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Abstract #3764

Learning how to Clean Fingerprints -- Deep Learning based Separated Artefact Reduction and Regression for MR Fingerprinting

Yiling Xu1, Elisabeth Hoppe1, Peter Speier2, Thomas Kluge2, Mathias Nittka2, Gregor Körzdörfer2, and Andreas Maier1
1Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany, 2Magnetic Resonance, Siemens Healthcare, Erlangen, Germany

Various deep learning approaches have been recently introduced to enable a fast MRF reconstruction compared to dictionary matching. Artefacts resulting from the strong undersampling during the acquisition often impair the reconstruction results. In this work, we introduce a deep learning artefact reduction method in order to provide clean fingerprints for the subsequent regression network. Our results achieve a decreased relative error by over 50% using our artefact reduction method compared to previously proposed deep learning regression model without prior artefact reduction.

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