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一种用于预测呼吸器适配的联合成像、变形和配准方法。

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

机构信息

Clinical Academic Facility, School of Health Sciences, University of Southampton, Southampton, United Kingdom.

School of Engineering, Cardiff University, Cardiff, United Kingdom.

出版信息

PLoS One. 2022 Nov 11;17(11):e0277570. doi: 10.1371/journal.pone.0277570. eCollection 2022.

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.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db52/9651551/2db058dbce7c/pone.0277570.g001.jpg

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