Gas flow Magnetic Resonance Imaging of the upper airway and Computational Fluid Dynamic modelling of airflow during Above Cuff Vocalisation
- Academic lead
- Dr Zinedine Khatir, School of Mechanical Engineering, Z.Khatir@leeds.ac.uk
- Industrial lead
- Sam Rigby, Atos Medical, sam.rigby@atosmedical.com
- Co-supervisor(s)
- Dr Claire Mills, School of Medicine, Leeds Teaching Hospitals NHS Trust, C.S.Mills@leeds.ac.uk, Professor Jim Wild, School of Medicine and Population Health, University of Sheffield, j.m.wild@sheffield.ac.uk (External)
- Project themes
- Computational & Analytical Tools, Experimental Techniques, Health
Above Cuff Vocalisation (ACV) involves the external application of an airflow (oxygen or medical air) via the subglottic port of a tracheostomy tube (Fig.1). This airflow exits above the inflated tracheostomy cuff, or balloon, and passes through the larynx (voice box) providing the potential for patients to be able to vocalise.
Figure 1: Above cuff vocalisation: dark blue arrow indicates where airflow is applied and white arrows show airflow movement through the voice box and oral cavity (Mills et al., 2023)
There is a lack of evidence or guidance for the specific airflow application. Furthermore, healthcare professionals have raised concerns about the application of high flow rates or application during swallowing, when there is nowhere for the air to escape. The primary research objective for this project is to provide clinical guidance for safe and effective airflow delivery during ACV with different tracheostomy tube designs and sizes.
This project will entail using gas flow MRI imaging of healthy individuals and patients (Fig.2) with a tracheostomy to develop and validate a CFD model of the airway (Fig.3). This model will then be used to evaluate the fluid dynamics of ACV under different conditions to provide specific clinical guidelines for ACV airflow application.
Figure 2: Images of HP xenon gas velocity in inspired air in the upper airways (Collier & Wild, 2015)
Figure 3: a) Geometry extracted from 4D-Flow MRI data and b) Mesh generated for CFD (Cherry and Khatir et al., 2022)