Bayesian parameter calibration of a one-dimensional hemodynamic model

Resumo

In this work, we couple a Bayesian optimization approach with a one-dimensional blood-flow model (known as ADAN86) to achieve a flexible and efficient strategy for the calibration of model parameters when in-vivo patient-specific data is available. The optimization step is addressed in the frequency domain, minimizing the discrepancies between the first harmonics of the in-vivo and predicted flow/pressure signals. The model parameters can be locally or globally perturbed, modifying the reference values in the same proportion for the whole system (global) or by region to account for the specificities of each location (local).

To test the proposed strategy, two cases are considered. The first one involves parameter calibration in a synthetic patient, where some parameters are locally modified, and the optimization process is compared in local and global scenarios. The second case addresses parameter optimization in a patient with in-vivo flow/pressure data, comparing the local and global calibration approaches.

The results show that the coupling between the Bayesian optimization process and the ADAN86 models yields an efficient strategy for the parameter calibration problem, which is naturally parallelizable and promising for data assimilation in computational hemodynamics.