Development and Analysis of Patient-Based Complete Conducting Airways Models

Area of Science:

• Pulmonary Medicine
• Computational Biology
• Medical Imaging

Background:

• High-resolution computed tomography (CT) analysis of lung airways is limited by inter-subject geometric variations.
• Current CT-based models often only analyze central airways (generations 6-10), missing crucial small airway information relevant to asthma and COPD.
• Algorithmic approaches can extrapolate CT data to create complete airway trees down to the acinar level for biomedical research.

Purpose of the Study:

• To develop and apply an image analysis and modeling pipeline for creating complete, patient-specific airway models.
• To analyze the morphometric properties of these airway models.
• To assess the suitability of these models for patient-specific computational studies by comparing airway resistance predictions with clinical lung function measures.

Main Methods:

• An image analysis and modeling pipeline was developed to generate complete airway trees from CT scans.
• The pipeline was applied to CT scans from healthy (n=11) and asthmatic (n=24) patients, creating models down to the acinar level (mean terminal generation 15.8 ± 0.47).
• Morphometric properties were analyzed, and airway resistance predictions were compared with global clinical lung function measures like forced expiratory volume in one second (FEV1) and FEV1/FVC.

Main Results:

• Complete patient-based airway models were successfully generated, consistent with previous research.
• Significant differences (p < 0.01) in airway resistance were observed at all flow rates between severe asthmatics (GINA 3-5) and healthy subjects.
• Model predictions of airway resistance correlated significantly with FEV1 (Spearman ρ = -0.65, p < 0.001) and FEV1/FVC at low flow rates (ρ = -0.58, p < 0.001).

Conclusions:

• The developed pipeline and anatomical models are suitable for mechanistic modeling studies.
• These patient-specific airway models can serve as a foundation for future personalized medicine approaches in respiratory research.
• The findings highlight the utility of detailed airway modeling in understanding the impact of airway geometry on lung function and disease.