# Fast Parameter Inference in a Biomechanical Model of the Left Ventricle using Statistical Emulation

## TL;DR

This paper introduces a fast, emulation-based approach to estimate myocardial properties from MRI data, significantly reducing computation time for clinical use in personalized heart modeling.

## Contribution

It presents a novel application of statistical emulation to biomechanical LV models, enabling rapid parameter inference suitable for clinical settings.

## Key findings

- Emulation methods drastically reduce computation time.
- Comparison of two emulation strategies shows their effectiveness.
- Different interpolation and loss functions impact inference accuracy.

## Abstract

A central problem in biomechanical studies of personalised human left ventricular (LV) modelling is estimating the material properties and biophysical parameters 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 medicine. However, finding a solution to the differential equations which mathematically describe the kinematics and dynamics of the myocardium through numerical integration can be computationally expensive. To circumvent this issue, we use the concept of emulation to infer the myocardial properties of a healthy volunteer in a viable clinical time frame using in-vivo magnetic resonance image (MRI) data. Emulation methods avoid computationally expensive simulations from the LV model by replacing the biomechanical model, which is defined in terms of explicit partial differential equations, with a surrogate model inferred from simulations generated before the arrival of a patient, vastly improving computational 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...