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

View ORCID ProfileSteffen Bollmann, View ORCID ProfileMatilde Holm Kristensen, View ORCID ProfileMorten Skaarup Larsen, View ORCID ProfileMathias Vassard Olsen, View ORCID ProfileMads Jozwiak Pedersen, Lasse Riis Østergaard, View ORCID ProfileKieran O’Brien, View ORCID ProfileChristian Langkammer, View ORCID ProfileAmir Fazlollahi, View ORCID ProfileMarkus Barth
doi: https://doi.org/10.1101/522151
Steffen Bollmann
aCentre for Advanced Imaging, The University of Queensland, Building 57 of University Dr, St Lucia QLD 4072, Brisbane, Australia
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Matilde Holm Kristensen
bDepartment of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7, 9000, Aalborg, Denmark
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Morten Skaarup Larsen
bDepartment of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7, 9000, Aalborg, Denmark
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Mathias Vassard Olsen
bDepartment of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7, 9000, Aalborg, Denmark
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Mads Jozwiak Pedersen
bDepartment of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7, 9000, Aalborg, Denmark
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Lasse Riis Østergaard
bDepartment of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7, 9000, Aalborg, Denmark
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Kieran O’Brien
aCentre for Advanced Imaging, The University of Queensland, Building 57 of University Dr, St Lucia QLD 4072, Brisbane, Australia
cSiemens Healthcare Pty Ltd, Brisbane, Australia
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Christian Langkammer
dDepartment of Neurology, Medical University of Graz, Auenbruggerplatz 22, 8036, Graz, Austria
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Amir Fazlollahi
eCSIRO Health and Biosecurity Flagship, The Australian eHealth Research Centre
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Markus Barth
aCentre for Advanced Imaging, The University of Queensland, Building 57 of University Dr, St Lucia QLD 4072, Brisbane, Australia
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Abstract

Quantitative susceptibility mapping (QSM) reveals pathological changes in widespread diseases such as Parkinson’s disease, Multiple Sclerosis, or hepatic iron overload. QSM requires multiple processing steps after the acquisition of magnetic resonance imaging (MRI) phase measurements such as unwrapping, background field removal and the solution of an ill-posed field-to-source-inversion. Current techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and lead to suboptimal or over-regularized solutions requiring a careful choice of parameters that make a clinical application of QSM challenging. We have previously demonstrated that a deep convolutional neural network can invert the magnetic dipole kernel with a very efficient feed forward multiplication not requiring iterative optimization or the choice of regularization parameters. In this work, we extended this approach to remove background fields in QSM. The prototype method, called SHARQnet, was trained on simulated background fields and tested on 3T and 7T brain datasets. We show that SHARQnet outperforms current background field removal procedures and generalizes to a wide range of input data without requiring any parameter adjustments. In summary, we demonstrate that the solution of ill-posed problems in QSM can be achieved by learning the underlying physics causing the artifacts and removing them in an efficient and reliable manner and thereby will help to bring QSM towards clinical applications.

Footnotes

  • Competing Interests statement: Authors SB, MB, KO are co-inventors of a patent “Solving the ill-posed quantitative susceptibility mapping inverse problem using deep convolutional neural networks”, filed on 29th Dec 2017. KO is employed by Siemens Healthineers.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted January 17, 2019.
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SHARQnet - Sophisticated Harmonic Artifact Reduction in Quantitative Susceptibility Mapping using a Deep Convolutional Neural Network
Steffen Bollmann, Matilde Holm Kristensen, Morten Skaarup Larsen, Mathias Vassard Olsen, Mads Jozwiak Pedersen, Lasse Riis Østergaard, Kieran O’Brien, Christian Langkammer, Amir Fazlollahi, Markus Barth
bioRxiv 522151; doi: https://doi.org/10.1101/522151
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SHARQnet - Sophisticated Harmonic Artifact Reduction in Quantitative Susceptibility Mapping using a Deep Convolutional Neural Network
Steffen Bollmann, Matilde Holm Kristensen, Morten Skaarup Larsen, Mathias Vassard Olsen, Mads Jozwiak Pedersen, Lasse Riis Østergaard, Kieran O’Brien, Christian Langkammer, Amir Fazlollahi, Markus Barth
bioRxiv 522151; doi: https://doi.org/10.1101/522151

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