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Thanks to the recent success of generative adversarial network (GAN) forimage synthesis, there are many exciting GAN approaches that successfullysynthesize MR image contrast from other images with different contrasts. Theseapproaches are potentially important for image imputation problems, wherecomplete set of data is often difficult to obtain and image synthesis is one ofthe key solutions for handling the missing data problem. Unfortunately, thelack of the scalability of the existing GAN-based image translation approachesposes a fundamental challenge to understand the nature of the MR contrastimputation problem: which contrast does matter? Here, we present a systematicapproach using Collaborative Generative Adversarial Networks (CollaGAN), whichenable the learning of the joint image manifold of multiple MR contrasts toinvestigate which contrasts are essential. Our experimental results showed thatthe exogenous contrast from contrast agents is not replaceable, but otherendogenous contrast such as T1, T2, etc can be synthesized from other contrast.These findings may give important guidance to the acquisition protocol designfor MR in real clinical environment.

Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GAN