Key result
CNN-based denoising cuts low-dose CCTA image noise ~40% in obese patients.
Why the study?
The added value of post-hoc CNN-based denoising for low-radiation coronary CT angiography in predominantly obese patients was not established.
Does post-hoc convolutional neural network (CNN)-based denoising improve image quality in predominantly obese patients undergoing low-dose coronary CT angiography?
Observational
Does post-hoc convolutional neural network (CNN)-based denoising improve image quality in predominantly obese patients undergoing low-dose coronary CT angiography?
Mean Difference: -13.9
Absolute Event Rate: 21% vs 35.1%
p-value: p=<0.001
CNN-based denoising significantly improves objective and subjective image quality in low-dose CCTA, independent of BMI, in predominantly obese patients.
May enable low-dose CCTA in obese patients; leaves open effects on diagnostic accuracy and outcomes.
OBJECTIVE: To determine the added value of post-hoc convolutional neural network (CNN)-based denoising for low-radiation coronary CT angiography (CCTA). METHODS: In this IRB-approved, post-hoc study, consecutive patients underwent clinically indicated CCTA on a third-generation dual-source CT scanner using a fast-pitch protocol and fully patient-tailored contrast injection. Images were reconstructed with a vascular kernel (Bv40) using iterative reconstruction (ADMIRE 4), with and without CNN-based denoising. Two readers assessed signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), attenuation, and subjective image quality across coronary segments using a 4-point scale. Linear mixed-effects models evaluated the relationship between body-mass index (BMI) and denoising efficiency. RESULTS: ), were analyzed. The dose-length product was 77.15 [50.10-133.38] mGy·cm. CNN-denoising reduced image noise (35.1 ± 7.8 vs 21.0 ± 4.7 HU, p < 0.001) and improved SNR (12.0 ± 3.3 vs 22.0 ± 5.7, p < 0.001) and CNR (11.0 ± 3.0 vs 19.0 ± 5.2, p < 0.001), with stable attenuation (all p > 0.1). Subjective quality improved (2 [2,3] vs 3 [2,3], p < 0.001). Poor-quality vessels decreased from 23/156 (14.7%) to 14/156 (9.0%), and good-to-excellent quality increased from 82/156 (52.6%) to 117/156 (75.0%). Mixed-effects modeling showed 31% noise reduction (-13.9 HU, p < 0.001) and SNR (+8.5, p < 0.001) and CNR (+7.4, p < 0.001) gains, irrespective of BMI (p > 0.46). CONCLUSION: CNN-based denoising improved objective and subjective image quality in low-dose CCTA independently of BMI in a predominantly obese cohort.
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Osoria-Velasquez et al. (2026) conducted an observational in Predominantly obese patients undergoing coronary CT angiography. Convolutional neural network (CNN)-based denoising vs. Iterative reconstruction without CNN-based denoising was evaluated on Image noise (HU) (MD -13.9 HU, p=<0.001). CNN-based denoising reduced image noise (21.0 vs 35.1 HU, P<0.001) and improved signal-to-noise and contrast-to-noise ratios in low-dose CCTA for predominantly obese patients.
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