Deep learning for dentomaxillofacial cone-beam computed tomography image quality enhancement: A pilot study.

Purpose: This study was conducted to develop and evaluate a deep learning-based super-resolution approach for enhancing the quality of cone-beam computed tomography (CBCT) images in dentomaxillofacial imaging.

Materials and methods: A deep learning-based super-resolution method using the MIRNet-v2 model was developed to enhance CBCT image quality. The study used a dataset comprising 6,961 anonymized axial slices from 15 CBCT scans. High-resolution images served as ground truth, while low-resolution versions were created through artificial degradation, including downscaling, blurring, and noise addition. The model was evaluated using a 5-fold cross-validation strategy, employing peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) as metrics. Qualitative assessments conducted by 2 experienced radiologists involved criteria such as noise, sharpness, spatial resolution, and diagnostic quality, scored using a CBCT evaluation chart.

Results: The model significantly improved degraded CBCT images across all evaluation metrics. Enhanced images demonstrated mean PSNR values exceeding 35 dB and SSIM values over 0.85, with the highest performance achieved for blurred images (PSNR: 43.86±1.61, SSIM: 0.98±0.01). Subjective assessments indicated improvements in diagnostic quality, noise reduction, and spatial resolution, with outputs comparable to the original images in several degradation scenarios. Interobserver reliability was fair (Cohen kappa: 0.335). Notable improvements were observed for noise and artifact reduction in specific degradation groups, suggesting improved diagnostic utility.

Conclusion: Deep learning-based super-resolution demonstrates considerable potential for enhancing CBCT image quality, especially in scenarios involving blur and downscaling. These results suggest possible applications in low-dose imaging protocols and improved clinical decision-making.