Abstract

Invited to present my research in which we proposed a multi-input, multi-output generative adversarial network (GAN) called MM-GAN for the synthesis of missing MR pulse sequences. The proposed network is designed as a multi-input, multi-output network which combines information from all the available pulse sequences, implicitly infers which sequences are missing, and synthesizes the missing ones in a single forward pass. We demonstrate and validate our method on two brain MRI datasets each with four sequences, and show the applicability of the proposed method in simultaneously synthesizing all missing sequences in any possible scenario where either one, two, or three of the four sequences may be missing. We compare our approach with competing unimodal and multi-modal methods, and show that we outperform both quantitatively and qualitatively. Paper can be found here: https://arxiv.org/abs/1904.12200