发表机构
Universidad Rey Juan Carlos（胡安卡洛斯国王大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出RARF框架，将其用于2026年BraTS三维脑部MRI修复，通过区域感知修正流结合掩码流匹配等目标训练，在BraTS评估中取得有竞争力的结果且保持解剖一致性。
AI 中文摘要
医学图像修复有潜力通过重建病理区域内的健康组织来改进自动化脑部MRI分析。我们提出了RARF，一种面向掩码数据生成的任务无关区域感知修正流框架，将该框架实例化为三维脑部MRI修复模型，作为2026年BraTS修复挑战赛的提交方案。RARF将随机插值过程限制在修复区域内，同时保持观测体素固定，以提供患者特异性解剖结构上下文。三维神经网络接收部分缺失的图像、填充缺失区域的高斯噪声、对应的修复掩码以及时间步，模型通过掩码流匹配和重建一致性目标，结合感知掩码的预处理与数据增强进行训练。推理阶段，学习到的速度场将初始噪声转换为缺失组织的合理重建结果，再与未改变的观测解剖结构结合。在BraTS评估协议下的实验表明，所提方法生成的重建结果具有竞争力，同时保持了解剖结构一致性。源代码可在以下URL获取：this https URL。
英文摘要
Medical image inpainting has the potential to improve automated brain MRI analysis by reconstructing healthy tissue within pathological regions. We introduce RARF, a task-agnostic region-aware rectified flow framework for masked data generation. We instantiate the framework for 3D brain MRI inpainting as our submission to the BraTS Inpainting Challenge 2026. RARF restricts the stochastic interpolation process to the inpainting region, while the observed voxels remain fixed and provide patient-specific anatomical context. A three-dimensional neural network receives the partially voided image, with Gaussian noise filling the missing region, together with the inpainting mask and the corresponding timestep. The model is trained using masked flow-matching and reconstruction-consistency objectives, combined with mask-aware preprocessing and data augmentation. During inference, the learned velocity field transports the initial noise toward a plausible reconstruction of the missing tissue, which is then combined with the unchanged observed anatomy. Experiments under the BraTS evaluation protocol show that the proposed approach produces competitive reconstructions while maintaining anatomical consistency. Source code is available at: https://github.com/TomasGuija/rarf.
Comments11 pages, 2 figures. Preprint version corresponding to the initial submission prior to peer review, submitted as part of our participation in the BraTS 2026 Challenge. The final accepted version will be openly available in the official MICCAI proceedings on the conference website
