Gaussian process emulation to accelerate parameter estimation in a mechanical model of the left ventricle: a critical step towards clinical end-user relevance
| Autor: | Xiaoyu Luo, Dirk Husmeier, Vinny Davies, Alan Lazarus, Umberto Noè, Hao Gao, Colin Berry, Kenneth Mangion, Benn Macdonald |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Discretization
Computer science Interface (computing) Heart Ventricles Biomedical Engineering Biophysics Myocardial Infarction Inference Bioengineering 01 natural sciences Biochemistry Clinical decision support system 030218 nuclear medicine & medical imaging Biomaterials 010104 statistics & probability 03 medical and health sciences symbols.namesake 0302 clinical medicine diagnostic imaging [Myocardial Infarction] diagnostic imaging [Heart Ventricles] Humans Relevance (information retrieval) ddc:530 Computer Simulation 0101 mathematics Gaussian process Emulation Estimation theory physiopathology [Heart Ventricles] physiopathology [Myocardial Infarction] Models Cardiovascular Magnetic Resonance Imaging symbols Life Sciences–Mathematics interface Algorithm Biotechnology |
| Zdroj: | Interface 16(156), 20190114 (2019). doi:10.1098/rsif.2019.0114 J R Soc Interface |
| ISSN: | 2017-0203 1742-5689 |
| DOI: | 10.1098/rsif.2019.0114 |
| Popis: | 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 real-time 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. |
| Databáze: | OpenAIRE |
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