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PRODID:-//Centre for Mathematical Sciences - ECPv4.9.2//NONSGML v1.0//EN
CALSCALE:GREGORIAN
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X-WR-CALNAME:Centre for Mathematical Sciences
X-ORIGINAL-URL:http://math-sciences.org
X-WR-CALDESC:Events for Centre for Mathematical Sciences
BEGIN:VEVENT
DTSTART;TZID=UTC+0:20190529T150000
DTEND;TZID=UTC+0:20190529T160000
DTSTAMP:20190519T091517
CREATED:20181026T213008Z
LAST-MODIFIED:20190419T191359Z
UID:3175-1559142000-1559145600@math-sciences.org
SUMMARY:Dirk Husmeier (University of Glasgow)
DESCRIPTION:TITLE\nStatistical inference in soft-tissue mechanics and fluid dynamics with an application to prognostication of myocardial infarction and pulmonary hypertension \nABSTRACT\nA 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 discuss an extension of this framework to fluid dynamics in the pulmonary blood circulation system for prognostication of pulmonary hypertension\, which is a major cause of stroke\, heart failure and coronary artery disease. \n
URL:http://math-sciences.org/event/dirk-husmeier/
LOCATION:Rolle 115\, University of Plymouth
CATEGORIES:Seminars,Statistics and Data Science
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