Deep learning-based post-hoc noise reduction improves quarter-radiation-dose coronary CT angiography

To evaluate the impact of deep learning-based post-hoc noise reduction (DLNR) on image quality, coronary artery disease reporting and data system (CAD-RADS) assessment, and diagnostic performance in quarter-dose versus full-dose coronary CT angiography (CCTA) on external datasets.

We retrospectively reviewed 221 patients who underwent retrospective electrocardiogram-gated CCTA in 2022-2023. Using dose modulation, either mid-diastole or end-systole was scanned at full dose depending on heart rates, and the other phase at quarter dose. Only patients with motion-free coronaries in both phases were included. Images were acquired using iterative reconstruction, and a residual dense network trained on external datasets denoised the quarter-dose images. Image quality was assessed by comparing noise levels using Tukey's test. Two radiologists independently assessed CAD-RADS, with agreement to full-dose images evaluated by Cohen's kappa. Diagnostic performance for significant stenosis referencing full-dose images was compared between quarter-dose and denoised images by the area under the receiver operating characteristic curve (AUC) using the DeLong test.

Among 40 cases (age, 71 ± 7 years; 24 males), DLNR reduced noise from 37 to 18 HU (P < 0.001) in quarter-dose CCTA (full-dose images: 22 HU), and improved CAD-RADS agreement from moderate (0.60 [95 % CI: 0.41-0.78]) to excellent (0.82 [95 % CI: 0.66-0.94]). Denoised images demonstrated a superior AUC (0.97 [95 % CI: 0.95-1.00]) for diagnosing significant stenosis compared with original quarter-dose images (0.93 [95 % CI: 0.89-0.98]; P = 0.032).

DLNR for quarter-dose CCTA significantly improved image quality, CAD-RADS agreement, and diagnostic performance for detecting significant stenosis referencing full-dose images.