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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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Published on: November 8, 2012

Extending longitudinal field-of-view for cone-beam CT based on a novel surface-aware diffusion model.

Jueye Zhang1, Qijian Lu2, Yasi Zhang3

  • 1School of Physics, Peking University, Beijing, Beijing, 100190, China.

Physics in Medicine and Biology
|July 9, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel surface-aware DDIM-based RePaint diffusion framework to extend limited longitudinal cone-beam CT (CBCT) fields-of-view. The method accurately restores anatomical changes for improved patient setup and adaptive radiotherapy (ART) without additional imaging dose.

Keywords:
Adaptive RadiotherapyCBCTDiffusion ModelFOV Extension

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Area of Science:

  • Medical Imaging
  • Radiotherapy Physics
  • Artificial Intelligence in Medicine

Background:

  • Limited longitudinal field-of-view (FOV) in cone-beam CT (CBCT) hinders accurate patient positioning and adaptive radiotherapy (ART).
  • Extending CBCT anatomy is crucial for precise treatment delivery and inter-fractional change assessment.

Purpose of the Study:

  • To develop and evaluate a surface-aware DDIM-based RePaint diffusion framework for extending longitudinal CBCT anatomies.
  • To leverage prior planning CT (pCT) and optical surface images (OSI) to reconstruct inter-fractional changes in CBCT.

Main Methods:

  • A retrospective study involving 41 patients from two centers, utilizing pCT and CBCT data.
  • Development of a surface-aware DDIM-RePaint model to restore truncated CBCT volumes using pCT and OSI as conditioning inputs.
  • Quantitative evaluation using Mean Absolute Error (MAE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR), with ablation studies for OSI contribution.

Main Results:

  • The DDIM-RePaint framework significantly outperformed the current clinical approach (pCT and CBCT fusion) in image restoration accuracy (P < 0.001).
  • Achieved MAE of 25.28 HU and SSIM of 94.89 for Center A, and MAE of 35.20 HU and SSIM of 91.27 for Center B.
  • Demonstrated the significant contribution of OSI to restoration accuracy and reduced inference time from 45 minutes to 2.2 minutes through sampler optimization.

Conclusions:

  • The proposed surface-aware DDIM-RePaint framework offers an accurate and efficient solution for longitudinal CBCT FOV extension.
  • This approach has the potential to enhance patient setup and ART planning.
  • The method achieves these benefits without increasing imaging dose or requiring hardware upgrades.