Abstract
INTRODUCTION: Quantitative computed tomography (qCT) provides detailed spatial assessments of lung structure and function, while electrical impedance tomography (EIT) offers high temporal resolution for analyzing breathing patterns but lacks structural detail. This study investigates the correlation between qCT-based spatial variables and EIT-based temporal signals to elucidate the physiological relationships between these two modalities. METHODS: Six participants with asthma underwent pulmonary function tests (PFTs) before and after bronchodilator inhalation. CT scans were obtained in full inhalation and normal exhalation, providing airway hydraulic luminal diameter (D (h)), airway wall thickness, and percent emphysema, and percent functional small airway disease (fSAD%). On the same day, EIT imaging was performed during tidal breathing, measuring impedance in three different anatomical regions on the chest wall (upper, middle, and lower). The EIT-based impedance was transformed using fast Fourier transform (FFT) to separate perfusion and respiration components including high and low frequencies. RESULTS: High-frequency EIT values in the upper lungs were associated with a decline of lung function in pre-bronchodilator. Skewness values, measured by an imbalance between exhalation and inhalation, was elevated in the upper lungs and correlated with impaired lung function. Besides, D (h) decreased with a higher expiration-to-inspiration (E:I) ratio in the upper lungs and middle lungs. Functionally, higher high frequency values and lower low frequency values in the upper lungs correlated with greater emphysema and functional small airway disease, accompanied by reduced ventilation deformation metrics. Similarly, increased hysteresis variables (e.g., E:I, skewness) in the upper and middle lungs were associated with a further decrease in ventilation deformation metrics. CONCLUSION: EIT temporal signals demonstrated significant associations with spatial metrics from CT images, as well as PFTs metrics. A frequency analysis of EIT may enhance diagnostic approaches and improve understanding of respiratory mechanics in subjects with asthma.