Clinical Academic Facility, School of Health Sciences, University of Southampton, Southampton, United Kingdom.
School of Engineering, Cardiff University, Cardiff, United Kingdom.
PLoS One. 2022 Nov 11;17(11):e0277570. doi: 10.1371/journal.pone.0277570. eCollection 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.
N95/FFP3 呼吸器对于保护医护人员及其患者免受 COVID-19 传播至关重要。然而,这些呼吸器的尺寸和几何形状有限,这往往与特定性别和种族亚组的适配问题有关。本研究描述了一种新的方法,该方法结合了一组个体的磁共振成像 (MRI)(n=8),以及有和无呼吸器在位的情况,以及 3D 配准算法,该算法预测了呼吸器的适配性。通过敏感性分析,优化了用于呼吸器-面部相互作用的变形值,并与从 MRI 图像估计的软组织位移进行了印证。然后,评估了该队列中预测的呼吸器适配与面部人体测量学之间的关联。