Enhancing Image Quality of Low-Dose Dental CBCT Using Residual Encoder- Decoder Convolutional Neural Network (RED-CNN): A Comparative Study with Non-Local Means Denoising

Objective To evaluate the effectiveness of Residual Encoder-Decoder Convolutional Neural Network (RED-CNN) in reducing noise and improving image quality in low-dose dental Cone-Beam Computed Tomography (CBCT), and to compare its performance with the conventional Non-Local Means (NLM) denoising algorithm. Methods A female head RANDO phantom was scanned using a dental CBCT system with high-dose protocol (90 kV, 10 mA) to obtain ground-truth images and a low-dose protocol (70 kV, 1 mA) to generate noisy datasets. The RED-CNN model was trained and validated on paired low-dose and high-dose images, and tested on unseen data to assess generalization performance. Quantitative evaluation included Signal Difference-to-Noise Ratio (SDNR), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), computational performance, and dose-reduction assessment using the Dose Area Product (DAP). Results Both methods reduced noise and enhanced the quality of low-dose dental CBCT images; however, RED-CNN consistently outperformed NLM across all quantitative metrics. RED-CNN achieved SDNR > 25, PSNR > 30 dB, and SSIM of 0.73, demonstrating improved noise suppression and preservation of anatomical structures, and also provided faster GPU-based inference. Dose analysis showed that the low-dose protocol reduced exposure by 94% while maintaining acceptable diagnostic quality. Conclusions These findings showed that low-dose dental CBCT may achieve clinically acceptable image quality when enhanced using RED-CNN, as the method effectively suppresses noise while preserving essential anatomical detail.