I received my Bachelor's degree in Biomedical Engineering with a minor in Industrial Design from Georgia Institute of Technology in 2020. During my time at Georgia Tech, I worked as an undergraduate researcher in Dr. Ajit Yoganathan's Cardiovascular Fluid Mechanics Lab. My project was focused on studying the contribution of foreign materials to thrombosis in transcatheter aortic valves using an in vitro flow loop. Beyond my research interests, I was also actively involved in the Society of Women Engineers, promoting outreach activities and creating mentorship opportunities for women in STEM.
Member (Student), Cardiovascular Institute
Honors & Awards
American Scandinavian Foundation Fellowship, American Scandinavian Foundation (August 2023)
NSF Graduate Research Fellowship, National Science Foundation (April 2020)
Education & Certifications
Master of Science, Stanford University, BIOE-MS (2022)
B.S., Georgia Institute of Technology, Biomedical Engineering (2020)
Non-invasive estimation of pressure drop across aortic coarctations: validation of 0D and 3D computational models with in vivo measurements.
medRxiv : the preprint server for health sciences
Blood pressure gradient (ΔP) across an aortic coarctation (CoA) is an important measurement to diagnose CoA severity and gauge treatment efficacy. Invasive cardiac catheterization is currently the gold-standard method for measuring blood pressure. The objective of this study was to evaluate the accuracy of ΔP estimates derived non-invasively using patient-specific 0D and 3D deformable wall simulations.Medical imaging and routine clinical measurements were used to create patient-specific models of patients with CoA (N=17). 0D simulations were performed first and used to tune boundary conditions and initialize 3D simulations. ΔP across the CoA estimated using both 0D and 3D simulations were compared to invasive catheter-based pressure measurements for validation.The 0D simulations were extremely efficient (~15 secs computation time) compared to 3D simulations (~30 hrs computation time on a cluster). However, the 0D ΔP estimates, unsurprisingly, had larger mean errors when compared to catheterization than 3D estimates (12.1 ± 9.9 mmHg vs 5.3 ± 5.4 mmHg). In particular, the 0D model performance degraded in cases where the CoA was adjacent to a bifurcation. The 0D model classified patients with severe CoA requiring intervention (defined as ΔP≥20 mmHg) with 76% accuracy and 3D simulations improved this to 88%.Overall, a combined approach, using 0D models to efficiently tune and launch 3D models, offers the best combination of speed and accuracy for non-invasive classification of CoA severity.
View details for DOI 10.1101/2023.09.05.23295066
View details for PubMedID 37732242
View details for PubMedCentralID PMC10508787