  Magnetic Resonance (MR) images of different modalities can provide
complementary information for clinical diagnosis, but whole modalities are
often costly to access. Most existing methods only focus on synthesizing
missing images between two modalities, which limits their robustness and
efficiency when multiple modalities are missing. To address this problem, we
propose a multi-modality generative adversarial network (MGAN) to synthesize
three high-quality MR modalities (FLAIR, T1 and T1ce) from one MR modality T2
simultaneously. The experimental results show that the quality of the
synthesized images by our proposed methods is better than the one synthesized
by the baseline model, pix2pix. Besides, for MR brain image synthesis, it is
important to preserve the critical tumor information in the generated
modalities, so we further introduce a multi-modality tumor consistency loss to
MGAN, called TC-MGAN. We use the synthesized modalities by TC-MGAN to boost the
tumor segmentation accuracy, and the results demonstrate its effectiveness.

Computer Vision and Pattern Recognition;Image and Video Processing