Conditional Diffusion Models for CT Image Synthesis from CBCT: A Systematic Review
Abstract
Background: Cone Beam Computed Tomography (CBCT) is widely used in image-guided radiotherapy because it provides on-board volumetric imaging at relatively low doses, but its clinical utility for synthetic CT (sCT) generation remains limited by noise, scatter, artifacts, and reduced Hounsfield Unit (HU) fidelity. Conditional diffusion models (CDMs) have recently emerged as a promising alternative to earlier deep learning approaches because their iterative denoising process may better preserve anatomical structure and model uncertainty.
Objective: This systematic review evaluates the use of conditional diffusion models for CBCT-to-CT synthesis, with particular attention to architectural strategies, reported quantitative outcomes, and potential clinical relevance. A systematic search was conducted in PubMed, Web of Science, Scopus, IEEE Xplore, and Google Scholar for studies published between 2013 and 2024. Eleven studies met the eligibility criteria and were analyzed to address three questions: (1) Which conditional diffusion strategies have been used? (2) What outcomes have been reported? and (3) What clinical implications have been discussed?
Results: Across the included studies, CDMs frequently showed promising image quality performance, especially when incorporating anatomical priors, spatial-frequency guidance, hierarchical refinement, or latent representations. However, the evidence base remains small and highly heterogeneous with respect to anatomy, dimensionality,…
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