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

# metadata.dc.description.abstract[].value
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, bioaccu-
mulation, 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.

# metadata.dc.relation.isformatof[].value
Versió postprint de la comunicació publicada a: https://doi.org/10.1117/12.3006961

# metadata.dc.relation.ispartof[].value
Comunicació a: Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129260Y (2 April 2024)

# metadata.dc.source[].value
Comunicacions a congressos  (Matemàtiques i Informàtica)

# metadata.dc.title[].value
Pre- to post-contrast breast MRI synthesis for enhanced tumour segmentation