Automatic construction of subject-specific human airway geometry including trifurcations based on a CT-segmented airway skeleton and surface
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We propose a method to construct three-dimensional airway geometric models based on airway skeletons, or centerlines (CLs). Given a CT-segmented airway skeleton and surface, the proposed CL-based method automatically constructs subject-specific models that contain anatomical information regarding branches, include bifurcations and trifurcations, and extend from the trachea to terminal bronchioles. The resulting model can be anatomically realistic with the assistance of an image-based surface; alternatively a model with an idealized skeleton and/or branch diameters is also possible. This method systematically identifies and classifies trifurcations to successfully construct the models, which also provides the number and type of trifurcations for the analysis of the airways from an anatomical point of view. We applied this method to 16 normal and 16 severe asthmatic subjects using their computed tomography images. The average distance between the surface of the model and the image-based surface was 11 % of the average voxel size of the image. The four most frequent locations of trifurcations were the left upper division bronchus, left lower lobar bronchus, right upper lobar bronchus, and right intermediate bronchus. The proposed method automatically constructed accurate subject-specific three-dimensional airway geometric models that contain anatomical information regarding branches using airway skeleton, diameters, and image-based surface geometry. The proposed method can construct (i) geometry automatically for population-based studies, (ii) trifurcations to retain the original airway topology, (iii) geometry that can be used for automatic generation of computational fluid dynamics meshes, and (iv) geometry based only on a skeleton and diameters for idealized branches.
KeywordsVisualization Simulation Geometric fitting Computed tomography
The authors are grateful to the Severe Asthma Research Project (SARP) for assisting with the acquisition of computed tomography data and Sanghun Choi for his comments on the present study. We also thank the San Diego Supercomputer Center (SDSC), the Texas Advanced Computing Center (TACC), and Extreme Science and engineering Discovery Environment (XSEDE) sponsored by the National Science Foundation for the computational time.
Conflict of interest
This work was supported in part by NIH grants U01-HL114494, R01-HL094315, R01-HL112986, and S10-RR022421. Eric A. Hoffman is a shareholder in VIDA diagnostics that is commercializing lung image analysis software derived by the University of Iowa lung imaging group. He is also a member of the Siemens CT advisory board. Shinjiro Miyawaki, Merryn H. Tawhai, Sally E. Wenzel, and Ching-Long Lin declare that they have no conflict of interest.
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