Influence of computed tomography reconstruction algorithms on coronary artery calcium scores and reader agreement

Coronary artery calcium scoring (CACS) is central to cardiovascular risk stratification. Differences between reconstruction algorithms may introduce inconsistencies in Agatston scores, resulting in reduced reliability and diminished clinical utility.

120 CACS scans were reconstructed using filtered back projection (FBP), iterative reconstruction (ASIR-V), and deep-learning-based image reconstruction (DLIR-H), and independently scored by two blinded readers. Intra-reader and inter-reader agreement were assessed categorically using Bayesian cumulative logit mixed-effects modeling and weighted kappa, and continuously using intraclass correlation coefficients (ICCs) of log-transformed scores. Reader confidence was compared using Friedman and Wilcoxon tests.

Categorical inter-reader agreement was highest for DLIR-H (agreement 94.2%, κ ​= ​0.994), followed by ASIR-V (90.0%, κ ​= ​0.984) and FBP (86.7%, κ ​= ​0.976). Inter-reader ICCs were significantly higher for DLIR-H (0.996) than both FBP (0.974, p ​= ​0.002) and ASIR-V (0.984, p ​< ​0.001). FBP and ASIR-V both exhibited a reader-dependent bias, with one reader assigning higher calcium scores (categorical: FBP OR 11.1 [3.7-36.0], ASIR-V OR 3.8 [1.3-12.1]; continuous: Δlog score +0.23 and ​+ ​0.11, respectively), a discrepancy not present with DLIR-H. Within-reader contrasts showed higher Agatston scores for FBP than DLIR-H for one reader (categorical: OR 4.03 [1.29-12.14], continuous: Δlog score +0.16). Mean reader confidence differed across algorithms and was greatest for DLIR-H.

DLIR-H, a deep-learning-based reconstruction algorithm, significantly enhances inter- and intra-reader reliability and reader confidence for CACS compared with FBP and ASIR-V, offering significantly improved reproducibility of CACS for cardiovascular risk assessment.