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Abstract #1635

xQSM: a deep learning QSM network using Octave Convolution

Yang Gao1, Xuanyu Zhu1, Stuart Crozier 1, Feng Liu1, and Hongfu Sun1
1University of Queensland, Brisbane, Australia

Deep learning frameworks are emerging methods for solving ill-posed inverse problems in medical imaging, including Quantitative Susceptibility Mapping (QSM). Previously, U-net has been successfully trained on susceptibility maps to learn the dipole inversion process; however, susceptibility contrast loss was observed in iron-rich deep grey matter regions. In this study, we propose an enhanced deep learning network “xQSM” using the state-of-the-art Octave Convolution, which shows more accurate susceptibility contrasts than the original U-net in both simulated and in vivo datasets.

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