  Magnetic resonance imaging (MRI) is being increasingly utilized to assess,
diagnose, and plan treatment for a variety of diseases. The ability to
visualize tissue in varied contrasts in the form of MR pulse sequences in a
single scan provides valuable insights to physicians, as well as enabling
automated systems performing downstream analysis. However many issues like
prohibitive scan time, image corruption, different acquisition protocols, or
allergies to certain contrast materials may hinder the process of acquiring
multiple sequences for a patient. This poses challenges to both physicians and
automated systems since complementary information provided by the missing
sequences is lost. In this paper, we propose a variant of generative
adversarial network (GAN) capable of leveraging redundant information contained
within multiple available sequences in order to generate one or more missing
sequences for a patient scan. 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.
