Deep learning frameworks are emerging methods for solving QSM problems these days. However, most previous deep neural networks designed for QSM requires phase unwrapping and background field removal preprocessing procedures. This work presents a novel end-to-end network, namely Lap-Unet, for instant QSM and tissue field mapping from the raw phase in a single run. Comparative results find that the proposed method resulted in more accurate and robust reconstructions than previously established single- and multi-step methods, particularly for QSM of intracranial hemorrhages, which has been challenging due to fast signal decays.
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