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Despite its benefits for tumour detection and treatment, the administrationof contrast agents in dynamic contrast-enhanced MRI (DCE-MRI) is associatedwith a range of issues, including their invasiveness, bioaccumulation, and arisk of nephrogenic systemic fibrosis. This study explores the feasibility ofproducing synthetic contrast enhancements by translating pre-contrastT1-weighted fat-saturated breast MRI to their corresponding first DCE-MRIsequence leveraging the capabilities of a generative adversarial network (GAN).Additionally, we introduce a Scaled Aggregate Measure (SAMe) designed forquantitatively evaluating the quality of synthetic data in a principled mannerand serving as a basis for selecting the optimal generative model. We assessthe generated DCE-MRI data using quantitative image quality metrics and applythem to the downstream task of 3D breast tumour segmentation. Our resultshighlight the potential of post-contrast DCE-MRI synthesis in enhancing therobustness of breast tumour segmentation models via data augmentation. Our codeis available at https://github.com/RichardObi/pre_post_synthesis.

Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation