# Synthetic CT image generation from CBCT: A Systematic Review

arXiv: https://arxiv.org/abs/2501.13972 · HTML: https://mlsanity.com/paper/2501.13972/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 59 references that are in the map.
- First posted on arXiv 22 Jan 2025.

## Builds on (most cited first, 4 of 4)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Deep Learning in Medical Image Registration: A Survey](https://mlsanity.com/paper/1903.02026/) (2019)
- [Deep Learning in Medical Image Registration: A Review](https://mlsanity.com/paper/1912.12318/) (2019)

## Built on by (most cited first, 8 of 8)

- [Conditional Diffusion Models for CT Image Synthesis from CBCT: A Systematic Review](https://mlsanity.com/paper/2509.17790/) (2025)
- [Equivariant Conditional Diffusion Model for Head and Neck CT Image Synthesis from CBCT](https://mlsanity.com/paper/2509.21913/) (2025)
- [A Privacy-Preserving Federated Learning Framework for Generalizable CBCT to Synthetic CT Translation in Head and Neck](https://mlsanity.com/paper/2506.08654/) (2025)
- [Enhancing Synthetic CT from CBCT via Multimodal Fusion: A Study on the Impact of CBCT Quality and Alignment](https://mlsanity.com/paper/2506.08716/) (2025)
- [Enhancing Synthetic CT from CBCT via Multimodal Fusion and End-To-End Registration](https://mlsanity.com/paper/2507.06067/) (2025)
- [EPC-3D-Diff: Equivariant Physics Consistent Conditional 3D Latent Diffusion for CBCT to CT Synthesis](https://mlsanity.com/paper/2605.20470/) (2026)
- [Toward CT-Equivalent Image Quality in Low-Dose Radiotherapy Planning: Conditional Diffusion-Based CBCT-to-CT Synthesis and the Impact of CBCT Input Representation](https://mlsanity.com/paper/2608.08919/) (2026)
- [When Misalignment Becomes Supervision: Structured Label Noise in Supervised Synthetic CT Generation](https://mlsanity.com/paper/2609.29387/) (2026)

## Abstract (arXiv)

The generation of synthetic CT (sCT) images from cone-beam CT (CBCT) data using deep learning methodologies represents a significant advancement in radiation oncology. This systematic review, following PRISMA guidelines and using the PICO model, comprehensively evaluates the literature from 2014 to 2024 on the generation of sCT images for radiation therapy planning in oncology. A total of 35 relevant studies were identified and analyzed, revealing the prevalence of deep learning approaches in the generation of sCT. This review comprehensively covers synthetic CT generation based on CBCT and proton-based studies. Some of the commonly employed architectures explored are convolutional neural networks (CNNs), generative adversarial networks (GANs), transformers, and diffusion models. Evaluation metrics including mean absolute error (MAE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) consistently demonstrate the comparability of sCT images with gold-standard planning CTs (pCT), indicating their potential to improve treatment precision and patient outcomes. Challenges such as field-of-view (FOV) disparities and integration into clinical workflows are discussed, along with recommendations for future research and standardization efforts. In general, the findings underscore the promising role of sCT-based approaches in personalized treatment planning and adaptive radiation therapy, with potential implications for improved oncology treatment delivery and patient care.