Modeling Realistic Geometries in Human Intrathoracic Airways

• Published in: Diagnostics (Basel) Vol. 14; no. 17; p. 1979
• Main Authors: Pennati, Francesca, Aliboni, Lorenzo, Aliverti, Andrea
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 07.09.2024
• Subjects: airways; Asymmetry; Chronic obstructive pulmonary disease; Clinical medicine; Fractals; geometrical models; Geometry; image processing; imaging; Lung diseases; Magnetic resonance imaging; Mathematical models; Medical diagnosis; MRI; image processing; CT; deep learning; MRI; pulmonary disease; airways; segmentation; biomechanical models; imaging; geometrical models
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics14171979

Geometrical models of the airways offer a comprehensive perspective on the complex interplay between lung structure and function. Originating from mathematical frameworks, these models have evolved to include detailed lung imagery, a crucial enhancement that aids in the early detection of morphological changes in the airways, which are often the first indicators of diseases. The accurate representation of airway geometry is crucial in research areas such as biomechanical modeling, acoustics, and particle deposition prediction. This review chronicles the evolution of these models, from their inception in the 1960s based on ideal mathematical constructs, to the introduction of advanced imaging techniques like computerized tomography (CT) and, to a lesser degree, magnetic resonance imaging (MRI). The advent of these techniques, coupled with the surge in data processing capabilities, has revolutionized the anatomical modeling of the bronchial tree. The limitations and challenges in both mathematical and image-based modeling are discussed, along with their applications. The foundation of image-based modeling is discussed, and recent segmentation strategies from CT and MRI scans and their clinical implications are also examined. By providing a chronological review of these models, this work offers insights into the evolution and potential future of airway geometry modeling, setting the stage for advancements in diagnosing and treating lung diseases. This review offers a novel perspective by highlighting how advancements in imaging techniques and data processing capabilities have significantly enhanced the accuracy and applicability of airway geometry models in both clinical and research settings. These advancements provide unique opportunities for developing patient-specific models.