ArticleImaging science in dentistry2020
Very deep super-resolution for efficient cone-beam computed tomographic image restoration.
Article in Imaging science in dentistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Transformer and generative models in dental and maxillofacial imaging: Narrative review of clinical evidence and future directions.Imaging science in dentistry · 2026Review
- Quantitative and Qualitative Evaluation of a Confidence-Aware Transformer-Based Super-Resolution Framework for Panoramic Radiographs.International dental journal · 2026Article
- Enhancing diagnostic quality in dental bitewings using transformer and GAN-Based image restoration.Oral radiology · 2026Article
- Super-resolution of periapical and bitewing digital radiographs using convolutional neural network.BMC medical imaging · 2026Article
- Deep learning-driven super-resolution for cone-beam computed tomography: AnImaging science in dentistry · 2026Article
- Impact of Generative AI-Enhanced Low-Dose Cone-Beam Computed Tomography on Diagnosis and Treatment Planning for Impacted Mandibular Third Molars.International dental journal · 2026Article
- Deep learning for dentomaxillofacial cone-beam computed tomography image quality enhancement: A pilot study.Imaging science in dentistry · 2025Article
- Can super resolution via deep learning improve classification accuracy in dental radiography?Dento maxillo facial radiology · 2025Article
- Improving resolution of panoramic radiographs: super-resolution concept.Dento maxillo facial radiology · 2024Article
- Deep Learning Super-Resolution Technique Based on Magnetic Resonance Imaging for Application of Image-Guided Diagnosis and Surgery of Trigeminal Neuralgia.Life (Basel, Switzerland) · 2024Article
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4 authors.
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Abstract
purposeAs cone-beam computed tomography (CBCT) has become the most widely used 3-dimensional (3D) imaging modality in the dental field, storage space and costs for large-capacity data have become an important issue. Therefore, if 3D data can be stored at a clinically acceptable compression rate, the burden in terms of storage space and cost can be reduced and data can be managed more efficiently. In this study, a deep learning network for super-resolution was tested to restore compressed virtual CBCT images. MATERIALS AND
methodsVirtual CBCT image data were created with a publicly available online dataset (CQ500) of multidetector computed tomography images using CBCT reconstruction software (TIGRE). A very deep super-resolution (VDSR) network was trained to restore high-resolution virtual CBCT images from the low-resolution virtual CBCT images.
resultsThe images reconstructed by VDSR showed better image quality than bicubic interpolation in restored images at various scale ratios. The highest scale ratio with clinically acceptable reconstruction accuracy using VDSR was 2.1.
conclusionVDSR showed promising restoration accuracy in this study. In the future, it will be necessary to experiment with new deep learning algorithms and large-scale data for clinical application of this technology.
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