Robust personalisation of 3D electromechanical cardiac models. Application to heterogeneous and longitudinal clinical databases

Roch Molléro 1
1 ASCLEPIOS - Analysis and Simulation of Biomedical Images
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Personalised cardiac modeling consists in creating virtual 3D simulations of real clinical cases to help clinicians predict the behaviour of the heart, or better understand some pathologies from the estimated values of biophysical parameters. In this work we first motivate the need for a consistent parameter estimation framework, from a case study were uncertainty in myocardial fibre orientation leads to an uncertainty in estimated parameters which is extremely large compared to their physiological variability. To build a consistent approach to parameter estimation, we then tackle the computational complexity of 3D models. We introduce an original multiscale 0D/3D approach for cardiac models, based on a multiscale coupling to approximate outputs of a 3D model with a reduced "0D" version of the same model. Then we derive from this coupling an efficient multifidelity optimisation algorithm for the 3D model. In a second step, we build more than 140 personalised 3D simulations, in the context of two studies involving the longitudinal analysis of the cardiac function: on one hand the analysis of long-term evolution of cardiomyopathies under therapy, on the other hand the modeling of short-term cardiovascular changes during digestion. Finally we present an algorithm to automatically detect and select observable directions in the parameter space from a set of measurements, and compute consistent population-based priors probabilities in these directions, which can be used to constrain parameter estimation for cases where measurements are missing. This enables consistent parameter estimations in a large databases of 811 cases with the 0D model, and 137 cases of the 3D model.
Complete list of metadatas

Cited literature [106 references]  Display  Hide  Download

https://tel.archives-ouvertes.fr/tel-01737200
Contributor : Abes Star <>
Submitted on : Monday, March 19, 2018 - 12:28:07 PM
Last modification on : Thursday, January 17, 2019 - 1:48:04 PM
Long-term archiving on : Tuesday, September 11, 2018 - 9:29:18 AM

File

2017AZUR4106.pdf
Version validated by the jury (STAR)

Identifiers

  • HAL Id : tel-01737200, version 1

Collections

Citation

Roch Molléro. Robust personalisation of 3D electromechanical cardiac models. Application to heterogeneous and longitudinal clinical databases. Signal and Image processing. Université Côte d'Azur, 2017. English. ⟨NNT : 2017AZUR4106⟩. ⟨tel-01737200⟩

Share

Metrics

Record views

468

Files downloads

427