Key result
Statistical emulation provided accurate parameter inference with a computational cost reduction of about three orders of magnitude compared with numerical integration.
Why the study?
Estimating left ventricular material properties and biophysical parameters from in vivo measurements via numerical integration is computationally expensive and slow for clinical use.
Does statistical emulation reduce computational cost while maintaining accuracy for parameter inference in left ventricular biomechanical models compared to numerical integration?
Does statistical emulation reduce computational cost while maintaining accuracy for parameter inference in left ventricular biomechanical models compared to numerical integration?
Effect estimate: Reduction of about three orders of magnitude
Statistical emulation can vastly improve the computational efficiency of personalized left ventricular biomechanical modeling, making it viable for clinical timeframes.
Statistical emulation may enable clinical-timeframe LV modeling; leaves open prospective validation in patient cohorts.
Summary A central problem in biomechanical studies of personalized human left ventricular modelling is estimating the material properties and biophysical parameters from in vivo clinical measurements in a timeframe that is suitable for use within a clinic. Understanding these properties can provide insight into heart function or dysfunction and help to inform personalized 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 timeframe by using in vivo magnetic resonance image data. Emulation methods avoid computationally expensive simulations from the left ventricular 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: emulation of the computational model outputs and emulation of the loss between the observed patient data and the computational model outputs. These strategies are tested with two interpolation methods, as well as two 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 the output emulation method, with local Gaussian process interpolation and the Euclidean loss function, provides accurate parameter inference in both simulated and clinical data, with a reduction in the computational cost of about three orders of magnitude compared with numerical integration of the differential equations by using finite element discretization techniques.
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Davies et al. (2019) studied Healthy volunteer (biomechanical modelling) (n=1). Statistical emulation (output emulation with local Gaussian process interpolation and Euclidean loss) vs. Numerical integration using finite element discretization was evaluated on Accuracy of parameter inference and computational cost (Reduction of about three orders of magnitude). Statistical emulation provided accurate parameter inference with a computational cost reduction of about three orders of magnitude compared with numerical integration.
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