Quantifying atrial anatomy uncertainty from clinical data and its impact on electro-physiology simulation predictions

Corrado, C., Razeghi, O., Roney, C. et al. (8 more authors) (2020) Quantifying atrial anatomy uncertainty from clinical data and its impact on electro-physiology simulation predictions. Medical Image Analysis, 61. 101626. ISSN 1361-8415

Abstract

Metadata

Authors/Creators:
Copyright, Publisher and Additional Information: © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/)
Keywords: Uncertainty Quantification; Cardiac models; Principal Component Analysis; Medical Image Processing
Dates:
  • Accepted: 5 December 2019
  • Published (online): 12 December 2019
  • Published: April 2020
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield)
Funding Information:
FunderGrant number
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC)EP/P010741/1
Depositing User: Symplectic Sheffield
Date Deposited: 11 Dec 2019 15:05
Last Modified: 12 Feb 2020 14:41
Status: Published
Publisher: Elsevier
Refereed: Yes
Identification Number: https://doi.org/10.1016/j.media.2019.101626

Share / Export

Statistics