Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

DeepQSM - Using Deep Learning to Solve the Dipole Inversion for MRI Susceptibility Mapping

Kasper Gade Bøtker Rasmussen, Mads Kristensen, Rasmus Guldhammer Blendal, Lasse Riis Østergaard, View ORCID ProfileMaciej Plocharski, View ORCID ProfileKieran O’Brien, View ORCID ProfileChristian Langkammer, View ORCID ProfileAndrew Janke, View ORCID ProfileMarkus Barth, View ORCID ProfileSteffen Bollmann
doi: https://doi.org/10.1101/278036
Kasper Gade Bøtker Rasmussen
aDepartment of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7, 9000, Aalborg, Denmark
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mads Kristensen
aDepartment of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7, 9000, Aalborg, Denmark
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Rasmus Guldhammer Blendal
aDepartment of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7, 9000, Aalborg, Denmark
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Lasse Riis Østergaard
aDepartment of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7, 9000, Aalborg, Denmark
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Maciej Plocharski
aDepartment of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7, 9000, Aalborg, Denmark
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Maciej Plocharski
Kieran O’Brien
bCentre for Advanced Imaging, University of Queensland, Building 57 of University Dr, St Lucia QLD 4072, Brisbane, Australia
cSiemens Healthcare Pty Ltd, Brisbane, Australia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Kieran O’Brien
Christian Langkammer
dDepartment of Neurology, Medical University of Graz, Auenbruggerplatz 22, 8036, Graz, Austria
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Christian Langkammer
Andrew Janke
bCentre for Advanced Imaging, University of Queensland, Building 57 of University Dr, St Lucia QLD 4072, Brisbane, Australia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Andrew Janke
Markus Barth
bCentre for Advanced Imaging, University of Queensland, Building 57 of University Dr, St Lucia QLD 4072, Brisbane, Australia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Markus Barth
Steffen Bollmann
bCentre for Advanced Imaging, University of Queensland, Building 57 of University Dr, St Lucia QLD 4072, Brisbane, Australia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Steffen Bollmann
  • For correspondence: steffen.bollmann@cai.uq.edu.au
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Quantitative susceptibility mapping (QSM) aims to extract the magnetic susceptibility of tissue from magnetic resonance imaging (MRI) phase measurements. The mapping of magnetic susceptibility in vivo has gained broad interest in several fields of science and medicine because it yields relevant information on biological tissue properties, predominantly myelin, iron and calcium. Thereby, QSM can also reveal pathological changes of these key components in devastating diseases such as Parkinson’s disease, Multiple Sclerosis, or hepatic iron overload. As QSM requires the solution of an ill-posed field-to-source-inversion, current techniques utilize manual optimization of regularization parameters to balance between smoothing, artifacts and quantification accuracy. We trained a fully convolutional deep neural network - DeepQSM - to invert the magnetic dipole kernel convolution. This network is capable of solving the ill-posed field-to-source inversion on real-world in vivo MRI phase data without the need for manual parameter tuning, which proves that this network has generalized the underlying mathematical principle of the dipole inversion. We demonstrate that DeepQSM’s susceptibility maps enable identification of deep brain substructures that are not visible in MRI phase data and provide information on their respective magnetic tissue properties. We illustrate DeepQSM’s clinical relevance in a patient with multiple sclerosis showing its sensitivity to white matter lesions. In summary, DeepQSM can be used to determine the composition of myelin sheets of nerve fibers in the brain, and to assess quantitative information on iron homeostasis and its dysregulation, and will subsequently contribute to a better understanding of these biological processes in health and disease.

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.
Back to top
PreviousNext
Posted March 07, 2018.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
DeepQSM - Using Deep Learning to Solve the Dipole Inversion for MRI Susceptibility Mapping
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
DeepQSM - Using Deep Learning to Solve the Dipole Inversion for MRI Susceptibility Mapping
Kasper Gade Bøtker Rasmussen, Mads Kristensen, Rasmus Guldhammer Blendal, Lasse Riis Østergaard, Maciej Plocharski, Kieran O’Brien, Christian Langkammer, Andrew Janke, Markus Barth, Steffen Bollmann
bioRxiv 278036; doi: https://doi.org/10.1101/278036
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
DeepQSM - Using Deep Learning to Solve the Dipole Inversion for MRI Susceptibility Mapping
Kasper Gade Bøtker Rasmussen, Mads Kristensen, Rasmus Guldhammer Blendal, Lasse Riis Østergaard, Maciej Plocharski, Kieran O’Brien, Christian Langkammer, Andrew Janke, Markus Barth, Steffen Bollmann
bioRxiv 278036; doi: https://doi.org/10.1101/278036

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (3579)
  • Biochemistry (7524)
  • Bioengineering (5486)
  • Bioinformatics (20699)
  • Biophysics (10260)
  • Cancer Biology (7939)
  • Cell Biology (11584)
  • Clinical Trials (138)
  • Developmental Biology (6573)
  • Ecology (10144)
  • Epidemiology (2065)
  • Evolutionary Biology (13551)
  • Genetics (9502)
  • Genomics (12794)
  • Immunology (7887)
  • Microbiology (19456)
  • Molecular Biology (7618)
  • Neuroscience (41915)
  • Paleontology (307)
  • Pathology (1253)
  • Pharmacology and Toxicology (2181)
  • Physiology (3253)
  • Plant Biology (7010)
  • Scientific Communication and Education (1291)
  • Synthetic Biology (1942)
  • Systems Biology (5410)
  • Zoology (1108)