Quantitative susceptibility mapping (QSM) aims to solve an ill-posed field-to-source inversion to extract magnetic susceptibility of tissue. QSM algorithms based on deep convolutional neural networks have shown to produce artefact-free susceptibility maps. However, clinical scans often have a large variability, and it is unclear how a deep learning-based QSM algorithm is affected by discrepancies between the training data and clinical scans. Here we investigated the effects of different B0 orientations and noise levels of the tissue phase on the final quantitative susceptibility maps.
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