Artificial Intelligence to Optimize the Evaluation of Magnetic Resonance Images
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In the project, the interdisciplinary research team is focusing on the raw data from Magnetic Resonance Imaging (MRI) scans, in particular the so-called k-space. Until now, these data are not sufficiently interpretable and therefore cannot be fully utilized in clinical practice to characterize tumors and tissues - and ultimately to improve the diagnosis and treatment of diseases. Therefore, the team from Essen and Dortmund is developing new AI methods for the use of such raw data. The ultimate aim is to enable improved tissue characterization in the form of “virtual biopsies”.
From the UDE, Prof. Jens Kleesiek, Prof. Jan Egger and Moritz Rempe from the Institute for Artificial Intelligence in Medicine at University Hospital Essen are heading the project; from TU Dortmund University, Prof. Kevin Kröninger and his team from the Department of Physics are also involved. The Dortmund working group is developing special machine learning procedures in the project: so-called generative neural networks that can process the complex raw data of the MRIs. These methods are then used to characterize tissue and compared with traditional methods. At the end of the project, the methods developed will be made available to other researchers as open source libraries.

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