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.

Keywords

artificial intelligence - deep learning - image reconstruction - CCTA - DLIR - ASiR-V - TrueFidelity

Ethic Approval

The study protocol of this retrospective study was approved by the Local Ethics Committee (University of Witten/Herdecke, application number: 234/2021.

^‡ Equal contribution.

Publikationsverlauf

Eingereicht: 24. November 2025

Angenommen: 03. Dezember 2025

Artikel online veröffentlicht:
21. Dezember 2025

• References

• 1 Miller JM, Rochitte CE, Dewey M. et al. Diagnostic performance of coronary angiography by 64-row CT. N Engl J Med 2008; 359 (22) 2324-2336
• 2 Achenbach S, Marwan M, Ropers D. et al. Coronary computed tomography angiography with a consistent dose below 1 mSv using prospectively electrocardiogram-triggered high-pitch spiral acquisition. Eur Heart J 2010; 31 (03) 340-346
• 3 Clerc OF, Kaufmann BP, Possner M. et al. Long-term prognostic performance of low-dose coronary computed tomography angiography with prospective electrocardiogram triggering. Eur Radiol 2017; 27 (11) 4650-4660
• 4 Benz DC, Gräni C, Hirt Moch B. et al. Minimized radiation and contrast agent exposure for coronary computed tomography angiography: first clinical experience on a latest generation 256-slice scanner. Acad Radiol 2016; 23 (08) 1008-1014
• 5 Benz DC, Fuchs TA, Gräni C. et al. Head-to-head comparison of adaptive statistical and model-based iterative reconstruction algorithms for submillisievert coronary CT angiography. Eur Heart J Cardiovasc Imaging 2018; 19 (02) 193-198
• 6 Tatsugami F, Higaki T, Nakamura Y. et al. Deep learning-based image restoration algorithm for coronary CT angiography. Eur Radiol 2019; 29 (10) 5322-5329
• 7 Benz DC, Benetos G, Rampidis G. et al. Validation of deep-learning image reconstruction for coronary computed tomography angiography: impact on noise, image quality and diagnostic accuracy. J Cardiovasc Comput Tomogr 2020; 14 (05) 444-451
• 8 Arndt C, Güttler F, Heinrich A, Bürckenmeyer F, Diamantis I, Teichgräber U. Deep learning CT image reconstruction in clinical practice. Rofo 2021; 193 (03) 252-261
• 9 Layritz C, Schmid J, Achenbach S. et al. Accuracy of prospectively ECG-triggered very low-dose coronary dual-source CT angiography using iterative reconstruction for the detection of coronary artery stenosis: comparison with invasive catheterization. Eur Heart J Cardiovasc Imaging 2014; 15 (11) 1238-1245
• 10 Sawall S, Klein L, Amato C. et al. Iodine contrast-to-noise ratio improvement at unit dose and contrast media volume reduction in whole-body photon-counting CT. Eur J Radiol 2020; 126: 108909
• 11 Trattner S, Halliburton S, Thompson CM. et al. Cardiac-specific conversion factors to estimate radiation effective dose from dose-length product in computed tomography. JACC Cardiovasc Imaging 2018; 11 (01) 64-74
• 12 Noblé HJ, Mühlbauer N, Ehling J, Bansmann PM. The value of AI-based analysis of fractional flow reserve of volume computed tomographically detected coronary artery stenosis with regard to their hemodynamic relevance. Rofo 2024; 196 (12) 1253-1261