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

Abstract

Model personalization is a key aspect for biophysical models to impact clinical practice, and cardiac contractility personalization from medical images is a major step in this direction. Existing gradient-based optimization approaches show promising results of identifying the maximum contractility from images, but the contraction and relaxation rates are not accounted for. A main reason is the limited choices of objective functions when their gradients are required. For complicated cardiac models, analytical evaluations of gradients are very difficult if not impossible, and finite difference approximations are computationally expensive and may introduce numerical difficulties. By removing such limitations with derivative-free optimization, we found that a velocity-based objective function can properly identify regional maximum contraction stresses, contraction rates, and relaxation rates simultaneously with intact model complexity. Experiments on synthetic data show that the parameters are better identified using the velocity-based objective function than its position-based counterpart, and the proposed framework is insensitive to initial parameters with the adopted derivative-free optimization algorithm. Experiments on clinical data show that the framework can provide personalized contractility parameters which are consistent with the underlying physiologies of the patients and healthy volunteers.

High‐resolution data assimilation of cardiac mechanics applied to a dyssynchronous ventricle

“…The contraction parameter in our study was resolved at a high P1 level of resolution. Previous studies have considered contraction parameters that were resolved up to the regional level of AHA zones. By comparing our P1 results to those generated with a regional resolution, we have shown that it is possible to greatly increase the fitting ability of a data assimilation method by increasing the parameter resolution.…”

“…The gradients necessary for these optimizations were calculated using direct differentiation or finite difference . More recent efforts include the use of global optimization methods: in particular, genetic algorithms, a Monte Carlo method, subplex algorithm, and parameter sweeps . Finally, reduced order unscented Kalman filtering has also been successfully applied as a data assimilation tool for patient‐specific model creation …”

Spatially Adaptive Multi-Scale Optimization for Local Parameter Estimation in Cardiac Electrophysiology

“…These include derivative-free optimization [1], [4], [5], Bayesian estimation by building surrogates using Polynomial chaos [6] or Gaussian process [7], and Bayesian filtering [8]. Essentially, all these methods involve a non-intrusive, repeated evaluation of the physiological model.…”

“…Another approach is to reduce the dimension of the unknowns by partitioning the cardiac mesh into pre-defined segments and to assume uniform parameter value within each segment. This reduces the spatial field of tissue properties to a low-resolution representation (in the range of 3-26 segments) [1], [4], [7], [11]. As a result of this drastically decreased resolution, the solution has limited ability to reflect different levels of tissue heterogeneity, or the existence of local abnormal tissue with various sizes and distributions.…”

“…As a result of this drastically decreased resolution, the solution has limited ability to reflect different levels of tissue heterogeneity, or the existence of local abnormal tissue with various sizes and distributions. Additionally, as the number of pre-defined segments increases, a good initialization is shown to become critical to the accuracy of parameter estimation [4]. This unfortunately relies on the availability of measurement data that can reveal diseased regions a priori .…”