Fully automated Bayesian analysis for quantifying the extent and distribution of pulmonary perfusion changes on CT pulmonary angiography in CTEPH

Abstract

Objectives This work aimed to develop an automated method for quantifying the distribution and severity of perfusion changes on CT pulmonary angiography (CTPA) in patients with chronic thromboembolic pulmonary hypertension (CTEPH) and to assess their associations with clinical parameters and expert annotations. Materials and methods Following automated segmentation of the chest, a machine-learning model assuming three distributions of attenuation in the pulmonary parenchyma (hyperemic, normal, and oligemic) was fitted to the attenuation histogram of CTPA images using Bayesian analysis. The proportion of each component, its spatial heterogeneity (entropy), and center-to-periphery distribution of the attenuation were calculated and correlated with the findings on CTPA semi-quantitatively evaluated by radiologists and with clinical function tests. Results CTPA scans from 52 patients (mean age, 65.2 +/- 13.0 years; 27 men) diagnosed with CTEPH were analyzed. An inverse correlation was observed between the proportion of normal parenchyma and brain natriuretic propeptide (proBNP, rho = -0.485, p = 0.001), mean pulmonary arterial pressure (rho = -0.417, p = 0.002) and pulmonary vascular resistance (rho = -0.556, p < 0.0001), mosaic attenuation (rho = -0.527, p < 0.0001), perfusion centralization (rho = -0.489, p = < 0.0001), and right ventricular diameter (rho = -0.451, p = 0.001). The entropy of hyperemic parenchyma showed a positive correlation with the pulmonary wedge pressure (rho = 0.402, p = 0.003). The slope of center-to-periphery attenuation distribution correlated with centralization (rho = -0.477, p < 0.0001), and with proBNP (rho = -0.463, p = 0.002). Conclusion This study validates an automated system that leverages Bayesian analysis to quantify the severity and distribution of perfusion changes in CTPA. The results show the potential of this method to support clinical evaluations of CTEPH by providing reproducible and objective measures.Objectives This work aimed to develop an automated method for quantifying the distribution and severity of perfusion changes on CT pulmonary angiography (CTPA) in patients with chronic thromboembolic pulmonary hypertension (CTEPH) and to assess their associations with clinical parameters and expert annotations. Materials and methods Following automated segmentation of the chest, a machine-learning model assuming three distributions of attenuation in the pulmonary parenchyma (hyperemic, normal, and oligemic) was fitted to the attenuation histogram of CTPA images using Bayesian analysis. The proportion of each component, its spatial heterogeneity (entropy), and center-to-periphery distribution of the attenuation were calculated and correlated with the findings on CTPA semi-quantitatively evaluated by radiologists and with clinical function tests. Results CTPA scans from 52 patients (mean age, 65.2 +/- 13.0 years; 27 men) diagnosed with CTEPH were analyzed. An inverse correlation was observed between the proportion of normal parenchyma and brain natriuretic propeptide (proBNP, rho = -0.485, p = 0.001), mean pulmonary arterial pressure (rho = -0.417, p = 0.002) and pulmonary vascular resistance (rho = -0.556, p < 0.0001), mosaic attenuation (rho = -0.527, p < 0.0001), perfusion centralization (rho = -0.489, p = < 0.0001), and right ventricular diameter (rho = -0.451, p = 0.001). The entropy of hyperemic parenchyma showed a positive correlation with the pulmonary wedge pressure (rho = 0.402, p = 0.003). The slope of center-to-periphery attenuation distribution correlated with centralization (rho = -0.477, p < 0.0001), and with proBNP (rho = -0.463, p = 0.002). Conclusion This study validates an automated system that leverages Bayesian analysis to quantify the severity and distribution of perfusion changes in CTPA. The results show the potential of this method to support clinical evaluations of CTEPH by providing reproducible and objective measures.