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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