Impact of Reconstruction Algorithm and Filter on Artificial Intelligence Measurement of Coronary Artery Calcification Scores

Objective: This study aims to analyze the impact of the reconstruction algorithm and filter on the measurement of coronary artery calcification scores using artificial intelligence (AI) and to evaluate the accuracy and consistency of risk stratification by AI-measured calcification scores. Methods: A retrospective analysis was conducted on coronary artery calcification score CT images from January 2024 at our hospital. A total of 30 cases were included, with 18 males and 12 females. Twelve groups of images were reconstructed using different reconstruction algorithms (FBP, iterative iDose4 level 1-5) and filtering kernels (Cardiac Standard and Cardiac Sharp). Two methods including an AI image workstation and a CT workstation were used to measure the coronary artery Agatston score (AS), volume score (VS), and mass score (MS) for each group of images, and to calculate risk stratification. The AS, VS, and MS obtained from different reconstruction algorithms were subjected to multiple-sample Friedman tests using measurements from both AI and CT workstations. For images using two different filtering kernels, paired Wilcoxon tests were conducted on the AS, VS, and MS measured by AI and CT workstations. Paired Wilcoxon tests and intraclass correlation coefficients (ICC) were performed on the AS, VS, and MS measured by both methods across the 12 groups of images. The consistency of risk stratification was analyzed using the weighted Kappa coefficient. The results measured by the CT workstation were used as a reference. Results: With the Cardiac Standard filtering kernel, there were statistically significant differences in the Agatston score (AS) and volume score (VS) measured by AI across different reconstruction algorithms (AS: X²=32.577; VS: X²=17.125, both P<0.05), but no significant difference in the mass score (MS) (X²=10.569, P>0.05). With the Cardiac Sharp filtering kernel, there were no statistically significant differences in AS, VS, and MS measured by AI (AS: X²=9.820; VS: X²=6.810; MS: X²=7.820, all P>0.05). Statistically significant differences were observed in AS and VS measured by the CT workstation across different reconstruction algorithms (AS: Cardiac Standard: X²=62.972, Cardiac Sharp: X²=134.82; VS: Cardiac Standard: X²=17.056, Cardiac Sharp: X²=91.417, all P<0.05). Statistical differences were present in AS, VS, and MS measured by AI using both filtering kernels (all P<0.05) and in AS and VS measured by the CT workstation using both filtering kernels (all P<0.05). Under the Cardiac Standard filtering kernel, there were no significant differences in AS, VS, and MS between the two measurement methods (P>0.05), while under the Cardiac Sharp filtering kernel, there were significant differences in AS and VS between the two methods (P<0.05), with good consistency (ICC > 0.75). The highest consistency in risk stratification was observed in image groups using the Cardiac Standard filtering kernel and iDose levels 1 and 2, with a Kappa coefficient of 0.967. Conclusion: The choice of reconstruction algorithms and filtering kernels greatly affects the accuracy of coronary artery calcification scores. Both AI and CT workstations rely on these choices, making careful selection critical in clinical practice.