Assessment of reduced‐order unscented Kalman filter for parameter identification in 1‐dimensional blood flow models using experimental data. (13th January 2017)

• Record Type: Journal Article
• Authors: Caiazzo, A.; Caforio, Federica; Montecinos, Gino; Muller, Lucas O.; Blanco, Pablo J.; Toro, Eluterio F.
• Is Part Of: International journal for numerical methods in biomedical engineering. Volume 33:Number 8(2017:Aug.)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-01-13
• Subjects: blood flow -- finite volume method -- Kalman filter -- 1‐dimensional model -- parameter estimation; Biomedical engineering -- Periodicals; Imaging systems in medicine -- Periodicals; Numerical analysis -- Periodicals; Engineering mathematics -- Periodicals; 610.28
• DOI: 10.1002/cnm.2843
• Languages: English
• ISSNs: 2040-7939

Abstract

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. Abstract : This work considers a parameter estimation approach on the basis of the reduced‐order unscented Kalman filter in the context of one‐dimensional blood flow models, investigating the effects of using real measurements versus synthetic data for the estimation procedure. The filter is assessed considering the results of an in vitro model of the human arterial network and the available experimental measurements, comparing the estimation results with an identifiability analysis on the basis of the generalized sensitivity function and considering flow and pressure observations.