Abstract #2078
Super-Resolution for CEST MRI
Lukas Folle1, Katharina Tkotz2, Andrzej Liebert2, Fasil Gadjimuradov1, Lorenz Kapsner2, Moritz Fabian3, Sebastian Bickelhaupt2, David Simon4, Arnd Kleyer4, Gerhard Krönke4, Frank Roemer2, Moritz Zaiss3,5, Armin Nagel2,6, and Andreas Maier1
1Computer Science, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany, 2Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany, 3Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany, 4Department of Internal Medicine 3, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany, 5Magnetic Resonance Center, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany, 6Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
Synopsis
The resolution of chemical exchange saturation transfer (CEST) magnetic resonance imaging is limited by physical constraints. To visualize metabolic processes of small structures using CEST in patients knees, an increased resolution is necessary. In this work, we compared trilinear interpolation and zero-filling to neural network-based approaches to estimate a high-resolution image given the corresponding low-resolution data. We could show that a substantial quantitative improvement using neural networks could be achieved for unsaturated images while maintaining a comparable CEST contrast. Generalization of the method to brain CEST MRI was achieved without retraining of the network.
This abstract and the presentation materials are available to members only;
a login is required.
Join Here
