  Magnetic resonance (MR) and computer tomography (CT) images are two typical
types of medical images that provide mutually-complementary information for
accurate clinical diagnosis and treatment. However, obtaining both images may
be limited due to some considerations such as cost, radiation dose and modality
missing. Recently, medical image synthesis has aroused gaining research
interest to cope with this limitation. In this paper, we propose a
bidirectional learning model, denoted as dual contrast cycleGAN (DC-cycleGAN),
to synthesize medical images from unpaired data. Specifically, a dual contrast
loss is introduced into the discriminators to indirectly build constraints
between real source and synthetic images by taking advantage of samples from
the source domain as negative samples and enforce the synthetic images to fall
far away from the source domain. In addition, cross-entropy and structural
similarity index (SSIM) are integrated into the DC-cycleGAN in order to
consider both the luminance and structure of samples when synthesizing images.
The experimental results indicate that DC-cycleGAN is able to produce promising
results as compared with other cycleGAN-based medical image synthesis methods
such as cycleGAN, RegGAN, DualGAN, and NiceGAN. The code will be available at
https://github.com/JiayuanWang-JW/DC-cycleGAN.
