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

• DOI: 10.48550/arxiv.2311.10879
• Publication Date: 2024-04-02
• Authors: Osuala, Richard; Joshi, Smriti; Tsirikoglou, Apos...; Garrucho, Lidia; López Pinaya, Wal...; Díaz, Oliver; Lekadir, Karim, 1...
• Keywords: Contrast media (Diagnostic imaging); FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Substàncies de contrast; Computer Science - Computer Vision and Pattern Recognition; Electrical Engineering and Systems Science - Image and Video Processing; 3. Good health; Càncer de mama; Machine Learning (cs.LG); Breast cancer; Aprenentatge automàtic; Machine learning; FOS: Electrical engineering, electronic engineering, information engineering

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.<br/>Accepted as oral presentation at SPIE Medical Imaging 2024 (Image Processing)<br/>