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Life sciences · Preprint
arXiv · September 3, 2026
Posted before peer review. The findings may change or fail to hold.
This is a preprint describing RARF, a neural network-based framework for reconstructing missing tissue in 3D brain MRI using rectified flows. The method was evaluated on the BraTS Inpainting Challenge 2026 dataset and reportedly produces competitive reconstructions while maintaining anatomical consistency, but has not undergone peer review and no clinical outcome data are provided.
Preprint. Brain MRI images from BraTS Inpainting Challenge 2026 dataset. Intervention: RARF: region-aware rectified flow neural network for 3D brain MRI inpainting, using masked flow-matching and reconstruction-consistency training.
RARF restricts stochastic interpolation to inpainting region while keeping observed voxels fixed Model trained using masked flow-matching and reconstruction-consistency objectives Approach produces competitive reconstructions under BraTS evaluation protocol
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If validated, the method could improve automated brain MRI analysis by reconstructing healthy tissue in pathological regions; however, no clinical endpoints, diagnostic accuracy, or patient outcomes are reported. Clinical utility remains unestablished.
This is an unrefereed preprint describing a machine learning method for brain MRI inpainting, evaluated on a challenge dataset without peer review or clinical validation.
As stated by the source record.
If validated, the method could improve automated brain MRI analysis by reconstructing healthy tissue in pathological regions; however, no clinical endpoints, diagnostic accuracy, or patient outcomes are reported. Clinical utility remains unestablished.
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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.
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