Fast Cross-Strength Multi-Contrast Brain MRI Translation using Latent Bridge Matching

Abstract:Magnetic Resonance Imaging (MRI) acquired at different field strengths exhibits pronounced variation in noise, resolution, homogeneity, and contrast, which limits comparability across acquisition settings and complicates downstream analysis. We address this with a unified conditional model for controllable field-to-field synthesis, built on the framework of conditional latent bridge matching. Our single model achieves highly competitive results across the validation phase for all three tasks of the MRIxFields2026 challenge without task-specific architectures or training. We achieve fast generation with only a single inference step, producing all modality and field-strength combinations for $30$ axial slices in under $90$ seconds, as well as cross-modality-strength translation for a full volume in under $70$ seconds, on a single NVIDIA A5000 GPU. We further provide extensive ablations regarding different components of our solution. Code: this https URL

10 pages, 4 figures. MRIxFields Workshop, MICCAI 2026

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

arXiv:2609.20341 [cs.CV]

(or arXiv:2609.20341v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.20341 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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