Quantitative airway analysis in longitudinal studies using groupwise registration and 4D optimal surfaces

Area of Science:

• Medical imaging analysis
• Pulmonary disease research
• Computational anatomy

Background:

• Assessing airway wall surface changes in computed tomography (CT) is crucial for understanding diseases like chronic obstructive pulmonary disease (COPD).
• Existing methods often segment airways at individual time points, limiting comprehensive regional change analysis.
• Challenges include aggregating data per airway generation or matching branches for accurate assessment.

Purpose of the Study:

• To develop an integrated 4D optimal surface segmentation method for simultaneous analysis of airway surfaces across multiple time points.
• To improve the quantification of local airway wall changes in diseases like COPD.
• To enable locally matched measurements across the entire airway surface over time.

Main Methods:

• A subject-specific groupwise space was utilized to analyze multiple time points simultaneously.
• 4D optimal surface segmentation was applied to integrate information from all time points.
• Measurements were matched locally at any position on the resulting airway surfaces.

Main Results:

• Visual inspection of 10 subjects indicated increased airway tree length compared to current methods, with minimal increase in false positives.
• Large-scale analysis of 374 subjects (1870 images) demonstrated significant correlation with lung function.
• The method exhibited high reproducibility of measurements.

Conclusions:

• The proposed 4D optimal surface segmentation method offers a more comprehensive approach to analyzing airway changes over time.
• This technique shows promise for improved quantification of disease progression in COPD and other airway diseases.
• The findings support the method's utility in large-scale studies and clinical applications due to its accuracy and reproducibility.