English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Validated reconstructions of geometries of nasal cavities from CT scans

MPS-Authors
/persons/resource/persons185097

Zwicker,  David
Max Planck Research Group Theory of Biological Fluids, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Zwicker, D., Yang, K., Melchionna, S., Brenner, M. P., Liu, B., & Lindsay, R. W. (2018). Validated reconstructions of geometries of nasal cavities from CT scans. Biomedical Physics and Engineering Express, 4(4): 045022. doi:10.1088/2057-1976/aac6af.


Cite as: https://hdl.handle.net/21.11116/0000-0001-6C71-7
Abstract
Developing validated objective measures of nasal airflow is paramount for improving the management
of nasal obstruction. Nasal airflow can be studied objectively by simulating the flow in the computer.
This requires a faithful reconstruction of the nasal geometry since the simulated airflow is sensitive to
small variations of the complex shape of the nasal cavity. We here show that altering the geometry by
less than 1 mm can change the airflow two-fold. We also show that a faithful reconstruction of the
nasal geometry is possible with a threshold-based segmentation. Utilizing the known geometry of a CT
phantom, we determine an optimal segmentation threshold of −450 HU. Changing this threshold by
100 HU alters the geometry by only about 0.1 mm. We use this verified segmentation to extract nasal
geometries of three patients and simulate the respective airflows using the Lattice Boltzmann method.
Using a simple model, we can predict how the reconstruction threshold affects the resistance to
airflow. Since the segmentation and the simulation can be automated completely, this is an important
step toward an objective analysis of nasal airflow based on CT scans.