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arXiv 2603.00535cs.CV

RAFM：用于无配对CBCT到CT翻译的检索增强流匹配

RAFM: Retrieval-Augmented Flow Matching for Unpaired CBCT-to-CT Translation

Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia（信息科技学院，墨尔本大学，澳大利亚墨尔本）；Shanghai Artificial Intelligence Laboratory, Shanghai, China（上海人工智能实验室，中国上海）

Xianhao Zhou, Jianghao Wu, Lanfeng Zhong, Ku Zhao, Jinlong He, Shaoting Zhang, Guotai Wang

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AI总结：

RAFM通过检索增强的流匹配方法，提升无配对CBCT到CT翻译的性能，优于现有方法。

摘要：

锥形束CT（CBCT）在放疗中经常被获取，但其存在严重的伪影和不可靠的Hounsfield单位（HU）值，限制了其直接用于剂量计算。从CBCT生成合成CT（sCT）是一项重要的任务，但配对的CBCT-CT数据往往不可用或不可靠，由于时间间隙、解剖差异和配准误差。在本工作中，我们引入了校正流（RF）到医学影像中的无配对CBCT到CT翻译。尽管RF理论上可以通过分布级耦合和确定性传输与无配对学习兼容，但其在小医疗数据集和有限批量大小下的实际有效性仍待探索。直接应用随机或批量本地伪配对会产生不稳定的监督，因为端点样本存在语义不匹配。为了解决这一挑战，我们提出了检索增强流匹配（RAFM），通过冻结的DINOv3编码器和全局CT内存库构建检索引导的伪配对，将RF适应到医学环境。这种策略提高了经验耦合质量并稳定了无配对流训练。在SynthRAD2023上进行的实验显示，在严格受试者级真实无配对协议下，RAFM在FID、MAE、SSIM、PSNR和SegScore上均优于现有方法。代码可在https://github.com/HiLab-git/RAFM.git上获取。

英文摘要：

Cone-beam CT (CBCT) is routinely acquired in radiotherapy but suffers from severe artifacts and unreliable Hounsfield Unit (HU) values, limiting its direct use for dose calculation. Synthetic CT (sCT) generation from CBCT is therefore an important task, yet paired CBCT--CT data are often unavailable or unreliable due to temporal gaps, anatomical variation, and registration errors. In this work, we introduce rectified flow (RF) into unpaired CBCT-to-CT translation in medical imaging. Although RF is theoretically compatible with unpaired learning through distribution-level coupling and deterministic transport, its practical effectiveness under small medical datasets and limited batch sizes remains underexplored. Direct application with random or batch-local pseudo pairing can produce unstable supervision due to semantically mismatched endpoint samples. To address this challenge, we propose Retrieval-Augmented Flow Matching (RAFM), which adapts RF to the medical setting by constructing retrieval-guided pseudo pairs using a frozen DINOv3 encoder and a global CT memory bank. This strategy improves empirical coupling quality and stabilizes unpaired flow-based training. Experiments on SynthRAD2023 under a strict subject-level true-unpaired protocol show that RAFM outperforms existing methods across FID, MAE, SSIM, PSNR, and SegScore. The code is available at https://github.com/HiLab-git/RAFM.git.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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