Conditional Flow Matching for Cross-Field MRI Harmonisation

2026-09-01

AI Summary

本文针对跨场强MRI图像不一致问题，通过条件流匹配路径学习源到目标的直接映射，并结合多阶段训练优化模型性能。

Abstract

Magnetic resonance images of the same subject look markedly different across field strengths, which complicates the comparison and pooling of data across sites. We address cross-field brain-MRI translation for the MRIxFields2026 challenge, and in particular its Task~3: a single model that translates between any directed pair of the five field strengths and across three contrasts. We phrase the problem as a conditional flow matching path: because the source and target volumes are spatially registered, we learn a velocity field that carries the source slice directly to the target slice, rather than starting from noise. To learn this mapping from only three paired subjects, the unified model is trained in three stages: a degradation-bridge pretraining that distills a restoration prior from the abundant unpaired retrospective cohort, a cross-field finetuning over all directed pairs on the paired cohort, and an adversarial refinement that sharpens the output. At inference, we integrate the learned velocity with a second-order Heun solver in a handful of steps. A restoration prior learned without any paired data already reaches a mean SSIM of 0.837, and each subsequent training stage improves on it. A single 6.3M-parameter model thereby covers all 60 field-pair and contrast combinations, with inference in five solver steps per slice. On the challenge evaluation set the model reaches a mean SSIM of 0.909, averaged over the three contrasts, outperforming regression and diffusion baselines built on the identical network on all three challenge metrics.

Problem

• Magnetic Resonance Imaging
• Cross-Field
• Harmonisation
• MRIxFields2026

Innovation

• Conditional Flow Matching
• Cross-Field MRI Harmonisation
• Velocity Field Learning
• Degradation-Bridge Pretraining
• Adversarial Refinement