ArticleAnnals of biomedical engineering2024
Clinical Super-Resolution Computed Tomography of Bone Microstructure: Application in Musculoskeletal and Dental Imaging.
Article in Annals of biomedical engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Dental image analysis and surgical decision support using modified generative adversarial networks.BMC oral health · 2026Article
- Enhancing diagnostic quality in dental bitewings using transformer and GAN-Based image restoration.Oral radiology · 2026Article
- The speed of sound in skull-mimicking digital phantoms depends on the microstructure.Physics in medicine and biology · 2025Article
- Influence of bone microstructure on ultrasound loss through skull-mimicking digital phantoms.Medical physics · 2025Article
- 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
- Super-resolution CBCT on a new generation flat panel imager of a C-arm gantry linear accelerator.Medical physics · 2025Article
- Post-Extraction Bone Changes in Molars Within Personalized Implant-Prosthetic Therapy as Evaluated with Fractal Analysis of CBCT.Journal of personalized medicine · 2025Article
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Authors and funding
13 authors.
Funding
Abstract
purposeClinical cone-beam computed tomography (CBCT) devices are limited to imaging features of half a millimeter in size and cannot quantify the tissue microstructure. We demonstrate a robust deep-learning method for enhancing clinical CT images, only requiring a limited set of easy-to-acquire training data.
methodsKnee tissue from five cadavers and six total knee replacement patients, and 14 teeth from eight patients were scanned using laboratory CT as training data for the developed super-resolution (SR) technique. The method was benchmarked against ex vivo test set, 52 osteochondral samples are imaged with clinical and laboratory CT. A quality assurance phantom was imaged with clinical CT to quantify the technical image quality. To visually assess the clinical image quality, musculoskeletal and maxillofacial CBCT studies were enhanced with SR and contrasted to interpolated images. A dental radiologist and surgeon reviewed the maxillofacial images.
resultsThe SR models predicted the bone morphological parameters on the ex vivo test set more accurately than conventional image processing. The phantom analysis confirmed higher spatial resolution on the SR images than interpolation, but image grayscales were modified. Musculoskeletal and maxillofacial CBCT images showed more details on SR than interpolation; however, artifacts were observed near the crown of the teeth. The readers assessed mediocre overall scores for both SR and interpolation. The source code and pretrained networks are publicly available.
conclusionModel training with laboratory modalities could push the resolution limit beyond state-of-the-art clinical musculoskeletal and dental CBCT. A larger maxillofacial training dataset is recommended for dental applications.
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