Airway wall thickness assessment: a new functionality in virtual bronchoscopy investigation

Abstract

While classic virtual bronchoscopy offers visualization facilities for investigating the shape of the inner airway wall surface, it provides no information regarding the local thickness of the wall. Such information may be crucial for evaluating the severity of remodeling of the bronchial wall in asthma and to guide bronchial biopsies for staging of lung cancers. This paper develops a new functionality with the virtual bronchoscopy, allowing to estimate and map the information of the bronchus wall thickness on the lumen wall surface, and to display it as coded colors during endoluminal navigation. The local bronchus wall thickness estimation relies on a new automated 3D segmentation approach using strong 3D morphological filtering and model-fitting. Such an approach reconstructs the inner/outer airway wall surfaces from multi-detector CT data as follows. First, the airway lumen is segmented and its surface geometry reconstructed using either a restricted Delaunay or a Marching Cubes based triangulation approach. The lumen mesh is then locally deformed in the surface normal direction under specific force constraints which stabilize the model evolution at the level of the outer bronchus wall surface. The developed segmentation approach was validated with respect to both 3D mathematicallysimulated image phantoms of bronchus-vessel subdivisions and to state-of-the-art cross-section area estimation techniques when applied to clinical data. The investigation in virtual bronchoscopy mode is further enhanced by encoding the local wall thickness at each vertex of the lumen surface mesh and displaying it during navigation, according to a specific color map.