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。

基于多条件潜在扩散模型的对比动力学学习

Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion  Models

Despite its benefits for tumour detection and treatment, the administration
of contrast agents in dynamic contrast-enhanced MRI (DCE-MRI) is associated
with a range of issues, including their invasiveness, bioaccumulation, and a
risk of nephrogenic systemic fibrosis. This study explores the feasibility of
producing synthetic contrast enhancements by translating pre-contrast
T1-weighted fat-saturated breast MRI to their corresponding first DCE-MRI
sequence leveraging the capabilities of a generative adversarial network (GAN).
Additionally, we introduce a Scaled Aggregate Measure (SAMe) designed for
quantitatively evaluating the quality of synthetic data in a principled manner
and serving as a basis for selecting the optimal generative model. We assess
the generated DCE-MRI data using quantitative image quality metrics and apply
them to the downstream task of 3D breast tumour segmentation. Our results
highlight the potential of post-contrast DCE-MRI synthesis in enhancing the
robustness of breast tumour segmentation models via data augmentation. Our code
is available at this https URL

利用生成对抗网络 (GAN) 将原始的 T1 加权饱和脂肪胸部 MRI 图像转化为相应的首次动态对比增强 MRI 序列，生成合成对比增强图像，并通过定量图像质量度量来评估生成的数据质量，将其应用于三维乳腺肿瘤分割，结果表明通过数据增强方式合成后的动态对比增强 MRI 图像在增强乳腺肿瘤分割模型的稳健性方面具有潜力，代码链接见摘要。