RESUMO
Purpose: To assess the accuracy of aortic valve calcium (AVC), mitral annular calcium (MAC), and coronary artery calcium (CAC) quantification and risk stratification using virtual noncontrast (VNC) images from late enhancement photon-counting detector CT as compared with true noncontrast images. Materials and Methods: This retrospective, institutional review board-approved study evaluated patients undergoing photon-counting detector CT between January and September 2022. VNC images were reconstructed from late enhancement cardiac scans at 60, 70, 80, and 90 keV using quantum iterative reconstruction (QIR) strengths of 2-4. AVC, MAC, and CAC were quantified on VNC images and compared with quantification of AVC, MAC, and CAC on true noncontrast images using Bland-Altman analyses, regression models, intraclass correlation coefficients (ICC), and Wilcoxon tests. Agreement between severe aortic stenosis likelihood categories and CAC risk categories determined from VNC and true noncontrast images was assessed by weighted κ analysis. Results: Ninety patients were included (mean age, 80 years ± 8 [SD]; 49 male patients). Scores were similar on true noncontrast images and VNC images at 80 keV for AVC and MAC, regardless of QIR strengths, and VNC images at 70 keV with QIR 4 for CAC (all P > .05). The best results were achieved using VNC images at 80 keV with QIR 4 for AVC (mean difference, 3; ICC = 0.992; r = 0.98) and MAC (mean difference, 6; ICC = 0.998; r = 0.99), and VNC images at 70 keV with QIR 4 for CAC (mean difference, 28; ICC = 0.996; r = 0.99). Agreement between calcification categories was excellent on VNC images at 80 keV for AVC (κ = 0.974) and on VNC images at 70 keV for CAC (κ = 0.967). Conclusion: VNC images from cardiac photon-counting detector CT enables patient risk stratification and accurate quantification of AVC, MAC, and CAC.Keywords: Coronary Arteries, Aortic Valve, Mitral Valve, Aortic Stenosis, Calcifications, Photon-counting Detector CT Supplemental material is available for this article © RSNA, 2023.
