Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation
This addresses issues like invasiveness and risks for patients undergoing breast MRI, but it is incremental as it applies existing generative methods to a specific medical imaging task.
This study tackled the problem of invasive contrast agents in breast MRI by using a GAN to synthesize post-contrast images from pre-contrast scans, aiming to enhance tumour segmentation robustness through data augmentation.
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.