Caggiari, S. orcid.org/0000-0002-8928-2141, Keenan, B., Bader, D.L. et al. (4 more authors) (2022) A combined imaging, deformation and registration methodology for predicting respirator fitting. PLoS ONE, 17 (11). e0277570. ISSN 1932-6203
Abstract
N95/FFP3 respirators have been critical to protect healthcare workers and their patients from the transmission of COVID-19. However, these respirators are characterised by a limited range of size and geometry, which are often associated with fitting issues in particular sub-groups of gender and ethnicities. This study describes a novel methodology which combines magnetic resonance imaging (MRI) of a cohort of individuals (n = 8), with and without a respirator in-situ, and 3D registration algorithm which predicted the goodness of fit of the respirator. Sensitivity analysis was used to optimise a deformation value for the respirator-face interactions and corroborate with the soft tissue displacements estimated from the MRI images. An association between predicted respirator fitting and facial anthropometrics was then assessed for the cohort.
Metadata
Item Type: | Article |
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Copyright, Publisher and Additional Information: | © 2022 Caggiari et al. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Biomedical Sciences (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Oct 2024 10:04 |
Last Modified: | 07 Oct 2024 10:04 |
Status: | Published |
Publisher: | Public Library of Science |
Identification Number: | 10.1371/journal.pone.0277570 |
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Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:217983 |