基于条件隐式传输的密集时间对比合成
Dense Temporal Contrast Synthesis via Conditioned Latent Transport
- Universitat de Barcelona（巴塞罗那大学）
- Institució Catalana de Recerca i Estudis Avançats (ICREA)（加泰罗尼亚高级研究学院）
- Karolinska Institutet（卡罗林斯卡学院）
- Helmholtz Munich（慕尼黑亥姆霍兹中心）
- Technical University of Munich（慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对DCE-MRI依赖钆基对比剂的缺陷，本文提出条件隐式传输框架合成对比增强，提升肿瘤分割性能，70%病例的合成图像可支持等效诊疗决策，为安全成像流程提供方案。
AI中文摘要：
动态对比增强磁共振成像（DCE-MRI）是乳腺癌诊疗的重要手段，但依赖钆基对比剂（GBCAs）的使用存在诸多限制：会禁忌人群无法使用、延长扫描流程，还存在环境毒性问题。对比合成提供了一种非侵入性替代方案，但现有方法存在空间真实性与时间连续性难以兼顾、迭代采样速度慢、结构先验利用不足、缺乏临床验证等缺陷。本文提出一种新型条件隐式传输框架，可通过单次前向传播预测对比增强效果；通过将隐式轨迹锚定到造影前解剖结构并应用连续时间条件，模型能合成患者在任意采集时间的个性化对比演化过程。所提方法在空间、感知、时间及分布指标上均优于基线模型和现有最优模型；在独立外部队列上评估时，该方法对扫描仪噪声及不同采集方案导致的域偏移具有鲁棒性，其合成的对比增强数据还显著提升了下游肿瘤分割性能：Dice系数相对提升22.4%（0.60 vs 基线造影前的0.49，p<0.01），边界分割误差降低超39%，且优于所有其他生成模型基线。此外，本文开展了由4名乳腺放射科医生参与的阅片研究，评估了40例随机病例中合成序列的图像质量、动力学保真度及诊断可行性，结果显示70%的病例中，合成图像提供了足够的临床信息以支持与真实DCE-MRI相同的诊疗决策，为更安全、快速的无对比剂或低对比剂成像流程提供了可行路径。
英文摘要：
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is essential for breast cancer management, but reliance on gadolinium-based contrast agents (GBCAs) restricts use in contraindicated populations, prolongs scan protocols, and presents environmental toxicity concerns. Contrast synthesis offers a non-invasive alternative; however, existing approaches struggle to balance spatial realism with temporal continuity, suffer from slow iterative sampling, underutilize structural priors, and lack clinical validation. We propose a novel conditioned latent transport framework that predicts contrast enhancement in a single forward pass. By anchoring the latent trajectory to the pre-contrast anatomy and applying continuous time conditioning, the model synthesizes patient-specific contrast evolution at any acquisition time. The proposed approach outperforms baseline and the state-of-the-art models across spatial, perceptual, temporal, and distributional metrics. Evaluated on an independent external cohort, the method demonstrates robustness to domain shifts induced by scanner noise as well as differing acquisition protocol. Furthermore, our synthetic contrast enhancement significantly improved downstream tumor segmentation performance, yielding a 22.4% relative increase in Dice coefficient (0.60 vs. 0.49 baseline pre-contrast, p < 0.01), reducing boundary segmentation error by over 39%, while outperforming all other generative model baselines. Finally, a reader study involving four breast radiologists evaluated the image quality, kinetic fidelity, and diagnostic viability of our synthesized sequences across 40 randomly selected cases. The results demonstrated that in 70% of cases, synthesized images provided sufficient clinical information to support the same management decisions as real DCE-MRI, suggesting a path toward safer and faster contrast-free or contrast-reduced imaging workflows.
