Comparison between Deep Learning and Iterative Image Reconstruction for Coronary Computed Tomography Angiography and Their Impact on Noise and Image Quality

Abstract The aim of this study was to conduct a comprehensive assessment of image quality in coronary computed tomography angiography (CCTA) reconstructed using deep learning image reconstruction (DLIR) technique. Objective and subjective assessment methods were utilized to analyze intraindividual image quality and to compare it with conventionally used hybrid iterative reconstruction algorithm (ASiR-V) regarding their dependency to heart rate, the main cause of motion blurring in CCTA. A total of 500 consecutive patients scheduled to undergo clinically indicated CCTA under prospective electrocardiography-triggering on a 256-slice computed tomography scanner were included. The image data were reconstructed using two different reconstruction techniques (ASiR-V and TrueFidelity), enabling a direct comparison. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and vessel contrast were used to determine objective image quality. Subjective image quality was assessed using a 4-point Likert scale. DLIR exhibited significantly better SNR in the aortic root, and this difference intensified with increasing heart rate. In contrast, SNR deteriorated with increasing heart rate for ASiR-V. The subjective comparison by two radiologists revealed significantly better ratings for TrueFidelity, both depending on heart rate and independent of it. For the left coronary artery, no significant differences were observed between the two algorithms in terms of SNR and CNR. In addition, vascular contrast did not differ significantly between DLIR and ASiR-V. Artificial intelligence in the form of DLIR offers significant advantages over manually created ASiR-V, objectively and subjectively evaluated, depending on and independent of heart rate. However, this study also identified drawbacks regarding vessel contrast and SNR, suggesting potential for further improvements.