Deep-learning-based noise reduction for ultra-low-dose dental CBCT images using paired datasets from different domains

This study presents a novel deep learning approach for noise reduction in ultra-low dose (ULD) dental cone beam computed tomography (CBCT). The method pre-trains a U-Net model on phantom datasets and fine-tunes it with simulated datasets, balancing noise reduction and anatomical structure preservation. Qualitative evaluation shows that the proposed method maintains soft tissue details while reducing noise effectively. Quantitative analysis demonstrates consistent improvements in the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) across phantom and simulated datasets compared to ULD images. This approach shows promise for clinical implementation of deep-learning-based noise reduction, potentially enabling diagnostic-quality CBCT scans at extremely low radiation exposure.