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

15 Citations (Scopus)
94 Downloads (Pure)
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

Fingerprint Dive into the research topics of 'DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping'. Together they form a unique fingerprint.

Cite this