A combined imaging, deformation and registration methodology for predicting respirator fitting.
Silvia CaggiariBethany E KeenanDan L BaderMark N MavrogordatoKathryn RankinSam L EvansPeter R WorsleyPublished in: PloS one (2022)
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.
Keyphrases
- magnetic resonance imaging
- contrast enhanced
- soft tissue
- deep learning
- coronavirus disease
- ejection fraction
- diffusion weighted imaging
- sars cov
- computed tomography
- newly diagnosed
- machine learning
- prognostic factors
- high resolution
- mental health
- magnetic resonance
- convolutional neural network
- mass spectrometry
- patient reported