109 research outputs found
Incorporating vessel wall remodeling into 1D cardiovascular network models of pulmonary hypertension
Parameter Inference in the Pulmonary Circulation of Mice
This study focuses on parameter inference in a pulmonary blood cir- culation model for mice. It utilises a fluid dynamics network model that takes selected parameter values and aims to mimic features of the pulmonary haemody- namics under normal physiological and pathological conditions. This is of medical relevance as it allows monitoring of the progression of pulmonary hypertension. Constraint nonlinear optimization is successfully used to learn the parameter values
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Impaired Cerebral Autoregulation Is Associated with Brain Atrophy and Worse Functional Status in Chronic Ischemic Stroke
Dynamic cerebral autoregulation (dCA) is impaired following stroke. However, the relationship between dCA, brain atrophy, and functional outcomes following stroke remains unclear. In this study, we aimed to determine whether impairment of dCA is associated with atrophy in specific regions or globally, thereby affecting daily functions in stroke patients. We performed a retrospective analysis of 33 subjects with chronic infarctions in the middle cerebral artery territory, and 109 age-matched non-stroke subjects. dCA was assessed via the phase relationship between arterial blood pressure and cerebral blood flow velocity. Brain tissue volumes were quantified from MRI. Functional status was assessed by gait speed, instrumental activities of daily living (IADL), modified Rankin Scale, and NIH Stroke Score. Compared to the non-stroke group, stroke subjects showed degraded dCA bilaterally, and showed gray matter atrophy in the frontal, parietal and temporal lobes ipsilateral to infarct. In stroke subjects, better dCA was associated with less temporal lobe gray matter atrophy on the infracted side ( = 0.029), faster gait speed ( = 0.018) and lower IADL score (0.002). Our results indicate that better dynamic cerebral perfusion regulation is associated with less atrophy and better long-term functional status in older adults with chronic ischemic infarctions
Influence of image segmentation on one-dimensional fluid dynamics predictions in the mouse pulmonary arteries
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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