  Automatically generating one medical imaging modality from another is known
as medical image translation, and has numerous interesting applications. This
paper presents an interpretable generative modelling approach to medical image
translation. By allowing a common model for group-wise normalisation and
segmentation of brain scans to handle missing data, the model allows for
predicting entirely missing modalities from one, or a few, MR contrasts.
Furthermore, the model can be trained on a fairly small number of subjects. The
proposed model is validated on three clinically relevant scenarios. Results
appear promising and show that a principled, probabilistic model of the
relationship between multi-channel signal intensities can be used to infer
missing modalities -- both MR contrasts and CT images.
