QSM from the raw phase using an end-to-end neural network
Yang Gao1, Zhuang Xiong 1, Amir Fazlollahi2, Peter J Nestor2, Viktor Vegh3, G. Bruce Pike4, Stuart Crozier1, Feng Liu1, and Hongfu Sun1
1School of ITEE, University of Queensland, Brisbane, Australia, 2Queensland Brain Institute, University of Queensland, Brisbane, Australia, 3Centre for Advanced Imaging, University of Queensland, Brisbane, Australia, 4Departments of Radiology and Clinical Neurosciences, University of Calgary, Calgray, AB, Canada
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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