@inbook{16646,
  author = {Joakim Sundnes and Roc{\'\i}o Rodr{\'\i}guez-Cantano and Gerhard Sommer and Kewei Li and Daniel Haspinger and Raymond Ogden},
  title = {A Bayesian Approach to Parameter Estimation in Cardiac Mechanics},
  abstract = {Computational models of cardiac mechanics have been shown to capture the general mechanical function of the heart, and have been parameterized based on in vivo patient data to accurately reproduce the heart function of individual patients. Pre- vious attempts for creating such patient-specific problem have typically been based on solving a deterministic inverse problem, which minimizes the misfit between cer- tain model outputs and measured values. While this approach is robust and efficient, and has shown good agreement between model results and patient data, it does not provide any information about the uncertainty in the resulting model parameters. We here present a parameter estimation framework based on a Bayesian approach, which estimates probability density functions (PDFs) of material parameters based on uncertain input values. The method is based on sampling the parameter space and solving the associated forward model for each sample, which may lead to a substantial computational problem if multiple parameters are considered. However, the model also offers additional information in the form of univariate or multivariate PDFs for the estimated parameters. We investigate the potential of the methodology by solving a simple parameter estimation problem in passive left ventricular mechan- ics, and show that the results are in agreement with previous results obtained with a deterministic parameter estimation method.},
  year = {2022},
  journal = {Solid (Bio) mechanics: Challenges of the Next Decade},
  pages = {245{\textendash}256},
  month = {06/2022},
  publisher = {Springer},
  address = {Cham},
  issn = {ISBN 978-3-030-92338-9},
  url = {https://doi.org/10.1007/978-3-030-92339-6_10},
  doi = {10.1007/978-3-030-92339-6_10},
}