Convolutional neural networks for direct inference of pharmacokinetic parameters: Application to stroke dynamic contrast-enhanced MRI

Ulas, C., Das, D., Thrippleton, M.J. et al. (5 more authors) (2019) Convolutional neural networks for direct inference of pharmacokinetic parameters: Application to stroke dynamic contrast-enhanced MRI. Frontiers in Neurology, 9. 1147. ISSN 1664-2295

Abstract

Metadata

Authors/Creators:
  • Ulas, C.
  • Das, D.
  • Thrippleton, M.J.
  • Valdes Hernandez, M.D.C.
  • Armitage, P.A.
  • Makin, S.D.
  • Wardlaw, J.M.
  • Menze, B.H.
Copyright, Publisher and Additional Information: © 2019 Ulas, Das, Thrippleton, Valdés Hernández, Armitage, Makin, Wardlaw and Menze. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. http://creativecommons.org/licenses/by/4.0/
Keywords: dynamic contrast enhanced MRI; pharmacokinetic parameter inference; convolutional neural networks; ischaemic stroke; tracer kinetic modeling; contrast agent concentration; loss function
Dates:
  • Accepted: 11 December 2018
  • Published (online): 8 January 2019
  • Published: 8 January 2019
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 14 Mar 2019 12:48
Last Modified: 14 Mar 2019 16:01
Status: Published
Publisher: Frontiers Media
Refereed: Yes
Identification Number: https://doi.org/10.3389/fneur.2018.01147
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