Berkeley Fluids Seminar
University of California, Berkeley
Bring your lunch and enjoy learning about fluids!
Monday, November 6, 2017
12:00-13:00, 3110 Etcheverry Hall
Dr. Christoph Augustin (UC Berkeley)
Abstract: Combined electromechanical and hemodynamic computer models of the left ventricle (LV) and the aorta are considered an important tool for analyzing the interplay between LV deformation, valvular anatomy and flow patterns. Typically, image-based kinematic models describing endocardial motion or MRI-based flow measurements in the LV outflow tract are used as an input to blood flow simulations. While such models are suitable for analyzing the hemodynamic status quo, they are limited in predicting the response to interventions that alter afterload conditions, as this approach assumes that the heartbeat itself will remain unchanged. Electro-mechano-fluidic (EMF) simulations of the LV have the potential to overcome this limitation, but are more difficult to formulate, parameterize and compute. In this talk, I report on our recent advancements in developing an automated workflow for the creation of patient-specific EMF models of the LV and the aorta that are suitable to drive blood flow simulations. Models are custom-tailored to individual patients to ensure that the in-silico simulation replicates clinical observations for a given patient. These models can be used to probe the effect of different therapeutic options and thus identify the therapy that yields the best post-treatment outcome.
Bio: Dr. Christoph Augustin is currently a Marie Curie post-doctoral fellow in the Department of Mechanical Engineering at UC Berkeley. Prior to this, he obtained his M.S. and Ph.D. in mathematics and biomechanics from Graz University of Technology and was a post-doctoral researcher at the Institute of Biophysics at the Medical University of Graz, Austria. His main research interests are multi-physics problems, in particular the electro-mechano-fluidic modeling of the heart, and their numerical simulation using high-performance and massively parallel computing.