MAMA-FLUX.2：面向MAMA-SYNTH挑战赛的对比后乳腺动态对比增强MRI图像到图像合成

中文摘要 AI 辅助

动态对比增强乳腺MRI是癌症诊断与监测的核心手段，但需使用基于钆的对比剂。本研究针对MAMA-SYNTH挑战赛解决对比前至对比后乳腺MRI的合成问题，提出基于FLUX.2-Klein-4B的条件潜在流匹配方法MAMA-FLUX.2，将对比前图像编码为空间条件，模型预测与对比后目标潜在空间相关的流场。为高效适配预训练模型，采用LoRA微调并引入结合全局流匹配、肿瘤区域监督及稳定前景正则化的区域训练目标；进一步在轴向切片上探究LoRA秩、强度窗设置及区域损失权重，优先关注临床相关的肿瘤聚焦指标。消融实验表明，适度的肿瘤与稳定前景权重可改善图像保真度与肿瘤区域准确率的权衡，最终模型在LoRA秩/α=64/64、MHA_max=25、λ_tumor=0.25、λ_stable=0.1时实现最佳整体平衡。这些结果表明，紧凑的预训练整流流变换器可通过参数高效微调与任务感知区域损失适配于对比增强MRI合成任务。

英文摘要

Dynamic contrast-enhanced breast MRI is central to cancer diagnosis and monitoring, but requires gadolinium-based contrast agents. In this work, we address pre-to-post contrast breast MRI synthesis for the MAMA-SYNTH challenge. We propose MAMA-FLUX.2, a conditional latent flow-matching approach based on FLUX.2-Klein-4B. The pre-contrast image is encoded as spatial conditioning, while the model predicts the flow field associated with the post-contrast target latent. To adapt the pretrained model efficiently, we use LoRA fine-tuning and introduce a regional training objective combining global flow matching, tumor-region supervision, and stable foreground regularization. We further investigate LoRA rank, intensity windowing, and regional loss weights on axial slices, prioritizing clinically relevant tumor-focused metrics. Our ablation study shows that moderate tumor and stable-foreground weighting improves the trade-off between image fidelity and tumor-region accuracy. The final model achieves the best overall balance with LoRA rank/$α=64/64$, $\mathrm{MHA}_{\max}=25$, $λ_{\mathrm{tumor}}=0.25$, and $λ_{\mathrm{stable}}=0.1$. These results demonstrate that compact pretrained rectified-flow transformers can be adapted for contrast-enhanced MRI synthesis using parameter-efficient fine-tuning and task-aware regional losses.

发表机构

• AGH University of Krakow（克拉科夫AGH科技大学）
• Sano Centre for Computational Medicine（萨诺计算医学中心）

机构由 AI 辅助整理，请以论文原文为准。

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9 pages, 3 figures, 1 table