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.
@article{arxiv.1909.05484,
title = {A Generalized Network for MRI Intensity Normalization},
author = {A. Simkó and T. Löfstedt and A. Garpebring and T. Nyholm and J. Jonsson},
journal= {arXiv preprint arXiv:1909.05484},
year = {2019}
}