  Image normalization, the correction for intra-volume inhomogeneities in
magnetic resonance imaging (MRI) data has little significance for visual
diagnosis, but is a crucial step before automated radiotherapy solutions. There
are several well-established normalization methods, however they are usually
time expensive and difficult to tune for a specific dataset. In this study, we
show how an artificial neural network (ANN) can be trained on non-medical
images --- making the model general --- for intensity normalization on medical
MRI images. Compared to one of the most well-known correction methods, N4ITK,
the trained network achieves a higher accuracy with a speedup-factor of almost
70.
