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Abstract #4931

SHARQnet - Sophisticated Harmonic Artifact Reduction in Quantitative Susceptibility Mapping using a Deep Convolutional Neural Network

Mathias Vassard Olsen1, Morten Skaarup Larsen1, Matilde Holm Kristensen1, Mads Jozwiak Pedersen1, Lasse Riis Ƙstergaard1, Kieran O'Brien2,3, Christian Langkammer4, Markus Barth2, and Steffen Bollmann2

1Department of Health Science and Technology, Aalborg University, Aalborg East, Denmark, 2Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia, 3Siemens Healthcare Pty Ltd, Brisbane, Australia, 4Department of Neurology, Medical University of Graz, Graz, Austria

We propose a fully convolutional neural network for background field removal in MR phase images for Quantitative Susceptibility Mapping. Our proposed method, SHARQnet, learns to solve the background field problem from theoretical simulations of background field distributions, and the results are compared to current state-of-the-art methods like SHARP, V-SHARP, and RESHARP.

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