A Comparative Study of Deep Learning Models for Pancreatic CT Image Denoising

Low dose computed tomography is routinely used to minimize radiation exposure of patients, but it has inherent quantum noise that can obscure details in more complicated organs like the pancreas. This paper compares four modern deep learning architectures based on DnCNN, Attention UNet, REDNet and NAFNet for blocking low dose pancreatic CT images. Specifically, the models were assessed quantitatively in PSNR, SSIM, RMSE and EPI, as well as qualitatively through visual inspections of the reconstructed tissues. The results of these analyses indicated that REDNet and NAFNet performed better in general. Such results show the importance of balancing denoising strength with structural fidelity in medical imaging. Future work will focus on hybrid transformer CNN architectures and unsupervised denoising strategies that provide greater strength and generalization across a number of scanners and anatomical sites. Overall, this study represents a strong comparative baseline for low dose CT restoration and provides an avenue to improve pancreatic imaging diagnostic accuracy and image interpretation.