用区域感知的流模型修复3D脑MRI病灶区，保持解剖结构一致。
RARF: Region-Aware Rectified Flows for 3D Brain MRI Inpainting
- 在缺失区域限制随机插值，保留真实像素提供个体解剖上下文
- 在BraTS挑战测试中重建质量达领先水平，解剖一致性优秀
- 适合需要高质量病灶修复的医学影像分析研究者使用
医学图像修复有望通过重建病灶区域的健康组织来提升自动化脑MRI分析效果。我们提出RARF——一种任务无关的区域感知修正流框架，用于掩码数据生成。本工作将该框架应用于3D脑MRI修复，作为2026年BraTS修补挑战的参赛方案。RARF将随机插值过程限制在修复区域，已观测体素保持固定，提供患者特异性解剖上下文。三维神经网络接收部分缺失图像（缺失区由高斯噪声填充），以及修复掩码和对应时间步信息。模型通过掩码流匹配与重建一致性目标联合训练，并结合掩码感知预处理与数据增强。推理时，学习到的速度场将初始噪声逐步引导至合理的缺失组织重建结果，再与未改变的观测解剖结构融合。在BraTS评估协议下实验表明，该方法生成了具有竞争力的重建结果，同时保持了解剖一致性。源代码见：https://github.com/TomasGuija/rarf。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。