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Predicting histological stainings of brain tissue from MRI data using artificial neural networks

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Metere,  Riccardo
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Marschner,  Henrik
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Pampel,  André
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Möller,  Harald E.
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

Metere, R., Marschner, H., Reimann, K., Pampel, A., & Möller, H. E. (2018). Predicting histological stainings of brain tissue from MRI data using artificial neural networks. Poster presented at Joint Annual Meeting ISMRM-ESMRMB 2018, Paris, France.


Cite as: http://hdl.handle.net/21.11116/0000-0004-C42D-D
Abstract
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.