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Journal of Inverse and Ill-posed Problems

Editor-in-Chief: Kabanikhin, Sergey I.

6 Issues per year

Increased IMPACT FACTOR 2012: 0.560
Mathematical Citation Quotient 2012: 0.50

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Material parameter estimation and hypothesis testing on a 1D viscoelastic stenosis model: Methodology

1Center for Research in Scientific Computation, Center for Quantitative Sciences in Biomedicine, North Carolina State University, Raleigh, NC 27695-8212, USA

2Center for Research in Scientific Computation, Center for Quantitative Sciences in Biomedicine, North Carolina State University, Raleigh, NC 27695-8212, USA

3Center for Research in Scientific Computation, Center for Quantitative Sciences in Biomedicine, North Carolina State University, Raleigh, NC 27695-8212, USA

4BICOM, Brunel University, Uxbridge, UB8 3PH, England

5BICOM, Brunel University, Uxbridge, UB8 3PH, England

6BICOM, Brunel University, Uxbridge, UB8 3PH, England

7Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary, University of London, England

8Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary, University of London, England

9Clinical Physics, Barts and the London National Health Service Trust, England

Citation Information: Journal of Inverse and Ill-Posed Problems. Volume 21, Issue 1, Pages 25–57, ISSN (Online) 1569-3945, ISSN (Print) 0928-0219, DOI: 10.1515/jip-2012-0081, February 2013

Publication History

Received:
2012-04-12
Published Online:
2013-02-01

Abstract.

Non-invasive detection, localization and characterization of an arterial stenosis (a blockage or partial blockage in the artery) continues to be an important problem in medicine. Partial blockage stenoses are known to generate disturbances in blood flow which generate shear waves in the chest cavity. We examine a one-dimensional viscoelastic model that incorporates Kelvin–Voigt damping and internal variables, and develop a proof-of-concept methodology using simulated data. We first develop an estimation procedure for the material parameters. We use this procedure to determine confidence intervals for the estimated parameters, which indicates the efficacy of finding parameter estimates in practice. Confidence intervals are computed using asymptotic error theory as well as bootstrapping. We then develop a model comparison test to be used in determining if a particular data set came from a low input amplitude or a high input amplitude; this we anticipate will aid in determining when stenosis is present. These two thrusts together will serve as the methodological basis for our continuing analysis using experimental data currently being collected.

Keywords: Viscoelastic model; sensitivity analysis; inverse problem; asymptotic theory; bootstrapping; model selection

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