Influence of deep learning image reconstruction and adaptive statistical iterative reconstruction-V on coronary artery calcium quantification

Abstract

BACKGROUND: Deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-V (ASIR-V) has been used for cardiac computed tomography imaging. However, DLIR and ASIR-V may influence the quantification of coronary artery calcification (CAC). METHODS: CT images of 96 patients were reconstructed using filtered back projection (FBP), ASIR-V 50%, and three levels of DLIR [low (L), medium (M), and high (H)]. Image noise and the Agatston, volume, and mass scores were compared between the reconstructions. Patients were stratified into six Agatston score-based risk categories and five CAC percentile risk categories adjusted by Agatston score, age, sex, and race. The number of patients who were switched to another risk stratification group when ASIR-V and DLIR were used was compared. Bland-Altman plots were used to present the agreement of Agatston scores between FBP and the different reconstruction techniques. RESULTS: Compared to that with FBP, image noise was significantly decreased with ASIR-V 50%, and DLIR-L, -M, and -H (all P<0.001). The Agatston, volume, and mass scores with ASIR-V 50% and DLIR-L, -M, and -H showed significant decreases in comparison to those calculated with FBP (all P<0.001). Severity classification showed no significant differences between the five reconstruction techniques in any of the CAC score-based risk categories (all P>0.05). CONCLUSIONS: DLIR and ASIR-V show great potential for improving CT image quality, and appear to have no pronounced impact on CAC quantification or Agatston score-based risk stratification.