Deep learning-driven super-resolution for cone-beam computed tomography: An ex vivo proof-of-concept study using artificially degraded micro-computed tomography data

Purpose: This ex vivo proof-of-concept study aimed to develop deep learning (DL)-based super-resolution (SR) models to enhance simulated cone-beam computed tomography (CBCT) images.Materials and Methods: Micro-computed tomography data from 51 extracted teeth were artificially degraded to simulate CBCT images.Three DL models, super-resolution convolutional neural network (SRCNN), local texture estimator (LTE), and Swin Transformer for image restoration (SwinIR), were compared with bicubic interpolation.Image quality was assessed using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), learned perceptual image patch similarity (LPIPS), and deep image structure and texture similarity (DISTS).Three dentists evaluated sharpness and noise using a 5-point Likert scale.Eight observers assessed crack visibility in 47 images for LTE and bicubic interpolation using a 5-point Likert scale; scores were binarized using high and low thresholds.Results: All models significantly outperformed bicubic interpolation on objective metrics.SwinIR showed the highest PSNR (30.36±2.66),whereas SRCNN achieved the highest SSIM (0.889±0.073).LTE achieved the best LPIPS (0.253±0.101) and DISTS (0.203±0.049).Subjectively, LTE received the highest sharpness ratings (mean.3.79±0.47),whereas bicubic interpolation received the highest noise ratings (3.97±1.43).LTE significantly improved crack visibility (odds ratio = 1.326,P = 0.006 for the low-threshold analysis; odds ratio = 1.310,P = 0.010 for the highthreshold analysis), with a higher pooled area under the curve (0.81 vs. 0.76, P = 0.063).Conclusion: DL-based SR models can enhance simulated CBCT images, with LTE demonstrating superior perceptual sharpness and crack visibility.