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

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.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-C42D-D Version Permalink: http://hdl.handle.net/21.11116/0000-0004-C42E-C
Genre: Poster

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 Creators:
Metere, Riccardo1, Author              
Marschner, Henrik1, Author              
Reimann, Katja2, Author
Pampel, André1, Author              
Möller, Harald E.1, Author              
Affiliations:
1Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634558              
2External Organizations, ou_persistent22              

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 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.

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 Dates: 2018-06-21
 Publication Status: Not specified
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Title: Joint Annual Meeting ISMRM-ESMRMB 2018
Place of Event: Paris, France
Start-/End Date: 2018-06-16 - 2018-06-21

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Title: Proceedings of the International Society for Magnetic Resonance in Medicine (2018)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: 2755 Start / End Page: - Identifier: -