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