Assessment of ventilation heterogeneity and particle deposition in asthmatics using combined SPECT/CT imaging and computational modeling approaches.

Objective: This study investigated asthma phenotypes and their associations with ventilation heterogeneity and particle deposition by utilizing Single-Photon Emission Computed Tomography (SPECT) imaging, quantitative Computed Tomography (qCT) imaging-based subgrouping, and a whole-lung computational model.

Methods: Two datasets were analyzed: one from a combined SPECT and CT (SPECT/CT) study with six asthmatic subjects, and another from the Severe Asthma Research Program (SARP) with 209 asthmatic subjects. Data from 35 previously acquired healthy subjects served as a control group. Each subject underwent CT scans at full inspiration and expiration, along with pulmonary function testing (PFT). The SPECT/CT study included ventilation SPECT imaging. Key qCT variables such as airway diameter, wall thickness, percentage of air trapping (AirT%), and percentage of small airway disease (fSAD%) were assessed. A subject-specific whole-lung computational fluid and particle dynamics (CFPD) model predicted airway resistance, particle deposition fraction, and the coefficient of variation (CV) for ventilation heterogeneity. Subjects were categorized into four predefined asthma imaging subgroups/clusters with increasing severity (C1-C4). CFPD-predicted CVs were validated against SPECT measurements. We compared PFT, qCT, and CFPD variables across SARP clusters and analyzed particle deposition fractions in large conducting, small conducting, and respiratory airways.

Results: Cluster C4 exhibited a significantly distinct ventilation profile compared to other clusters and health controls. This distinction contrasted with the insignificant differences between ventilation profiles in severity subgroups defined by conventional spirometry-based guidelines. Airway resistance varied significantly across the asthma clusters. Although both C3 and C4 clusters represented severe asthma, only C4 showed a significant increase in AirT%, primarily due to fSAD%. Since inflammatory phenotypes differ - C3 with wall thickening in large and small conducting airways, and C4 with elevated fSAD% and Emph% in small conducting and respiratory airways - fine particles (∼5 μm) and extrafine particles (∼1 μm) are more effective at reaching the respective regions in C3 and C4. Given that C2 and C4 have hyper-responsive phenotypes with narrowed conducting airways, fine particles are more effective in reaching these areas. Airway enlargement in targeted segments of the left lower lobe resulted in improved particle deposition.

Conclusions: Our cluster-informed CFPD-based approach enhances the understanding of ventilation heterogeneity in asthma and holds potential for refining strategies for inhalational therapies.