Due to the intrinsic data-driven property, many existing deep learning QSM methods can only be applied to local field maps with FOV orientation and image resolution consistent with the training data. This work proposes a novel and robust deep learning approach to reconstruct QSM of arbitrary head orientation and image resolution. Experiments are conducted on both simulated and in vivo human brain data to verify the proposed approach.
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