用流匹配方法从MRI或CBCT生成合成CT，助力无辐射放疗
Flow Matching for Conditional MRI-CT and CBCT-CT Image Synthesis
- 基于3D流匹配框架，通过噪声体积逐步转换生成sCT
- 在腹部、头颈、胸部三区域测试，全局结构重建准确
- 适合需降低辐射暴露的放疗场景，关注图像细节者可参考
从MRI或CBCT生成合成CT（sCT）对于实现仅使用MRI或基于CBCT的自适应放疗至关重要，可在提升治疗精度的同时减少患者辐射暴露。本文采用全3D流匹配（FM）框架，利用近期研究表明的高效率生成高质量图像的优势。方法通过轻量级3D编码器提取输入MRI或CBCT特征，结合学习到的FM速度场，将三维高斯噪声体素逐步变换为sCT图像。在SynthRAD2025挑战赛基准上评估，分别训练了腹部、头颈和胸部三个解剖区域的MRI-to-sCT与CBCT-to-sCT模型，验证与测试通过挑战提交系统完成。结果表明该方法能准确重建整体解剖结构；但精细结构保留有限，主要受制于内存与运行时间约束导致的训练分辨率较低。未来工作将探索基于补丁的训练和潜在空间流模型以提升分辨率与局部结构保真度。
原文摘要 · Abstract (English)
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 to sCT and CBCT to 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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