About the series
Our speaker: Alison Marsdenis an associate professor and Wall Center scholar in the departments of Pediatrics, Bioengineering, and, by courtesy, Mechanical Engineering at Stanford University. She was previously in the department of Mechanical and Aerospace Engineering at UCSD from 2007-2015. She was the recipient of a Burroughs Wellcome Fund Career Award at the Scientific Interface in 2007, an NSF CAREER award in 2011, and is a fellow of the American Institute of Medical and Biological Engineers and the Society for Industrial and Applied Mathematics. She has published 105 peer reviewed journal papers, and has received funding from the NSF, NIH, and several private foundations. Her work focuses on the development of numerical methods for cardiovascular blood flow simulation, medical device design, application of optimization to large-scale fluid mechanics simulations, and application of engineering tools to impact patient care in cardiovascular surgery and congenital heart disease.
Abstract: Cardiovascular blood flow simulations have emerged as a powerful tool for personalized medicine and biomechanics research. With the first FDA approval of coronary simulations for routine clinical use at the end of 2014, cardiovascular simulations have already led to paradigm shifts in clinical practice. In this talk we will describe the SimVascular open source package which provides a complete pipeline for image-based patient-specific blood flow modeling and simulation. We will briefly describe new capabilities of SimVascular, including large-deformation fluid structure interaction, machine learning to accelerate image segmentation, and reduced order 1D simulations. Simulations are increasingly used to predict surgical outcomes, test and optimize devices, and personalize risk-assessment for individual patients. However, most simulation results currently reported by the community are deterministic. As simulation methods gain clinical traction, uncertainty quantification is emerging as an essential computational tool for increasing rigor in results and accounting for myriad sources of uncertainty in the clinical data acquisition and modeling process. We will discuss and demonstrate a suite of tools, compatible with SimVascular, for handling uncertainty in the patient-specific modeling process, including 1) automated parameter estimation of model parameters to match clinical data targets under uncertainty, 2) forward uncertainty propagation to produce statistics on model predictions, and 3) multi-fidelity approaches which leverage reduced order models for improved efficiency. Finally, we will discuss continued challenges and areas for future research in UQ methods and their application to pediatric and adult cardiovascular disease.
How to Attend: Plan to join us on Thursday June 20, 2019 at 2pm by registering at: https://nsf2.webex.com/nsf2/onstage/g.php?MTID=e4f755835166bdb2e508e3b1ff998b812
Access code/event number: 906 192 402
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OAC - CI Webinar Series Format: The format for the webinar will begin with opening remarks from OAC director, Dr. Manish Parashar and a speaker introduction. The presentation will follow, concluding with a 5-10min Q&A. Questions must be sent via email and will be read by the moderator at the conclusion of the presentation. The webinars will be recorded and posted on the NSF website.