ArticleScientific reports2021
QCBCT-NET for direct measurement of bone mineral density from quantitative cone-beam CT: a human skull phantom study.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 31 citations in OpenAlex.
- Current evidence of generative artificial intelligence specifically developed for dental and maxillofacial radiology: a systematic review.Dento maxillo facial radiology · 2026Pooled it
- Generative adversarial networks in dental imaging: a systematic review.Oral radiology · 2024Pooled it
- Age-related structural changes of the facial skeleton: insights from sutural ossification and CT-based analysis.Oral radiology · 2026Article
- Feasibility of multisource CBCT for improving the predictability of dental implant primary stability compared to conventional CBCT.Scientific reports · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Artificial intelligence in osteoporosis assessment using CT imaging: a scoping review.Frontiers in medicine · 2026Review
- Relationship Between Facial Types and Alveolar Crest Cortical Bone Thickness and CT Values Determined by Multidetector Computed Tomography.Dentistry journal · 2025Article
- Primary Stability of Dental Implants in Human Jawbones: Experiments & FE Analyses.Clinical oral implants research · 2025Article
- Retrospective Analysis of Dental Implant Stability in Relation to Mandibular Bone Density and Cortical Thickness in Osteopenic and Osteoporotic Patients.Journal of clinical medicine · 2025Article
- Bone Densitometric Analysis using Cone Beam Computed Tomography (Cone Beam CT) and Computed Tomography (CT): Establishing the Correlation between Predicted and Actual Values.Advanced biomedical research · 2025Article
- Deep learning to assess bone quality from panoramic radiographs: the feasibility of clinical application through comparison with an implant surgeon and cone-beam computed tomography.Journal of periodontal & implant science · 2024Article
- Improving the accuracy of bone mineral density using a multisource CBCT.Scientific reports · 2024Article
- Comparison of 2D, 2.5D, and 3D segmentation networks for maxillary sinuses and lesions in CBCT images.BMC oral health · 2023Article
- Ceph-Net: automatic detection of cephalometric landmarks on scanned lateral cephalograms from children and adolescents using an attention-based stacked regression network.BMC oral health · 2023Article
- Validation of bone mineral density measurement using quantitative CBCT image based on deep learning.Scientific reports · 2023Article
- SinusC-Net for automatic classification of surgical plans for maxillary sinus augmentation using a 3D distance-guided network.Scientific reports · 2023Article
- The Application of Deep Learning on CBCT in Dentistry.Diagnostics (Basel, Switzerland) · 2023Review
- Volumetric computed tomography with carbon nanotube X-ray source array for improved image quality and accuracy.Communications engineering · 2023Article
- A Comparative Study on the Voxel Values in Alveolar Bones Acquired by MDCT and Newly Developed Dental Dual-Energy CBCT.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this study was to directly and quantitatively measure BMD from Cone-beam CT (CBCT) images by enhancing the linearity and uniformity of the bone intensities based on a hybrid deep-learning model (QCBCT-NET) of combining the generative adversarial network (Cycle-GAN) and U-Net, and to compare the bone images enhanced by the QCBCT-NET with those by Cycle-GAN and U-Net. We used two phantoms of human skulls encased in acrylic, one for the training and validation datasets, and the other for the test dataset. We proposed the QCBCT-NET consisting of Cycle-GAN with residual blocks and a multi-channel U-Net using paired training data of quantitative CT (QCT) and CBCT images. The BMD images produced by QCBCT-NET significantly outperformed the images produced by the Cycle-GAN or the U-Net in mean absolute difference (MAD), peak signal to noise ratio (PSNR), normalized cross-correlation (NCC), structural similarity (SSIM), and linearity when compared to the original QCT image. The QCBCT-NET improved the contrast of the bone images by reflecting the original BMD distribution of the QCT image locally using the Cycle-GAN, and also spatial uniformity of the bone images by globally suppressing image artifacts and noise using the two-channel U-Net. The QCBCT-NET substantially enhanced the linearity, uniformity, and contrast as well as the anatomical and quantitative accuracy of the bone images, and demonstrated more accuracy than the Cycle-GAN and the U-Net for quantitatively measuring BMD in CBCT.
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