Abstract #3375

MultiNet PyGRAPPA: A Novel Method for Highly Accelerated Metabolite Mapping

Sahar Nassirpour^1,2, Paul Chang^1,2, and Anke Henning^1,3

¹Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, ²IMPRS for Cognitive and Systems Neuroscience, Eberhard-Karls University of Tuebingen, Tuebingen, Germany, ³Institute of Physics, Ernst-Moritz-Arndt University Greifswald, Greifswald, Germany

In this work, a novel acceleration method (MultiNet PyGrappa) is introduced which enables high in-plane acceleration factors for non-lipid suppressed ¹H MRSI data. By using a variable density undersampling scheme and reconstructing the missing data points with multiple neural networks, this method enables a more robust reconstruction of highly undersampled data. High resolution metabolite maps acquired at 9.4T in the human brain using the proposed method are presented.

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