Statistical inference in soft-tissue mechanics and fluid dynamics with an application to prognostication of myocardial infarction and pulmonary hypertension
|Starts:||14:00 11 Feb 2019|
|Ends:||15:00 11 Feb 2019|
|What is it:||Seminar|
|Organiser:||School of Mathematics|
|Who is it for:||University staff, External researchers, Adults, Alumni, Current University students|
Join us for this seminar, which is part of the Mathematics in Life Science seminar series.
A central problem in biomechanical studies of personalized human left ventricular (LV) modelling is estimating the material properties from in-vivo clinical MRI measurements in a time frame suitable for use in the clinic. Understanding these properties can provide insight into heart function or dysfunction and help inform personalised treatment. However, finding a solution to the differential equations which describe the myocardium through numerical integration can be computationally expensive. To circumvent this issue, we use the concept of statistical emulation to infer the myocardium properties of a healthy volunteer in a viable clinical time frame using in-vivo LV data. Emulation methods avoid computationally expensive simulations from the LV model by replacing it with a surrogate model inferred from simulations generated before the arrival of a patient, vastly improving efficiency at the clinic. I will compare and contrast various emulation strategies, discuss uncertainty quantification and (it time permits) discuss an extension of this framework to fluid dynamics in the pulmonary blood circulation system for prognostication of pulmonary hypertension.
Role: Professor of Statistics
Organisation: University of Glasgow
Biography: See Professor Husmeier's research profile
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