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.

翻译：尽管动态对比增强MRI（DCE-MRI）在肿瘤检测与治疗中具有显著优势，但对比剂的使用仍伴随着一系列问题，包括其侵入性、生物累积性以及肾源性系统性纤维化的风险。本研究探索了利用生成对抗网络（GAN）的能力，将对比前T1加权脂肪饱和乳腺MRI转换为对应的首个DCE-MRI序列，从而生成合成对比增强图像的可行性。此外，我们提出了一种尺度聚合度量（Scaled Aggregate Measure, SAMe），用于以原则性方式定量评估合成数据质量，并作为选择最优生成模型的基础。我们采用定量图像质量指标评估生成的DCE-MRI数据，并将其应用于三维乳腺肿瘤分割的下游任务。研究结果表明，通过数据增强，对比后DCE-MRI合成在提升乳腺肿瘤分割模型鲁棒性方面具有潜在价值。我们的代码已开源，地址为：https://github.com/RichardObi/pre_post_synthesis。