Scientific article
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Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI

Published inEuropean Journal of Nuclear Medicine and Molecular Imaging, vol. 46, no. 13, p. 2746-2759
Publication date2019
Abstract

Quantitative PET/MR imaging is challenged by the accuracy of synthetic CT (sCT) generation from MR images. Deep learning-based algorithms have recently gained momentum for a number of medical image analysis applications. In this work, a novel sCT generation algorithm based on deep learning adversarial semantic structure (DL-AdvSS) is proposed for MRI-guided attenuation correction in brain PET/MRI.

Keywords
  • Attenuation correction
  • Brain imaging
  • Deep learning
  • PET/MRI
  • Quantitative imaging
NoteMerci de mettre à jour le pdf avec le fichier suivant:https://www.hug-ge.ch/sites/interhug/files/structures/pinlab/documents/ejnmmi2019_petmri.pdf
Funding
  • Swiss National Science Foundation - SNRF 320030_176052
  • Autre - Swiss Cancer Research Foundation : Grant KFS-3855-02-2016
Citation (ISO format)
ARABI, Hossein et al. Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI. In: European Journal of Nuclear Medicine and Molecular Imaging, 2019, vol. 46, n° 13, p. 2746–2759. doi: 10.1007/s00259-019-04380-x
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Article (Published version)
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Identifiers
ISSN of the journal1619-7070
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Creation05/07/2019 12:02:00
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Update time15/03/2023 17:53:52
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