Towards safer imaging: A comparative study of deep learning-based denoising and iterative reconstruction in intraindividual low-dose CT scans using an in-vivo large animal model

Purpose: Computed tomography (CT) scans are a significant source of medically induced radiation exposure. Novel deep learning -based denoising (DLD) algorithms have been shown to enable diagnostic image quality at lower radiation doses than iterative reconstruction (IR) methods. However, most comparative studies employ low -dose simulations due to ethical constraints. We used real intraindividual animal scans to investigate the dose -reduction capabilities of a DLD algorithm in comparison to IR. Materials and methods: Fourteen veterinarian -sedated alive pigs underwent 2 CT scans on the same 3rd generation dual -source scanner with two months between each scan. Four additional scans ensued each time, with mAs reduced to 50 %, 25 %, 10 %, and 5 %. All scans were reconstructed ADMIRE levels 2 (IR2) and a novel DLD algorithm, resulting in 280 datasets. Objective image quality (CT numbers stability, noise, and contrast -to -noise ratio) was measured via consistent regions of interest. Three radiologists independently rated all possible dataset combinations per time point for subjective image quality (-1 = inferior, 0 = equal, 1 = superior). The points were averaged for a semiquantitative score, and inter -rater agreement was measured using Spearman's correlation coefficient and adequately corrected mixedeffects modeling analyzed objective and subjective image quality. Results: Neither dose -reduction nor reconstruction method negatively impacted CT number stability (p > 0.999). In objective image quality assessment, the lowest radiation dose achievable by DLD when comparing noise (p = 0.544) and CNR (p = 0.115) to 100 % IR2 was 25 %. Overall, inter -rater agreement of the subjective image quality ratings was strong (r >= 0.69, mean 0.93 +/- 0.05, 95 % CI 0.92-0.94; each p < 0.001), and subjective assessments corroborated that DLD at 25 % radiation dose was comparable to 100 % IR2 in image quality, sharpness, and contrast (p >= 0.281). Conclusions: The DLD algorithm can achieve image quality comparable to the standard IR method but with a significant dose reduction of up to 75%. This suggests a promising avenue for lowering patient radiation exposure without sacrificing diagnostic quality.