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Michelle A Bartolo

Publications and source records attributed to Michelle A Bartolo.

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Multiscale hemodynamics model for the pulmonary arteries, arterioles, capillaries, venules and veins

This study presents the first mathematical model of pulsatile hemodynamics that encompasses the complete pulmonary circulation, explicitly linking the large arteries, arterioles, capillaries, venules, and large veins. To overcome the limitations of previous models that exclude explicit capillary dynamics, we incorporate a one-dimensional structured-tree model of the pulmonary arteries and veins with a dynamic capillary sheet model. This approach establishes a recursive method for coupling the capillary sheets to the structured trees, connecting arterioles and venules in a ladder-like architecture. To evaluate the impact of incorporating this capillary structure, we compare simulated hemodynamics in a healthy control subject and a pulmonary hypertension (PH) patient. Results illustrate that including capillaries in the model significantly alters hemodynamic predictions by introducing downstream damping. In the healthy control subject, the inclusion of the capillary network attenuates pulsatile energy, yielding the expected steady venous pressure and flow profiles, whereas omitting the capillaries results in an unphysiological high pulsatility transmitting into the venous system. The structural impact of the capillaries is even more pronounced in the PH patient, where explicitly modeling the capillary bed corrects an over-prediction in peak systolic pressure in the main pulmonary artery. Furthermore, unlike the healthy control subject, the remodeled PH microvasculature fails to completely isolate the venous system from arterial pulsations. Finally, we employ parametric sensitivity analysis to investigate how specific biomechanical factors drive vascular remodeling, demonstrating the framework's capability to quantify disease progression and severity.

q-bio.TO

Computational framework for the generation of one-dimensional vascular models accounting for uncertainty in networks extracted from medical images

Patient-specific computational modeling is a popular, non-invasive method to answer medical questions. Medical images are used to extract geometric domains necessary to create these models, providing a predictive tool for clinicians. However, in vivo imaging is subject to uncertainty, impacting vessel dimensions essential to the mathematical modeling process. While there are numerous programs available to provide information about vessel length, radii, and position, there is currently no exact way to determine and calibrate these features. This raises the question, if we are building patient-specific models based on uncertain measurements, how accurate are the geometries we extract and how can we best represent a patient's vasculature? In this study, we develop a novel framework to determine vessel dimensions using change points. We explore the impact of uncertainty in the network extraction process on hemodynamics by varying vessel dimensions and segmenting the same images multiple times. Our analyses reveal that image segmentation, network size, and minor changes in radius and length have significant impacts on pressure and flow dynamics in rapidly branching structures and tapering vessels. Accordingly, we conclude that it is critical to understand how uncertainty in network geometry propagates to fluid dynamics, especially in clinical applications.

q-bio.TO