Personalization of a cardiac electromechanical model using reduced order unscented Kalman filtering from regional volumes

Abstract

Patient-specific cardiac modeling can help in understanding pathophysiology and therapy planning. However it requires to combine functional and anatomical data in order to build accurate models and to personalize the model geometry, kinematics, electrophysiology and mechanics. Personalizing the electromechanical coupling from medical images is a challenging task. We use the Bestel-Clément-Sorine (BCS) electromechanical model of the heart, which provides reasonable accuracy with a reasonable number of parameters (14 for each ventricle) compared to the available clinical data at the organ level. We propose a personalization strategy from cine MRI data in two steps. We first estimate global parameters with an automatic calibration algorithm based on the Unscented Transform which allows to initialize the parameters while matching the volume and pressure curves. In a second step we locally personalize the contractilities of all AHA (American Heart Association) zones of the left ventricle using the reduced order unscented Kalman filtering on Regional Volumes. This personalization strategy was validated synthetically and tested successfully on eight healthy and three pathological cases.

A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion

“…The statistical shape model can be used as prior knowledge to guide image segmentation (Heimann and Meinzer, 2009). The fibre orientation atlas has the potential to be be adapted to a patient image to enable cardiac electromechanical modelling (Marchesseau et al, 2013). A review on the applications of the computational cardiac atlas can be referred to at (Young and Frangi, 2009).…”

Integrated Heart—Coupling multiscale and multiphysics models for the simulation of the cardiac function

“…Current ways of performing personalization include estimation of fiber directions from imaging modalities such as diffusion MRI [178], as well as data assimilation of cardiac motion from imaging into electromechanics simulations through unscented Kalman filters [164,233]. Meanwhile, in the cardiac fluid dynamics community more and more advanced patient-specific valve models incorporating fluid-structure interaction are being developed [1,73,52,81,148,175,267], and may in the future be incorporated into full-heart computational models to study the joint effect of valve function and cardiac mechanics.…”

Velocity-based cardiac contractility personalization from images using derivative-free optimization

“…Secondly, some of the calibrations were performed by manual adjustments, for example, the time differences between the electrical excitation and the onset of the active contraction. Techniques such as that in Marchesseau et al (2013) can be used to alleviate this issue. Thirdly, we use the cine MRI with the electromechanical model to provide the velocity measurements because image modalities such as phase-contrast MRI are unavailable in our data sets.…”

“…In Xi et al (2011), the performances of rUKF and SQP for the estimation of stiffness parameters were compared on synthetic data. In Marchesseau et al (2013), a method based on the unscented transform algorithm was proposed to calibrate the global mechanical parameters of the Bestel-Clément-Sorine electromechanical model (Bestel et al, 2001), followed by the personalization of the regional maximum contraction stresses using rUKF.…”