Fast Parameter Inference in a Computational Model of the Left-Ventricle using Emulation

Vinny Davies (University of Leeds)

Friday 6th April, 2018 15:00-16:00 Maths 311B


A central problem in biomechanical studies of personalized human left ventricular (LV) modelling is estimating the material properties from in-vivo clinical measurements in a time frame suitable for use within a 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 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. We compare and contrast two emulation strategies: (i) emulation of the computational model outputs and (ii) emulation of the loss between the observed patient data and the computational model outputs. These strategies are tested with two different interpolation methods, as well as two different loss functions. The best combination of methods is found by comparing the accuracy of parameter inference on simulated data for each combination. This combination, using emulation of the model outputs (i), with local Gaussian process interpolation and mean squared error loss function, provides accurate parameter inference in both simulated and clinical data, with a reduction in computational cost of about 3 orders of magnitude compared to numerical integration of the differential equations using finite element discretisation techniques.

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