A Generalized Network for MRI Intensity Normalization.

Attila Simkó, Tommy Löfstedt, Anders Garpebring, Tufve Nyholm, Joakim Jonsson

Keywords: Intensity normalization, gain field, magnetic resonance imaging, artificial neural network, machine learning

TL;DR: An artificial neural network for MRI intensity normalization improves on current benchmark accuracy of N4ITK with a speedup of nearly 70.

Abstract: 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.

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