TY - JOUR
T1 - Gaussian process emulation to accelerate parameter estimation in a mechanical model of the left ventricle
T2 - A critical step towards clinical end-user relevance
AU - Noè, Umberto
AU - Lazarus, Alan
AU - Gao, Hao
AU - Davies, Vinny
AU - MacDonald, Benn
AU - Mangion, Kenneth
AU - Berry, Colin
AU - Luo, Xiaoyu
AU - Husmeier, Dirk
PY - 2019/7
Y1 - 2019/7
N2 - In recent years, we have witnessed substantial advances in the mathematical modelling of the biomechanical processes underlying the dynamics of the cardiac soft-tissue. Gao et al. (Gao et al. 2017 J. R. Soc. Interface 14, 20170203 (doi:10.1098/rsif.2017.0203)) demonstrated that the parameters underlying the biomechanical model have diagnostic value for prognosticating the risk of myocardial infarction. However, the computational costs of parameter estimation are prohibitive when the goal lies in building realtime clinical decision support systems. This is due to the need to repeatedly solve the mathematical equations numerically using finite-element discretization during an iterative optimization routine. The present article presents a method for accelerating the inference of the constitutive parameters by using statistical emulation with Gaussian processes. We demonstrate how the computational costs can be reduced by about three orders of magnitude, with hardly any loss in accuracy, and we assess various alternative techniques in a comparative evaluation study based on simulated data obtained by solving the left ventricular model with the finite-element method, and real magnetic resonance images data for a human volunteer.
AB - In recent years, we have witnessed substantial advances in the mathematical modelling of the biomechanical processes underlying the dynamics of the cardiac soft-tissue. Gao et al. (Gao et al. 2017 J. R. Soc. Interface 14, 20170203 (doi:10.1098/rsif.2017.0203)) demonstrated that the parameters underlying the biomechanical model have diagnostic value for prognosticating the risk of myocardial infarction. However, the computational costs of parameter estimation are prohibitive when the goal lies in building realtime clinical decision support systems. This is due to the need to repeatedly solve the mathematical equations numerically using finite-element discretization during an iterative optimization routine. The present article presents a method for accelerating the inference of the constitutive parameters by using statistical emulation with Gaussian processes. We demonstrate how the computational costs can be reduced by about three orders of magnitude, with hardly any loss in accuracy, and we assess various alternative techniques in a comparative evaluation study based on simulated data obtained by solving the left ventricular model with the finite-element method, and real magnetic resonance images data for a human volunteer.
KW - Constitutive parameters
KW - Emulation
KW - Finite element discretization
KW - Left ventricular mechanics
KW - Parameter estimation
KW - Simulation
U2 - 10.1098/rsif.2019.0114
DO - 10.1098/rsif.2019.0114
M3 - Article
C2 - 31266415
AN - SCOPUS:85069261070
VL - 16
JO - Journal of the Royal Society, Interface
JF - Journal of the Royal Society, Interface
SN - 1742-5689
IS - 156
M1 - 20190114
ER -