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. 2025 Nov 7:S1076-6332(25)00988-2.
doi: 10.1016/j.acra.2025.10.030. Online ahead of print.

Deep Learning Denoising Algorithm for Improved Assessment of Coronary Arteries in Transcatheter Aortic Valve Implantation CT Imaging

Ludovica R M Lanzafame  1 , Sebastian Steinmetz  2 , Tommaso D'Angelo  3 , Silvio Mazziotti  1 , Ibrahim Yel  4 , Vitali Koch  4 , Leon D Gruenewald  4 , Simon S Martin  4 , Jan-Erik Scholtz  4 , Katrin Eichler  4 , Leona S Alizadeh  4 , Thomas J Vogl  5 , Marc A Brockmann  2 , Chulkyun Ahn  6 , Jong H Kim  7 , Ahmed E Othman  2 , Christian Booz  4
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Free article

Deep Learning Denoising Algorithm for Improved Assessment of Coronary Arteries in Transcatheter Aortic Valve Implantation CT Imaging

Ludovica R M Lanzafame et al. Acad Radiol. .
Free article

Abstract

Rationale and objectives: To assess the impact of a deep learning-based noise reduction (DLD) technique on image quality and diagnostic accuracy for the evaluation of coronary arteries in transcatheter aortic valve implantation (TAVI) CT imaging.

Materials and methods: Two hundred patients with severe aortic stenosis who underwent CT scans for pre-TAVI planning between October 2022 and April 2024 were retrospectively enrolled. Conventional images were reconstructed and denoised images were generated using dedicated software. Objective image quality was evaluated by measuring the mean Hounsfield unit (HU) and standard deviation (SD) in regions of interest within the aortic root, coronary arteries, and subcutaneous fat to calculate signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). For subjective assessment, two readers used a 5-point Likert scoring system to evaluate sharpness, noise, contrast and overall image quality. The diagnostic performance of both datasets was assessed using invasive coronary angiography as reference standard.

Results: Denoised reconstructions showed significantly higher SNR (37.5±12.8 vs.12.3±4.1) and CNR (45.3±15.4 vs. 14.7±4.4), and lower noise (16.9±7.9 vs. 47.9±11.6 HU) (all p<0.001). Subjective assessment demonstrated that denoised images received the highest score for sharpness, noise, contrast and overall image quality (all p<0.001). For the evaluation of diagnostic accuracy, a total of 800 vessels and 1787 segments were analyzed. The per-segment diagnostic performance of the DLD for detection of CAD revealed an AUC of 90% (95% CI: 88.5-91.3), with accuracy of 93.9% (95% CI: 92.7-95), 85.7% (95% CI: 78.7-90.4) sensitivity and 94.7% (95% CI: 93.5-95.7) specificity, in the absence of a statistically significant difference compared with the evaluation performed on standard images (p=0.056).

Conclusion: The DLD substantially improves image quality without affecting diagnostic accuracy for the evaluation of coronary arteries in patients undergoing pre-TAVI CT scans.

Keywords: Computed tomography angiography; Coronary artery disease; Deep learning; Noise.

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Conflict of interest statement

Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ibrahim Yel reports a relationship with Siemens Healthineers that includes: speaking and lecture fees. Christian Booz reports a relationship with Siemens Healthineers that includes: speaking and lecture fees. Tommaso D’Angelo reports a relationship with Philips that includes: speaking and lecture fees. Tommaso D’Angelo reports a relationship with Bracco that includes: speaking and lecture fees. Chulkyun Ahn and Jong H. Kim are employees of ClariPi. The other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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