Computational fluid dynamics (CFD) models are emerging as mathematical tools to assist in the diagnostic assessment of cardiovascular disease. Recent advances in medical imaging and computational algorithms for analyzing said images have made subject-specific modeling of the cardiovascular system a feasible task . This is particularly important in the case of pulmonary hypertension , as the pulmonary circulation transports oxygen to the lungs via a rapidly bifurcating network of highly compliant blood vessels. However, medical image analysis is subject to uncertainty, which can lead to variation in fluid model predictions . This study addresses this issue by developing a computational algorithm to determine pulmonary hemodynamics ( blood pressure and flow ) in a large network of arterial vessels and subsequently quantify the uncertainty of these predictionsto geometric variation associated with image segmentation. We introduce uncertainty in the blood vessel network by generating multiple segmentations of a pulmonary tree from a single image of an excised mouse lung using randomly selected pre-segmentation parameters. We use a custom MATLAB algorithm to extract vessel radii, length, and connectivity for each pulmonary network generated . We quantify the uncertainty in geometric features of the network using density estimation techniques to specify probability densities for vessel radius and length . We sample from these distributions , compute model parameters, and propagate uncertainties to pressure and flow predictions using a one-dimensional ( 1D ) fluid dynamics model. Results show that variation in network connectivity is a larger contributor to hemodynamic uncertainty in comparison to changes in vessel radius and length.
Computational fluid dynamics (CFD) models are emerging as tools for assisting in diagnostic assessment of cardiovascular disease. Recent advances in image segmentation has made subject-specific modelling of the cardiovascular system a feasible task , which is particularly important in the case of pulmonary hypertension (PH), which requires a combination of invasive and non-invasive procedures for diagnosis. Uncertainty in image segmentation can easily propagate to CFD model predictions, making uncertainty quantification crucial for subject-specific models . This study quantifies the variability of one-dimensional (1D) CFD predictions by propagating the uncertainty of network geometry and connectivity to blood pressure and flow predictions. We analyse multiple segmentations of an image of an excised mouse lung using different pre-segmentation parameters. A custom algorithm extracts vessel length, vessel radii, and network connectivity for each segmented pulmonary network . We quantify uncertainty in geometric features by constructing probability densities for vessel radius and length , and then sample from these distributions and propagate uncertainties of haemodynamic predictions using a 1D CFD model. Results show that variation in network connectivity is a larger contributor to haemodynamic uncertainty than vessel radius and length.
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