Assessment of reduced‐order unscented Kalman filter for parameter identification in 1‐dimensional blood flow models using experimental data

Abstract

This work presents a detailed investigation of a parameter estimation approach on the basis of the reduced-order unscented Kalman filter (ROUKF) in the context of 1-dimensional blood flow models. In particular, the main aims of this study are (1) to investigate the effects of using real measurements versus synthetic data for the estimation procedure (i.e., numerical results of the same in silico model, perturbed with noise) and (2) to identify potential difficulties and limitations of the approach in clinically realistic applications to assess the applicability of the filter to such setups. For these purposes, the present numerical study is based on a recently published in vitro model of the arterial network, for which experimental flow and pressure measurements are available at few selected locations. To mimic clinically relevant situations, we focus on the estimation of terminal resistances and arterial wall parameters related to vessel mechanics (Young's modulus and wall thickness) using few experimental observations (at most a single pressure or flow measurement per vessel). In all cases, we first perform a theoretical identifiability analysis on the basis of the generalized sensitivity function, comparing then the results owith the ROUKF, using either synthetic or experimental data, to results obtained using reference parameters and to available measurements.

Identification of dynamic load for prosthetic structures

“…The relative errors under different noise levels for the following: A, the implant-supported fixed partial denture structure; B, the teethsupported fixed partial denture structure FIGURE 10 The identified results for different iteration steps k with 5% noise level of the teeth-supported fixed partial denture structure To evaluate the error between the actual and the identified loads, an error estimator was defined in Equation 37. The results of relative error in these 2 examples were presented in Figure 9A and B.…”

“…Marais et al evaluated the diameter and thickness‐related variations in mechanical properties of degraded arterial wall, in which an inverse FE method was used to identify the material parameters. More literature on the parameter identification can be referred to the references herein …”

“…More literature on the parameter identification can be referred to the references herein. [34][35][36][37] In the abovementioned literatures, the identification of material parameters relevant to biomechanical systems has drawn certain attention; however, there has been no report concerning the methodological development for identification of dynamic load in biomechanical systems. Many biomedical structures may not be easy to place transducers or other sensors directly on the points of interest.…”

Backward sensitivity analysis and reduced‐order covariance estimation in noninvasive parameter identification for cerebral arteries

“…In previous studies, still others have developed an algorithm based on a data assimilation technique to estimate hemodynamic parameters such as elastic properties of arteries, arterial compliance, and boundary condition parameters (Windkessel boundary parameters and reflection coefficients).…”

“…Others have demonstrated the considerable influence of patient-specific anatomy of cerebral arterial network on blood flow patterns in local cerebral aneurysms. 10,[14][15][16][17][18][19] In previous studies, [20][21][22][23][24] still others have developed an algorithm based on a data assimilation technique to estimate hemodynamic parameters such as elastic properties of arteries, arterial compliance, and boundary condition parameters (Windkessel boundary parameters and reflection coefficients).…”