Flow Matching for Conditional MRI-CT and CBCT-CT Image Synthesis

• URL: http://arxiv.org/abs/2510.04823v1
• Date: Mon, 06 Oct 2025 14:07:03 GMT
• Title: Flow Matching for Conditional MRI-CT and CBCT-CT Image Synthesis
• Authors: Arnela Hadzic, Simon Johannes Joham, Martin Urschler,
• Abstract summary: Flow Matching framework is used to generate synthetic CT from MRI or CBCT images.<n>We train models for MRI sCT and CBCT sCT across three anatomical regions.<n>The results indicate that the method accurately reconstructs global anatomical structures.<n>Future work will explore patch-based training and latent-space flow models to improve resolution and local structural fidelity.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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