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PMID: 34328596 Published · aheadofprint English

Automatic triangulated mesh generation of pulmonary airways from segmented lung 3DCTs for computational fluid dynamics.

Lauria M, Singhrao K, Stiehl B, Low D, Goldin J, Barjaktarevic I, Santhanam A

Abstract

Computational fluid dynamics (CFD) of lung airflow during normal and pathophysiological breathing provides insight into regional pulmonary ventilation. By integrating CFD methods with 4D lung imaging workflows, regions of normal pulmonary function can be spared during treatment planning. To facilitate the use of CFD simulations in a clinical setup, a robust, automated, and CFD-compliant airway mesh generation technique is necessary. We define a CFD-compliant airway mesh to be devoid of blockages of airflow and leaks in the airway path, both of which are caused by airway meshing errors that occur when using conventional meshing techniques. We present an algorithm to create a CFD-compliant airway mesh in an automated manner. Beginning with a medial skeleton of the airway segmentation, the branches were tracked, and 3D points at which bifurcations occur were identified. Airway branches and bifurcation features were isolated to allow for automated and careful meshing that considered their anatomical nature. We present the meshing results from three state-of-the-art tools and compare them with the meshes generated by our algorithm. The results show that fully CFD-compliant meshes were automatically generated for an ideal geometry and patient-specific CT scans. Using an open-source smoothed-particle hydrodynamics CFD implementation, we compared the airflow using our approach and conventionally generated airway meshes. Our meshing algorithm was able to successfully generate a CFD-compliant mesh from pre-segmented lung CT scans, providing an automatic meshing approach that enables interventional CFD simulations to guide lung procedures such as radiotherapy or lung volume reduction surgery.

Keywords
3D triangulation Computational fluid dynamics Lung airways
Article Info
Journal
International journal of computer assisted radiology and surgery
Abbr.
Int J Comput Assist Radiol Surg
ISSN
1861-6429
Corresponding email
Published
2021-07-30
Language
English
Country/Region
Germany
NLM ID
101499225
Analysis Services
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