A semi-automatic framework of measuring pulmonary arterial metrics at anatomic airway locations using CT imaging

Pulmonary vascular dysfunction has been implicated in smoking-related susceptibility to emphysema. With the growing interest in characterizing arterial morphology for early evaluation of the vascular role in pulmonary diseases, there is an increasing need for the standardization of a framework for arterial morphological assessment at airway segmental levels. In this paper, we present an effective and robust semi-automatic framework to segment pulmonary arteries at different anatomic airway branches and measure their cross-sectional area (CSA). The method starts with user-specified endpoints of a target arterial segment through a custom-built graphical user interface. It then automatically detect the centerline joining the endpoints, determines the local structure orientation and computes the CSA along the centerline after filtering out the adjacent pulmonary structures, such as veins or airway walls. Several new techniques are presented, including collision-impact based cost function for centerline detection, radial sample-line based CSA computation, and outlier analysis of radial distance to subtract adjacent neighboring structures in the CSA measurement. The method was applied to repeat-scan pulmonary multirow detector CT (MDCT) images from ten healthy subjects (age: 21-48 Yrs, mean: 28.5 Yrs; 7 female) at functional residual capacity (FRC). The reproducibility of computed arterial CSA from four airway segmental regions in middle and lower lobes was analyzed. The overall repeat-scan intra-class correlation (ICC) of the computed CSA from all four airway regions in ten subjects was 96% with maximum ICC found at LB10 and RB4 regions.