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Abstract #1454

Linear projection-based CEST reconstruction – the simplest explainable AI

Felix Glang1, Moritz Fabian2, Alex German2, Katrin Khakzar2, Angelika Mennecke2, Frederik Laun3, Burkhard Kasper4, Manuel Schmidt2, Arnd Doerfler2, Klaus Scheffler1,5, and Moritz Zaiss1,2
1High-field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2Department of Neuroradiology, University Hospital Erlangen, Erlangen, Germany, 3Institute of Radiology, University Hospital Erlangen, Erlangen, Germany, 4Neurology, Epilepsy Center, University Clinic of Friedrich Alexander University Erlangen-Nürnberg, Erlangen, Germany, 5Department of Biomedical Magnetic Resonance, Eberhard Karls University Tübingen, Tübingen, Germany

Evaluation of multi-parametric in vivo CEST MRI often requires complex computational processing for both field inhomogeneity correction and contrast generation. In this work, linear regression was used to obtain coefficient vectors that directly map uncorrected 7T spectra to corrected Lorentzian target parameters by simple linear projection. The method generalizes from healthy subject training data to unseen test data of both healthy subjects and tumor patients. The linear projection approach thus integrates correction of both B0 and B1 inhomogeneity as well as contrast generation in a single fast and interpretable computation step.

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