ArticleRadiology. Artificial intelligence2023
A Super-Resolution Diffusion Model for Recovering Bone Microstructure from CT Images.
Article in Radiology. Artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Demystifying machine learning approaches in digital bone imaging using microCT and HRpQCT.Bone reports · 2026Review
- MUSiK: An Open Source Simulation Library for 3D Multi-View Ultrasound.IEEE transactions on bio-medical engineering · 2026Article
- A Systematic Review of Diffusion Models for Medical Image-Based Diagnosis: Methods, Taxonomies, Clinical Integration, Explainability, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- Scaphoid Fracture Detection and Localization Using Denoising Diffusion Models.Diagnostics (Basel, Switzerland) · 2025Article
- Super-resolution of biomedical volumes with 2D supervision.Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Purpose: To use a diffusion-based deep learning model to recover bone microstructure from low-resolution images of the proximal femur, a common site of traumatic osteoporotic fractures. Materials and Methods: Training and testing data in this retrospective study consisted of high-resolution cadaveric micro-CT scans ( Results: Compared with popular deep learning baselines, the proposed model exhibited greater accuracy (mean ICC of proposed model, 0.92 vs ICC of next best method, 0.83) and lower bias (mean difference in means, 3.80% vs 10.00%, respectively) across the physiologic metrics. Two gradient-based image quality metrics strongly correlated with accuracy across structural and mechanical criteria ( Conclusion: The proposed method may enable accurate measurements of bone structure and strength with a radiation dose on par with current clinical imaging protocols, improving the viability of clinical CT for assessing bone health.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.