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

Steffen Bollmann, Kasper Gade Bøtker Rasmussen, Mads Kristensen, Rasmus Guldhammer Blendal, Lasse Riis Østergaard, Maciej Plocharski, Kieran O'Brien, Christian Langkammer, Andrew Janke, Markus Barth

Research output: Contribution to journalJournal articleResearchpeer-review

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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.

Original languageEnglish
JournalNeuroImage
Volume195
Pages (from-to)373-383
Number of pages11
ISSN1053-8119
DOIs
Publication statusPublished - Jul 2019

Bibliographical note

Copyright © 2019 Elsevier Inc. All rights reserved.

Keywords

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

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