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。