RARF: Region-Aware Rectified Flows for 3D Brain MRI Inpainting

Abstract: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: this https URL.

11 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

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2609.03956 [cs.CV]

(or arXiv:2609.03956v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.03956 arXiv-issued DOI via DataCite (pending registration)