A dual-channel deep learning approach for lung cavity estimation from hyperpolarized gas and proton MRI

Astley, J.R., Biancardi, A.M., Marshall, H. orcid.org/0000-0002-7425-1449 et al. (7 more authors) (2022) A dual-channel deep learning approach for lung cavity estimation from hyperpolarized gas and proton MRI. Journal of Magnetic Resonance Imaging. ISSN 1053-1807

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2022 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)
Keywords: deep learning; segmentation; pulmonary MRI; hyperpolarized gas MRI
Dates:
  • Accepted: 24 October 2022
  • Published (online): 14 November 2022
  • Published: 14 November 2022
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Sheffield Teaching Hospitals
Funding Information:
FunderGrant number
MEDICAL RESEARCH COUNCILMR/M008894/1
NIHR AcademyNIHR-RP-R3-12-027
EUROPEAN COMMISSION - HORIZON 2020116106
Depositing User: Symplectic Sheffield
Date Deposited: 22 Nov 2022 10:03
Last Modified: 22 Nov 2022 10:03
Status: Published online
Publisher: Wiley
Refereed: Yes
Identification Number: https://doi.org/10.1002/jmri.28519
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