Multicenter Comparison of AI Deep Learning Reconstruction, Iterative Reconstruction, and Filtered Back Projection for Coronary Artery Calcification Scoring

ObjectiveTo validate the feasibility of AI Deep Learning Reconstruction for Coronary Artery Calcification Scoring in order to decrease radiation exposure on a 4cm detector CT scanner. This is the first such validation on devices that are most commonly utilized for this procedure. MethodsData from 105 consecutive patients referred for Coronary Artery Calcification Scoring (CACS) in 4 centers was reconstructed with Filtered Back Projection (FBP), Iterative Reconstruction (Hybrid-IR), and AI Deep Learning Reconstruction (AI DLR), and analyzed both quantitatively and qualitatively to determine if AI DLR can be routinely used for this purpose. Additional phantom testing was performed to determine if further dose reduction can be accomplished with AI DLR while maintaining or improving image quality compared to current Hybrid-IR reconstruction. ResultsQuantitively, there was excellent agreement between the three reconstructions (FBP, Hybrid IR and AI DLR) with an interclass coefficient of 0.99. The mean CACS for Filtered Back Projection Reconstructions was 111.05. The mean CACS for Hybrid-IR was 91.30. The mean CACS for AI Deep Learning Reconstructions was 93.50. Qualitatively, image quality was consistently better with AI DLR than with Hybrid-IR at both soft tissue and lung windowing. Based on our phantom experiments, AI DLR allows for dose reduction of at least a 37% without any image quality penalty compared to Hybrid-IR. ConclusionsThe use of AI DLR for use in CACS on 4 cm coverage CT scanner has been quantitatively and qualitatively validated for use for the first time. AI DLR produces qualitatively and quantitively better image quality than Hybrid-IR at the same dose level, and produces good agreement in categorization of Agatston scores. In vivo and in vitro evaluations show that AI DLR will allow for an at least a 37% further dose reduction on a 4 cm coverage CT scanner.