CT imaging in COPD: towards functional parameters

COPD (Chronic Obstructive Pulmonary Disease) is a progressive lung disease characterized by irreversible airway obstruction, often caused by smoking and air pollution. The disease leads to chronic inflammation, emphysema, and hyperinflation, resulting in breathing difficulties. Diagnosis is performed using spirometry, while disease severity is classified according to the GOLD guidelines.
Lung volume reduction (LVR) is a treatment option for severe COPD, in which damaged lung areas are removed or sealed to reduce hyperinflation. High-resolution CT (HRCT) plays a crucial role in selecting suitable patients but is currently used mainly qualitatively. This dissertation investigates how quantitative CT analysis can improve lung function assessment and treatment planning.
This thesis demonstrates that CT-based lung volume measurements strongly correlate with body plethysmography measurements, particularly when spirometry-guided scans are used. Additionally, a reference framework for lobe volumes is established, aiding in selecting the optimal lobe for LVR. Furthermore, the effect of different CT reconstruction kernels on emphysema scoring is evaluated, and the use of various Hounsfield Unit thresholds to assess air trapping is explored. Lastly, a method is developed to quantify diaphragm flattening, showing that LVR has a positive effect on diaphragm function.