  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 https://github.com/RichardObi/pre_post_synthesis.
