Flow Matching for Conditional MRI-CT and CBCT-CT Image Synthesis
This work addresses the need for MRI-only and CBCT-based adaptive radiotherapy to improve treatment precision and reduce radiation exposure, though it is incremental as it builds on existing Flow Matching methods.
The paper tackled the problem of generating synthetic CT images from MRI or CBCT for radiotherapy by using a 3D Flow Matching framework, achieving accurate reconstruction of global anatomical structures but with limited fine detail preservation due to low training resolution.
Generating synthetic CT (sCT) from MRI or CBCT plays a crucial role in enabling MRI-only and CBCT-based adaptive radiotherapy, improving treatment precision while reducing patient radiation exposure. To address this task, we adopt a fully 3D Flow Matching (FM) framework, motivated by recent work demonstrating FM's efficiency in producing high-quality images. In our approach, a Gaussian noise volume is transformed into an sCT image by integrating a learned FM velocity field, conditioned on features extracted from the input MRI or CBCT using a lightweight 3D encoder. We evaluated the method on the SynthRAD2025 Challenge benchmark, training separate models for MRI $\rightarrow$ sCT and CBCT $\rightarrow$ sCT across three anatomical regions: abdomen, head and neck, and thorax. Validation and testing were performed through the challenge submission system. The results indicate that the method accurately reconstructs global anatomical structures; however, preservation of fine details was limited, primarily due to the relatively low training resolution imposed by memory and runtime constraints. Future work will explore patch-based training and latent-space flow models to improve resolution and local structural fidelity.