In the framework of clinical respiratory investigation, providing accurate modalities for morpho-functional analysis is essential for diagnosis improvement, surgical planning and follow-up. This paper focuses on the upper airways investigation and develops an automated approach for 3D mesh reconstruction from MDCT acquisitions. In order to overcome the difficulties related to the complex morphology of the upper airways and to the image gray level heterogeneity of the airway lumens and thin cartilaginous septa, the proposed 3D reconstruction methodology combines 2D segmentation and 3D surface regularization approaches. The segmentation algorithm relies on mathematical morphology theory and provides robust discrimination of the airway lumen from the surrounding tissues, while preserving the connectivity relationship between the different anatomical structures. The 3D regularization step uses an energy-based modeling in order to achieve a smooth and well-fitted 3D surface of the upper airways. An accurate 3D mesh representation of the reconstructed airways makes it possible to develop specific clinical applications such as virtual endoscopy, surgical planning and computer assisted intervention. In addition, building up patient-specific 3D models of upper airways is highly valuable for the study and design of inhaled medication delivery via computational fluid dynamics (CFD) simulations.
In the framework of computer-aided diagnosis, pulmonary airway investigation based on multi-detector computerized tomography (MDCT) requires the development of specific tools for data interaction and analysis. The 3D segmentation of the bronchial tree provides radiologists with appropriate examination modalities such as CT bronchography, for a global analysis, or virtual endoscopy, for a local endoluminal diagnosis. Focusing on the latter modality, this paper proposes a set of advanced navigation and investigation tools based on the automatic extraction of the central axis (CA) of the 3D segmented airways. In the case of complex branching structures, such as the bronchial tree, the automatic CA computation is a challenging problem raising several difficulties related to geometry and topology preservation. In this respect, an original approach is presented, combining 3D distance map information and geodesic front propagation in order to accurately detect branching points and to preserve the original 3D topology of the airways, irrespective to both caliber variability with the bronchial order and to bronchial wall irregularities. The CA information is represented as a multi-valued and hierarchic tree structure, making possible automatic trajectory computation between two given points, bronchial caliber estimation in the plane orthogonal to the bronchus axis at a given location, branch indexation, and so on. These applications are illustrated on clinical data including both normal and pathological airway morphologies.