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A combined imaging, deformation and registration methodology for predicting respirator fitting
Silvia Caggiari1, Bethany Keenan2, Dan L Bader1
1Clinical Academic Facility, School of Health Sciences, University of Southampton, Southampton, United Kingdom.
Plos One
|November 11, 2022
Summary
This study introduces a new method using MRI and 3D registration to assess how well N95/FFP3 respirators fit diverse facial structures. The findings aim to improve respirator design for better protection across all users.
Area of Science:
- Medical Imaging
- Biomedical Engineering
- Public Health
Background:
- N95/FFP3 respirators are crucial for preventing COVID-19 transmission.
- Limited respirator size and geometry cause fitting issues, particularly for specific genders and ethnicities.
Purpose of the Study:
- To develop and validate a novel methodology for predicting respirator fit.
- To assess the association between facial anthropometrics and respirator fitting.
Main Methods:
- Combined magnetic resonance imaging (MRI) with and without respirators in situ.
- Employed a 3D registration algorithm to predict respirator fit.
- Utilized sensitivity analysis to optimize deformation values and validate against soft tissue displacement.
Main Results:
- Developed a novel methodology combining MRI and 3D registration for respirator fit prediction.
- Optimized deformation values for respirator-face interactions.
- Assessed the relationship between predicted fit and facial anthropometrics in a cohort of 8 individuals.
Conclusions:
- The novel methodology shows promise for evaluating respirator fit.
- Understanding facial anthropometrics can help improve respirator design and fit for diverse populations.
- Further research is needed to validate findings in larger, more diverse cohorts.
