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Abstract #3280

Automatic parameter selection for quantitative susceptibility mapping (QSM) with regard to Shearlet/TGV-regularization

Janis Stiegeler1,2 and Sina Straub1
1Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, 2Department of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany


In this work noise is assumed to be a random vector and the method of unbiased predictive risk estimator (UPRE) is used to select suitable data/regularization parameters to solve the local phase to susceptibility deconvolution problem of quantitative susceptibility mapping (QSM). The proposed algorithm is tested on the simulated multi-echo data provided at the 2019 QSM Reconstruction Challenge. This work is a further development of the algorithm presented at the ISMRM Meeting 2021 and its purpose is to show that the method of UPRE can be applied advantageously to a shearlet /TGV based susceptibility reconstruction.

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