Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GAN
This work addresses the MR contrast imputation problem for clinical acquisition protocol design, but it is incremental as it builds on existing GAN-based methods.
The paper tackled the problem of determining which MR contrasts are essential for image synthesis by using Collaborative GANs to learn joint manifolds, finding that exogenous contrasts from agents are irreplaceable while endogenous ones like T1 and T2 can be synthesized from other contrasts.
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.