is unknown. The aim of this study was to evaluate the effect of four generations of image reconstruction techniques (filtered back projection (FBP), hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), and DLR) on CAC detectability, quantification, and risk classification. First, CAC detectability was assessed with a dedicated static phantom containing 100 small calcifications varying in size and density. Second, CAC quantification was assessed with a dynamic coronary phantom with velocities equivalent to heart rates of 60-75 bpm. Both phant

SUBMITTER: Dobrolinska MM 

PROVIDER: S-EPMC9813085 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature