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 this https URL

通过多条件潜在扩散模型实现了动态对比增强磁共振成像的时间序列图像合成，提出了基于 Frechet 放射学距离的医学图像质量评估方法，并展示了深度学习在对比动力学模拟中的潜力。