The 2016 Reconstruction Challenge urged the need of QSM algorithms which minimize several classical image quality measures relatively to a ground truth, but also achieve an acceptable human image perception by avoiding oversmoothing effects. A multicale shearlet system was used together with a total generalized variation (TGV) term to regularize the susceptibility-phase convolution problem. The results show that these regularizers are useful to obtain quantitative susceptibility maps which are rich in detail and simultaneously can achieve top five results in the ranking of the 2016 Reconstruction Challenge regarding all classical image quality measures used in this challenge.
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