Chronic Thromboembolic Pulmonary Hypertension (CTEPH) is a fatal, underdiagnosed, yet curable disease which afflicts the pulmonary arteries. Currently, there is no systematic way to determine which lesions to treat. Therefore, we create a patient-specific model to help physicians choose the right lesions to treat for optimal prognosis.
In this project, we first created 3D volumetric surfaces of each patient's pulmonary arterial structure using 3D Slicer. Then, with the Vascular Modeling Toolkit, we extract centerlines, connected in a labeled tree. Nodes along the centerlines provide data about radii at different points along the vessel and also have coordinates in 3D space. Vessel junctions occur when nodes intersect, and we represent a network of vessels through a connectivity matrix. We find a representative radius for each vessel by averaging radii of relevant nodes, using our novel change point algorithm. The algorithm determines vessel radii and uncertainty and corrects junctions between vessels for physiological accuracy. To standardize network size and extend our network past limits in CT scan resolution, we prune networks to the same number of vessels and then attach asymmetric binary trees at the end of each terminal vessel. We solve a 1D fluid dynamics model in this model, predicting blood pressure and flow for each vessel.
Preliminary results predicting mean pulmonary arterial pressure and flow in five segmentations from a healthy control’s pulmonary arteries show that the size of the tree matters, and, using ANOVA analysis, we found that vessel radii and length differ significantly between segmentations; however, sampling from the radius estimates for each vessel provides reliable predictions of pressure and flow. Segmenting CTEPH patients reveals multiple lesion types per patient, including ring lesions, total occlusions, and tortuous vessels. Future work includes generating CTEPH networks and conducting fluid dynamics simulations in CTEPH geometries. These in-silico treatments and simulations mitigate the need for invasive and expensive scanning and provide insight into optimal treatment plans for physicians.
This work was conducted during the DRUMS REU in 2023 under the supervision of Dr. Mette Olufsen. There, I worked with four undergrads from across the country: Zach(ASU), Isaiah(NCSU), Alex(USF), and Emma(CSU). We collaborated closely with the Cardiovascular Dynamics Group, specifically with Michelle Bartolo, currently a fifth-year Biomathematics PhD student at NC State.
In this project, we first created 3D volumetric surfaces of each patient's pulmonary arterial structure using 3D Slicer. Then, with the Vascular Modeling Toolkit, we extract centerlines, connected in a labeled tree. Nodes along the centerlines provide data about radii at different points along the vessel and also have coordinates in 3D space. Vessel junctions occur when nodes intersect, and we represent a network of vessels through a connectivity matrix. We find a representative radius for each vessel by averaging radii of relevant nodes, using our novel change point algorithm. The algorithm determines vessel radii and uncertainty and corrects junctions between vessels for physiological accuracy. To standardize network size and extend our network past limits in CT scan resolution, we prune networks to the same number of vessels and then attach asymmetric binary trees at the end of each terminal vessel. We solve a 1D fluid dynamics model in this model, predicting blood pressure and flow for each vessel.
Preliminary results predicting mean pulmonary arterial pressure and flow in five segmentations from a healthy control’s pulmonary arteries show that the size of the tree matters, and, using ANOVA analysis, we found that vessel radii and length differ significantly between segmentations; however, sampling from the radius estimates for each vessel provides reliable predictions of pressure and flow. Segmenting CTEPH patients reveals multiple lesion types per patient, including ring lesions, total occlusions, and tortuous vessels. Future work includes generating CTEPH networks and conducting fluid dynamics simulations in CTEPH geometries. These in-silico treatments and simulations mitigate the need for invasive and expensive scanning and provide insight into optimal treatment plans for physicians.
This work was conducted during the DRUMS REU in 2023 under the supervision of Dr. Mette Olufsen. There, I worked with four undergrads from across the country: Zach(ASU), Isaiah(NCSU), Alex(USF), and Emma(CSU). We collaborated closely with the Cardiovascular Dynamics Group, specifically with Michelle Bartolo, currently a fifth-year Biomathematics PhD student at NC State.
The CTEPH group ❤️
My first solo presentation
The CTEPH group ❤️
My first solo presentation
