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CL-GAN: A progressive curriculum learning approach for bone CT super-resolution.

Yousif Al-Khoury1, Camille P Figueiredo2, Josephine Therkildsen3

  • 1Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada; McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.

Computers in Biology and Medicine
|November 23, 2025
PubMed
Summary

We developed a new method using curriculum learning generative adversarial network (CL-GAN) to improve cone-beam computed tomography (CBCT) imaging for rheumatoid arthritis (RA) assessment. This technique enhances image resolution, providing more detailed bone analysis for RA patients.

Keywords:
Cone beam CTCurriculum learningMusculoskeletal imagingSuper-resolution

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Rheumatology

Background:

  • Cone-beam computed tomography (CBCT) offers low-dose, large-coverage imaging but lacks resolution for detailed rheumatoid arthritis (RA) bone assessment.
  • Standard super-resolution methods struggle with CBCT due to resolution, noise, and artifact differences compared to high-resolution peripheral quantitative CT (HR-pQCT).

Purpose of the Study:

  • To develop a stable and effective super-resolution algorithm for CBCT images specifically for RA analysis.
  • To enhance CBCT image quality to approach the resolution of HR-pQCT for improved trabecular bone assessment in RA.

Main Methods:

  • Proposed a curriculum learning generative adversarial network (CL-GAN) to train a Cycle-Consistent GAN for CBCT super-resolution.
  • Implemented a four-stage training process, starting with synthetic data and progressively incorporating real, unpaired CBCT and HR-pQCT images from healthy and RA-affected joints.
  • Evaluated performance using image quality metrics, trabecular bone morphometry, and blinded expert review.

Main Results:

  • Progressive training significantly improved image quality and trabecular bone metric accuracy (p<0.001).
  • Blinded reviewers could detect RA erosions effectively, with enhanced CBCT images being difficult to distinguish from HR-pQCT.
  • Ablation studies confirmed the necessity of curriculum stages for optimal performance and stable domain adaptation.

Conclusions:

  • Curriculum learning enables stable and effective training of super-resolution algorithms for CBCT in RA.
  • The CL-GAN framework enhances CBCT image quality and structural interpretability, increasing its utility for RA analysis and research.
  • This approach supports the use of CBCT for more detailed bone assessment in rheumatoid arthritis.