Even subtle differences in masks can generate systematic but avoidable errors in QSM calculations. We believe these errors propagate through the calculation of the background phase. In this work, we assessed the effect of the mask on the QSM, selected optimal mask generation method and Deep Learning-based efficient mask generation method for in-vivo has been presented. This study represents the first step towards a fully-automated and optimal workflow for QSM calculation.
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