Automatic construction of subject-specific human airway geometry including trifurcations based on a CT-segmented airway skeleton and surface

Abstract

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.

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Acknowledgments

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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Correspondence to Ching-Long Lin.

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This work was supported in part by NIH grants U01-HL114494, R01-HL094315, R01-HL112986, and S10-RR022421.

Appendix

Appendix

Identification of associated bifurcations for direction of v-axis

The direction of v-axis is defined as follows. If the branch is not a child branch of the previous bifurcation (red line and circle in Fig. 16a), i.e., the trachea, the nearest bifurcation in the positive CL direction (red arc in Fig. 16a) is associated. If the branch is a child branch of the previous bifurcation as well as the parent branch of the following bifurcation (thick black line and circle in Fig. 16b), both bifurcations (black arcs in Fig. 16b) are associated. If there is no bifurcation in the positive CL direction (blue line and circle in Fig. 16a), i.e., ending branches, the nearest bifurcation in the negative CL direction (blue arc in Fig. 16a) is associated.

Fig. 16
figure16

The bifurcations that are associated with branches to determine the direction of v-axis in OXYZ at: b a branch that does not have neighboring bifurcations in the negative centerline (CL) direction (red), a branch that does not have neighboring bifurcations in the positive CL direction (blue), and c a branch that does have neighboring bifurcations in both negative and positive CL directions (black). Solid lines denote the branches of interest, circles denote the rings at the distal end of branches, and arcs denote the bifurcations associated with the branches

Coordinates of crux nodes around trifurcations

At a fork-type trifurcation, the coordinates of \(P_5\) and \(P_6\), or \(P_7\), are determined by the two nearest child branches. At a tripod-type trifurcation, e.g., whose child branches are right, left-front, and left-back child branches, the coordinates of \(P_7\) are determined by the left-front and left-back child branches. The coordinates of \(P_5\) are the averages of two intersections of the child branches: the one between right and left-front child branches (the curve \(P_2 P_5\) in Fig. 6a) and the one between right and left-back child branches (the curve \(P_4 P_5\) in Fig. 6d).

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Miyawaki, S., Tawhai, M.H., Hoffman, E.A. et al. Automatic construction of subject-specific human airway geometry including trifurcations based on a CT-segmented airway skeleton and surface. Biomech Model Mechanobiol 16, 583–596 (2017). https://doi.org/10.1007/s10237-016-0838-6

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Keywords

  • Visualization
  • Simulation
  • Geometric fitting
  • Computed tomography