  Medical image translation has the potential to reduce the imaging workload,
by removing the need to capture some sequences, and to reduce the annotation
burden for developing machine learning methods. GANs have been used
successfully to translate images from one domain to another, such as MR to CT.
At present, paired data (registered MR and CT images) or extra supervision
(e.g. segmentation masks) is needed to learn good translation models.
Registering multiple modalities or annotating structures within each of them is
a tedious and laborious task. Thus, there is a need to develop improved
translation methods for unpaired data. Here, we introduce modified pix2pix
models for tasks CT$\rightarrow$MR and MR$\rightarrow$CT, trained with unpaired
CT and MR data, and MRCAT pairs generated from the MR scans. The proposed
modifications utilize the paired MR and MRCAT images to ensure good alignment
between input and translated images, and unpaired CT images ensure the
MR$\rightarrow$CT model produces realistic-looking CT and CT$\rightarrow$MR
model works well with real CT as input. The proposed pix2pix variants
outperform baseline pix2pix, pix2pixHD and CycleGAN in terms of FID and KID,
and generate more realistic looking CT and MR translations.
