Osuala, RichardJoshi, SmritiTsirikoglou, ApostoliaGarrucho, LidiaLópez Pinaya, Walter HugoDíaz, OliverLekadir, Karim, 1977-2025-03-252025-03-252024https://hdl.handle.net/2445/219974Despite 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, bioaccu- mulation, 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.12 p.application/pdfeng(c) SPIE, 2024Càncer de mamaAprenentatge automàticSubstàncies de contrastBreast cancerMachine learningContrast media (Diagnostic imaging)info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccess