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Neuroimage. 2019 Jul 15;195:373-383. doi: 10.1016/j.neuroimage.2019.03.060. Epub 2019 Mar 29.

DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping.

Author information

1
Centre for Advanced Imaging, The University of Queensland, Building 57 of University Dr, St Lucia, QLD, 4072, Brisbane, Australia. Electronic address: steffen.bollmann@cai.uq.edu.au.
2
Department of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7, 9000, Aalborg, Denmark.
3
Centre for Advanced Imaging, The University of Queensland, Building 57 of University Dr, St Lucia, QLD, 4072, Brisbane, Australia; Siemens Healthcare Pty Ltd, Brisbane, Australia.
4
Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, 8036, Graz, Austria.
5
Centre for Advanced Imaging, The University of Queensland, Building 57 of University Dr, St Lucia, QLD, 4072, Brisbane, Australia.

Abstract

Quantitative susceptibility mapping (QSM) is based on magnetic resonance imaging (MRI) phase measurements and has gained broad interest because it yields relevant information on biological tissue properties, predominantly myelin, iron and calcium in vivo. Thereby, QSM can also reveal pathological changes of these key components in widespread diseases such as Parkinson's disease, Multiple Sclerosis, or hepatic iron overload. While the ill-posed field-to-source-inversion problem underlying QSM is conventionally assessed by the means of regularization techniques, we trained a fully convolutional deep neural network - DeepQSM - to directly invert the magnetic dipole kernel convolution. DeepQSM learned the physical forward problem using purely synthetic data and is capable of solving the ill-posed field-to-source inversion on in vivo MRI phase data. The magnetic susceptibility maps reconstructed by DeepQSM enable identification of deep brain substructures and provide information on their respective magnetic tissue properties. In summary, DeepQSM can invert the magnetic dipole kernel convolution and delivers robust solutions to this ill-posed problem.

KEYWORDS:

Deep learning; Dipole inversion; Ill-posed problem; Quantitative susceptibility mapping

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