mlsanityData to 7 Oct 2026

Energy-Guided Diffusion Model for CBCT-to-CT Synthesis

arXiv 2308.03354 ↗First posted 7 Aug 2023eess.IV cs.CV physics.med-phcited by 4

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Cone Beam CT (CBCT) plays a crucial role in Adaptive Radiation Therapy (ART) by accurately providing radiation treatment when organ anatomy changes occur. However, CBCT images suffer from scatter noise and artifacts, making relying solely on CBCT for precise dose calculation and accurate tissue localization challenging. Therefore, there is a need to improve CBCT image quality and Hounsfield Unit (HU) accuracy while preserving anatomical structures. To enhance the role and application value of CBCT in ART, we propose an energy-guided diffusion model (EGDiff) and conduct experiments on a chest tumor dataset to generate synthetic CT (sCT) from CBCT. The experimental results demonstrate impressive performance with an average absolute error of 26.87±6.14 HU, a structural similarity index measurement of 0.850±0.03, a peak signal-to-noise ratio of the sCT of 19.83±1.39 dB, and a normalized cross-correlation of the sCT of 0.874±0.04. These results indicate that our method outperforms state-of-the-art unsupervised synthesis methods in accuracy and visual quality, producing superior sCT images.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 29 of its 66 references that are in the map. First posted on arXiv 7 Aug 2023.

Builds on

29 of 66 references.

Closest ancestors · 5

Same direction, newest first

  1. 2022Brain Imaging Generation with Latent Diffusion Models15 Sep
  2. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstruction12 Jul
  3. 2022Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models7 Jun
  4. 2021Diffusion Models for Implicit Image Segmentation Ensembles6 Dec
  5. 2021Score-based diffusion models for accelerated MRI8 Oct

Other references · 24

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  4. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  5. 2022Diffusion Models in Vision: A Surveycited by 1,008
  6. 2018Computational Optimal Transportcited by 765
  7. 2020Contrastive Learning for Unpaired Image-to-Image Translationcited by 637
  8. 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 340
  9. 2017DualGAN: Unsupervised Dual Learning for Image-to-Image Translationcited by 310
  10. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 284
  11. 2017Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)cited by 224
  12. 2017Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Losscited by 161
12 more
  1. 2018MedGAN: Medical Image Translation using GANscited by 152
  2. 2018Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networkscited by 131
  3. 2022Conversion Between CT and MRI Images Using Diffusion and Score-Matching Modelscited by 69
  4. 2022MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusioncited by 66
  5. 2021Instance-wise Hard Negative Example Generation for Contrastive Learning in Unpaired Image-to-Image Translationcited by 45
  6. 2018Cross-modality image synthesis from unpaired data using CycleGAN: Effects of gradient consistency loss and training data sizecited by 42
  7. 2022QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translationcited by 39
  8. 2019MRI Super-Resolution with Ensemble Learning and Complementary Priorscited by 27
  9. 2022Modulated Contrast for Versatile Image Synthesiscited by 23
  10. 2017Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)cited by 18
  11. 2023Zero-shot-Learning Cross-Modality Data Translation Through Mutual Information Guided Stochastic Diffusioncited by 6
  12. 2022Brain PET Synthesis from MRI Using Joint Probability Distribution of Diffusion Model at Ultrahigh Fieldscited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2025Conditional Diffusion Models for CT Image Synthesis from CBCT: A Systematic Reviewcited by 3
  2. 2024HC3L-Diff: Hybrid conditional latent diffusion with high frequency enhancement for CBCT-to-CT synthesiscited by 2
  3. 2025Segmentation-Guided CT Synthesis with Pixel-Wise Conformal Uncertainty Boundscited by 1
  4. 2026EPC-3D-Diff: Equivariant Physics Consistent Conditional 3D Latent Diffusion for CBCT to CT Synthesiscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Improving Cone-Beam CT Image Quality with Knowledge Distillation-Enhanced Diffusion Model in Imbalanced Data Settings
  2. 2026Toward CT-Equivalent Image Quality in Low-Dose Radiotherapy Planning: Conditional Diffusion-Based CBCT-to-CT Synthesis and the Impact of CBCT Input Representation
  3. 2025Equivariant Conditional Diffusion Model for Head and Neck CT Image Synthesis from CBCT
  4. 2026Anatomy-preserving unpaired cone-beam CT refinement for image-guided radiotherapy using pseudo-label guided diffusion
  5. 2025Limited-Angle CBCT Reconstruction via Geometry-Integrated Cycle-domain Denoising Diffusion Probabilistic Models
  6. 2024Prior Frequency Guided Diffusion Model for Limited Angle (LA)-CBCT Reconstruction
  7. 2025Synthetic CT image generation from CBCT: A Systematic Review
  8. 2026Cone-Beam CT Image Quality Enhancement Using A Latent Diffusion Model Trained with Simulated CBCT Artifacts