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Contains fulltext : 287839.pdf (Publisher’s version ) (Open Access) 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 phantoms were scanned and reconstructed with four techniques. Last, scans of fifty patients were included and the Agatston calcium score was calculated for all four reconstruction techniques. FBP was used as a reference. In the phantom studies, all reconstruction techniques resulted in less detected small calcifications, up to 22%. No clinically relevant quantification changes occurred with different reconstruction techniques (less than 10%). In the patient study, the cardiovascular risk classification resulted, for all reconstruction techniques, in excellent agreement with the reference (κ = 0.96–0.97). However, MBIR resulted in significantly higher Agatston scores (61 (5.5–435.0) vs. 81.5 (9.25–435.0); p

The overall aims of this thesis, entitled ‘Coronary artery calcium detection & quantification with current and next-generation CT’, are to better understand spurious variations in coronary artery calcium (CAC) scoring and to provide strategies to address this problem. To better understand sources of variation we investigated the extent to which differences in CAC scores are attributable to the type of CT system, patient size and heart rate, level of radiation dose reduction, iterative reconstruction (IR), deep-learning reconstruction (DLR), calcium-aware reconstruction kernel, slice thickness, CAC scoring algorithms, and CT detector technology, rather than true variations in the amount of CAC. Importantly, we demonstrate that technical advances in CT can improve clinical CAC scoring, by reducing CAC scoring variability and/or reducing patient radiation dose.