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Osuala, R. ; Joshi, S.* ; Tsirikoglou, A.* ; Garrucho, L.* ; Pinaya, W.H.L.* ; Diaz, O.* ; Lekadir, K.*

Pre- to post-contrast breast MRI synthesis for enhanced tumour segmentation.

In: (Conference on Medical Imaging - Image Processing, 19-22 Februar 2024, San Diego, California). 1000 20th St, Po Box 10, Bellingham, Wa 98227-0010 Usa: SPIE, 2024.:129260Y (Proc. SPIE ; 12926)

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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.

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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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