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Related Experiment Video

Updated: Jan 11, 2026

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Few-shot CBCT-based synthetic CT generation with denoising diffusion probabilistic model.

Ping Lin Yeap1, Xin Du2, Meng Zhou3

  • 1Department of Oncology, University of Cambridge, Cambridge, UK.

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|November 13, 2025
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Summary
This summary is machine-generated.

This study introduces a novel denoising diffusion probabilistic model for generating synthetic CT (sCT) images from cone-beam CT (CBCT) with minimal training data. The method significantly improves image quality and aids adaptive radiotherapy by enabling accurate dose evaluation.

Keywords:
adaptive radiotherapycone‐beam CTdiffusion modelsynthetic CT generation

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Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Artificial Intelligence

Background:

  • Adaptive radiotherapy requires frequent re-optimization of treatment plans based on anatomical changes observed via in-room imaging.
  • In-room cone-beam CT (CBCT) provides daily monitoring but suffers from poor image quality and inaccurate Hounsfield Unit (HU) values, hindering accurate dose evaluation.
  • Existing CBCT-to-CT generation models often require extensive datasets for training, posing a challenge for clinical implementation.

Purpose of the Study:

  • To develop a data-efficient method for generating high-fidelity synthetic CT (sCT) images from CBCT using a denoising diffusion probabilistic model (DDPM).
  • To enable few-shot learning for sCT generation, reducing the dependency on large training datasets.

Main Methods:

  • A DDPM was trained on rigidly registered CT-CBCT image pairs, eliminating the need for complex deformable registrations.
  • A modified sampling process utilizing channel- and noise-conditioning was employed, exploiting latent space convergence between CBCT and planning CT (pCT) representations.
  • The model was trained on a limited dataset (25 head-and-neck cancer patients) and validated/tested on separate patient cohorts, demonstrating few-shot learning capabilities.

Main Results:

  • The generated sCTs significantly outperformed CBCTs in head-and-neck cancer patients, reducing masked mean absolute error (MAE) from 131 HU to 49 HU.
  • Image quality metrics improved substantially, with peak signal-to-noise ratio (PSNR) increasing from 20.0 dB to 22.9 dB and normalized cross-correlation (NCC) from 0.93 to 0.96.
  • The method demonstrated generalizability across different anatomical sites (pelvis) and datasets, achieving comparable performance after retraining and showing clinician preference for sCTs over pCTs for fractional evaluation.

Conclusions:

  • The proposed DDPM offers a practical, data-efficient, and site-robust solution for generating high-fidelity sCTs from CBCT images.
  • This approach facilitates accurate CBCT-based dose evaluation and plan adaptation within adaptive radiotherapy workflows.
  • The method's ability to perform well with limited data makes it a valuable tool for clinical settings.