ArticleImaging science in dentistry2026
Deep learning-driven super-resolution for cone-beam computed tomography: An
Article in Imaging science in dentistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Transformer and generative models in dental and maxillofacial imaging: Narrative review of clinical evidence and future directions.Imaging science in dentistry · 2026Review
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Authors and funding
7 authors.
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Abstract
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, Conclusion: DL-based SR models can enhance simulated CBCT images, with LTE demonstrating superior perceptual sharpness and crack visibility.
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