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Figure 1: Nasal Anatomy. Olfactory Cilia Superior Turbinate Internal Naris Nasopharynx Nasal Valve Middle Turbinate Inferior Turbinate Nostril © Philip Wilson In Vitro Analysis of Airflow in the Human Nasal Cavity D. J. Taylor †*, D. J. Doorly †, R. C. Schroter * Departments of Aeronautics † and Bioengineering *, Imperial College London. Introduction The nose performs many physiological functions including heating, humidification and filtration of inspired air as well as providing one’s sense of smell. The healthy function of the nose is highly dependent on the fluid dynamic characteristics of the airflow through the nasal cavities. A better understanding in this area could allow significant advances in the fields of: toxicology, drug delivery, particle deposition and surgical planning. In vivo and in vitro methods of nasal assessment are compounded by: the complexity of the anatomy, shown in figure 1; the inaccessibility of the nasal passages; and the unsteady nature of nasal airflow, making assessment particularly suited to optical measurement techniques. Particle Image Velocimetry (PIV) and dye filament injection were applied to an anatomically accurate model of a patient specific nasal geometry, for different steady inspiratory flow rates. Model Creation 1. Segmentation: A twice scale computer model of one half of the nasal cavity, shown in figure 2, was constructed from 82 axially-acquired CT images. The dataset of overlapping 512 x 512 pixel images, which have a slice spacing and thickness of 0.7mm and 1.3mm, respectively, was segmented using commercially available software (Amira™, TGS, Europe). 2. Physical Model: A physical rapid-prototype of the geometry was created, in 0.076 mm layers, on a 3D printer (Z Printer © 310, Z Corporation) and sealed with a layer of water-soluble glue. 3. Silicone Model: This inverse model was placed in a acrylic box, which was filled with optical grade silicone (Sylgard 184 Silicone Elastomer, Dow Corning) and allowed to cure. The prototype was dissolved with water, leaving a clear block of silicone, containing the nasal geometry, as shown in figure 3. 4. Model Fidelity: To ensure the silicone model faithfully followed the original computer segmentation, the silicone phantom was rescanned using high resolution CT and segmented as before. Both geometries were registered and it was found that the mean distance between their surfaces was less than a pixel size of the original CT images (the displacements are shown as contours in figure 4). Results All figures depict a consistent lateral view of the nasal model, with flow entering from the right and exiting through the left of each figure. The depicted velocities and flow rates are scaled to match those found in the in vivo nose. 1. Dye filament injection: Neutrally buoyant dye filaments were injected directly upstream of the model’s nostril and captured using a colour digital camera and a high-speed monochromic CCD camera. Figures 5 and 6 depict laminar and disturbed laminar flow patterns, respectively, which are representative of quite restful breathing. It can be seen that the onset of transition occurs as the flow rate reaches and exceeds 115 ml/s in the real nose. Figure 7 is indicative of the types of complex flow structures encountered in the nose, even for laminar flow. 2. PIV: Images of the dynamic flow scene were captured at up to 462 frames per second using a high speed camera. Consecutive images were interrogated and the whole field velocities were resolved. Figure 8 depicts the PIV velocity measurements, superimposed on selected particle images. Each subfigure contains a blow up of the regions of interest and a landmark image to help orient the results. Conclusions The depicted results have proved essential to validate Computational Fluid Dynamics (CFD) simulations by enabling the selection of a flow solver which faithfully matches the in vivo airflow conditions. The dye filament injection provided a useful qualitative means of assessing the flow regime, locations of the onset of transition, recirculations and flow paths through the nose, while PIV provided a quantitative assessment of whole field velocity measurements throughout the nose. Figure 2: The segmented nasal anatomy is shown with respect to the CT images in Amira™. Figure 3: The stages of model creation: 1) rapid-prototype (left), 2) silicone casting (top) and 3) acrylic mould (bottom). Figure 5: Laminar flow, dye filament injection at 100 ml/s and Re = 890. Figure 6: Onset of transition, dye filament injection at 115 ml/s and Re = 1010. Figure 7: Onset of transition, dye filament injection at 115 ml/s and Re =1010. Figure 8: PIV velocity measurements, anticlockwise from top left: 1) olfactory slit, flow rate = 100 ml/s; 2) olfactory slit, flow rate = 115 ml/s; 3) nasal valve, flow rate = 115 ml/s and 4) inferior meatus flow rate = 115 ml/s. Figure 4: Surface displacement between the twice-scale computer model and the rescanned silicone phantom. Surface Displacement (mm)
Date of download: 9/19/2016 Copyright © ASME. All rights reserved. From: A Novel Method for Optical High Spatiotemporal Strain Analysis for Transcatheter.
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