Contrast agents in dynamic contrast enhanced magnetic resonance imaging allow to localize tumors and observe their contrast kinetics, which is essential for cancer characterization and respective treatment decision-making. However, contrast agent administration is not only associated with adverse health risks, but also restricted for patients during pregnancy, and for those with kidney malfunction, or other adverse reactions. With contrast uptake as key biomarker for lesion malignancy, cancer recurrence risk, and treatment response, it becomes pivotal to reduce the dependency on intravenous contrast agent administration. To this end, we propose a multi-conditional latent diffusion model capable of acquisition time-conditioned image synthesis of DCE-MRI temporal sequences. To evaluate medical image synthesis, we additionally propose and validate the Fr\'echet radiomics distance as an image quality measure based on biomarker variability between synthetic and real imaging data. Our results demonstrate our method's ability to generate realistic multi-sequence fat-saturated breast DCE-MRI and uncover the emerging potential of deep learning based contrast kinetics simulation. We publicly share our accessible codebase at https://github.com/RichardObi/ccnet and provide a user-friendly library for Fr\'echet radiomics distance calculation at https://pypi.org/project/frd-score.

翻译：动态对比增强磁共振成像中的对比剂可定位肿瘤并观察其对比动力学，这对癌症特征分析及相应治疗决策至关重要。然而，对比剂给药不仅伴随健康风险，还对妊娠期患者、肾功能不全者或存在其他不良反应的人群存在使用限制。鉴于对比剂摄取是病灶恶性程度、癌症复发风险及治疗反应的关键生物标志物，减少对静脉注射对比剂依赖性的需求日益迫切。为此，我们提出一种多条件潜在扩散模型，能够实现DCE-MRI时间序列的采集时间条件图像合成。为评估医学图像合成质量，我们进一步提出并验证了基于合成与真实影像数据间生物标志物变异性的弗雷歇影像组学距离作为图像质量度量指标。实验结果表明，我们的方法能够生成逼真的多序列脂肪抑制乳腺DCE-MRI，并揭示了基于深度学习的对比动力学模拟的潜在能力。我们已在https://github.com/RichardObi/ccnet公开共享可访问代码库，并在https://pypi.org/project/frd-score提供了弗雷歇影像组学距离计算的用户友好型工具库。