Early-to-Late Prediction of DCE-MRI Contrast-Enhanced Images in Using Generative Adversarial Networks

We consider the problem of predicting early-to-late Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) in breast cancer sequences. This is approached with conditional generative adversarial networks that synthesize the late response image given the early response. We propose a novel loss function to improve the ability of GAN models to learn the relevant temporal tissue dynamics under this setting, as well as a clinically relevant metric to assess performance. Our experiments show that the proposed strategy predicts accurate responses and could serve as a solution to implement fast diagnostic protocols.