Quantitative Accuracy of CT Protocols for Cross-sectional and Longitudinal Assessment of COPD: A Virtual Imaging Study.

Chronic obstructive pulmonary disease (COPD), encompassing chronic bronchitis and emphysema, requires precise quantification through CT imaging to accurately assess disease severity and progression. However, inconsistencies in imaging protocols often lead to unreliable measurements. This study aims to optimize CT acquisition and reconstruction protocols for cross-sectional and longitudinal CT measurements of COPD using a virtual (in-silico) imaging framework. We developed human models at various stages of emphysema and bronchitis, informed by the COPDGene cohort. The specifications of a clinical CT scanner (Aquilion ONE Prism, Canon Medical Systems) were integrated into a CT simulator. This simulation framework was validated against experimental data. The analysis focused on the impact of tube current and kernel sharpness on two COPD biomarkers: LAA-950 (percentage of lung voxels with attenuation less than -950 HU) and Pi10 (the square root of the wall area around an airway with an internal perimeter of 10 mm) and mean absolute error (MAE; a voxel-wise error metric for emphysema density measurements). The increase in dose level showed minimal impact on the Pi10 measurements, but affected the LAA-950, with a reduction in variability observed at higher dose levels. Increasing kernel sharpness introduced variability in the LAA-950 and Pi10 measurements and higher MAE with sharper kernels. Longitudinal analysis demonstrated that kernel sharpness contributed more to variability in the COPD biomarker measurements over time compared to dose level. Similarly, cross-sectional assessments showed that an increase in MAE, while a decrease in Pi10 measurement error with sharper kernels. The study underlines the need for standardized task-specific imaging protocols to enhance the reliability and accuracy of COPD assessments, thus improving diagnostic precision and patient assessments.

Duke Scholars

Author Ehsan Abadi Radiology