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

2026-09-17

AI Summary

该研究通过使用条件潜桥匹配方法解决不同场强MRI图像间的可比性问题，实现快速高质量的多对比度脑MRI转换。

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: https://gitlab.com/siddharthsrivastava/mrixfields-2026

Problem

• Magnetic Resonance Imaging
• field strengths
• noise
• resolution
• contrast

Innovation

• conditional latent bridge matching
• field-to-field synthesis
• fast generation
• multi-contrast MRI
• cross-modality-strength translation