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Abstract #4756

A deep learning dipole inversion method for QSM of arbitrary head orientation and image resolution

Zhuang Xiong1, Yang Gao1, Steffen Bollmann1, and Hongfu Sun1
1School of ITEE, the University of Queensland, Brisbane, Australia


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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