用真实医学影像数据集和统一模型，实现任意缺损情况下对比增强图像合成。
Contrast-X: A Multi-Modal Contrast Image Synthesis Benchmark and Universal Modality Flow Matching
- 构建跨器官的多模态配对影像数据集，支持病灶级评估
- 在1526例CT和1116例乳腺MRI上验证合成效果，达临床可用水平
- 单模型适配任意缺失模态，可推广至不同器官
对比增强成像在肿瘤诊断中至关重要，但许多患者因禁忌无法使用对比剂。从非对比输入合成对比图像成为自然解决方案。当前面临两大挑战：缺乏包含病灶级标注的配对数据集，且单一模型难以应对实际中任意缺失的模态组合。本文提出Contrast-X基准，涵盖CT（1,526例患者，10个器官）与多时相乳腺DCE-MRI（1,116例患者），每例均含放射科医生验证的时相标签与肿瘤掩码。我们进一步提出FlowMI模型，通过统一多模态潜在空间与流匹配机制，实现任意可用模态子集的处理。在多种缺失模态配置下进行评估，报告标准图像质量指标、放射科医生阅片研究及下游病灶分析结果。此外，测试跨器官泛化能力以检验模型是否学习到可迁移的对比增强操作。数据集、代码与排行榜将公开。
原文摘要 · Abstract (English)
Contrast-enhanced imaging is central to oncologic diagnosis, but contrast agents can be contraindicated for many of the patients who need them most. Synthesizing contrast scans from non-contrast inputs is the natural response. Two obstacles stand in the way: no benchmark provides paired contrast data with lesion-level evaluation, and no single model handles the arbitrary missing patterns seen in practice. We introduce Contrast-X, a benchmark of paired contrast-enhanced and non-contrast imaging spanning 10 organs in CT (1{,}526 patients) and multi-phase breast DCE-MRI (1116 patients). Every case carries radiologist-verified phase labels and tumor masks. We further propose FlowMI, a single model that handles arbitrary subsets of available modalities through a unified multi-modal latent space and flow matching. We benchmark a range of missing-modality configurations, reporting standard image-quality metrics, radiologist reader studies, and downstream lesion analysis on the synthesized scans. We further evaluate cross-organ generalization to test whether the model has learned a transferable contrast-enhancement operation. Dataset, code, and leaderboard will be released. Our code are available at https://github.com/YifanChen02/Contrast-X.
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