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Poster

Linear projection-based CEST reconstruction: the simplest explainable AI

MPS-Authors
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Glang,  F
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Scheffler,  K
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons214560

Zaiss,  M
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

https://www.ismrm.org/21/program-files/D-42.htm
(Abstract)

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Citation

Glang, F., Fabian, M., German, A., Khakzar, K., Mennecke, A., Laun, F., et al. (2021). Linear projection-based CEST reconstruction: the simplest explainable AI. Poster presented at 2021 ISMRM & SMRT Annual Meeting & Exhibition (ISMRM 2021).


Cite as: https://hdl.handle.net/21.11116/0000-0008-876F-4
Abstract
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.