A combined imaging, deformation and registration methodology for predicting respirator fitting

Abdullah, Johari Yap, Caggiari, Silvia, Keenan, Bethany ORCID: https://orcid.org/0000-0001-7787-2892, Bader, Dan L., Mavrogordato, Mark N., Rankin, Kathryn, Evans, Sam L. ORCID: https://orcid.org/0000-0003-3664-2569 and Worsley, Peter R. 2022. A combined imaging, deformation and registration methodology for predicting respirator fitting. PLoS ONE 17 (11) , e0277570. 10.1371/journal.pone.0277570

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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.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Schools > Engineering
Publisher:	Public Library of Science
ISSN:	1932-6203
Date of First Compliant Deposit:	14 November 2022
Date of Acceptance:	30 October 2022
Last Modified:	04 May 2023 16:15
URI:	https://orca.cardiff.ac.uk/id/eprint/154135

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