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Abstract #2755

Predicting Histological Stainings of Brain Tissue from MRI Data using Artificial Neural Networks

Riccardo Metere1, Henrik Marschner1, Katja Reimann2,3, André Pampel1, and Harald E. Möller1

1NMR Unit, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 2Paul Flechsig Institute for Brain Research, University of Leipzig, Leipzig, Germany, 3Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

The generation of contrast in MRI relies on a variety of physical processes (e.g. relaxation, magnetization transfer, etc.) that produces a relatively rich amount of information for biological samples. However, given the complex microstructure of tissues, some histological information of relevance in biology and medicine are obtained more easily using optical acquisition techniques on specifically stained specimens. Here, we propose a machine-learning-based method of replicating the contrast information from optical microscopy by exploiting the richness of MRI acquisitions (which will limit the final resolution). The approach exploits the properties of multi-layer feed-forward neural networks as universal function approximators.

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