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arXiv · 2311.10879

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

Richard Osuala·Smriti Joshi·Apostolia Tsirikoglou·Lidia Garrucho·Walter H. L. Pinaya·Oliver Diaz·Karim Lekadir
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Abstract

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.

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Richard Osuala, Smriti Joshi, Apostolia Tsirikoglou, Lidia Garrucho, Walter H. L. Pinaya, Oliver Diaz, Karim Lekadir. 2023-11-17. Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation. https://doi.org/10.1117/12.3006961

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