Literature DB >> 15046996

Development of an in vivo method for determining material properties of passive myocardium.

Espen W Remme1, Peter J Hunter, Otto Smiseth, Carey Stevens, Stein Inge Rabben, Helge Skulstad, B Bjørn Angelsen.   

Abstract

Calculation of mechanical stresses and strains in the left ventricular (LV) myocardium by the finite element (FE) method relies on adequate knowledge of the material properties of myocardial tissue. In this paper, we present a model-based estimation procedure to characterize the stress-strain relationship in passive LV myocardium. A 3D FE model of the LV myocardium was used, which included morphological fiber and sheet structure and a nonlinear orthotropic constitutive law with different stiffness in the fiber, sheet, and sheet-normal directions. The estimation method was based on measured wall strains. We analyzed the method's ability to estimate the material parameters by generating a set of synthetic strain data by simulating the LV inflation phase with known material parameters. In this way we were able to verify the correctness of the solution and to analyze the effects of measurement and model error on the solution accuracy and stability. A sensitivity analysis was performed to investigate the observability of the material parameters and to determine which parameters to estimate. The results showed a high degree of coupling between the parameters governing the stiffness in each direction. Thus, only one parameter in each of the three directions was estimated. For the tested magnitudes of added noise and introduced model errors, the resulting estimated stress-strain characteristics in the fiber and sheet directions converged with good accuracy to the known relationship. The sheet-normal stress-strain relationship had a higher degree of uncertainty as more noise was added and model error was introduced.

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Year:  2004        PMID: 15046996     DOI: 10.1016/j.jbiomech.2003.09.023

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  10 in total

1.  A framework for biomechanics simulations using four-chamber cardiac models.

Authors:  Arian Jafari; Edward Pszczolkowski; Adarsh Krishnamurthy
Journal:  J Biomech       Date:  2019-05-21       Impact factor: 2.712

2.  Bayesian optimisation for efficient parameter inference in a cardiac mechanics model of the left ventricle.

Authors:  Agnieszka Borowska; Hao Gao; Alan Lazarus; Dirk Husmeier
Journal:  Int J Numer Method Biomed Eng       Date:  2022-04-07       Impact factor: 2.648

3.  Robust and efficient fixed-point algorithm for the inverse elastostatic problem to identify myocardial passive material parameters and the unloaded reference configuration.

Authors:  Laura Marx; Justyna A Niestrawska; Matthias A F Gsell; Federica Caforio; Gernot Plank; Christoph M Augustin
Journal:  J Comput Phys       Date:  2022-08       Impact factor: 4.645

4.  A computationally efficient formal optimization of regional myocardial contractility in a sheep with left ventricular aneurysm.

Authors:  Kay Sun; Nielen Stander; Choon-Sik Jhun; Zhihong Zhang; Takamaro Suzuki; Guan-Ying Wang; Maythem Saeed; Arthur W Wallace; Elaine E Tseng; Anthony J Baker; David Saloner; Daniel R Einstein; Mark B Ratcliffe; Julius M Guccione
Journal:  J Biomech Eng       Date:  2009-11       Impact factor: 2.097

5.  Fast parameter inference in a biomechanical model of the left ventricle by using statistical emulation.

Authors:  Vinny Davies; Umberto Noè; Alan Lazarus; Hao Gao; Benn Macdonald; Colin Berry; Xiaoyu Luo; Dirk Husmeier
Journal:  J R Stat Soc Ser C Appl Stat       Date:  2019-09-20       Impact factor: 1.864

6.  A machine learning model to estimate myocardial stiffness from EDPVR.

Authors:  Hamed Babaei; Emilio A Mendiola; Sunder Neelakantan; Qian Xiang; Alexander Vang; Richard A F Dixon; Dipan J Shah; Peter Vanderslice; Gaurav Choudhary; Reza Avazmohammadi
Journal:  Sci Rep       Date:  2022-03-31       Impact factor: 4.379

Review 7.  Personalised computational cardiology: Patient-specific modelling in cardiac mechanics and biomaterial injection therapies for myocardial infarction.

Authors:  Kevin L Sack; Neil H Davies; Julius M Guccione; Thomas Franz
Journal:  Heart Fail Rev       Date:  2016-11       Impact factor: 4.214

8.  Parameter estimation in a Holzapfel-Ogden law for healthy myocardium.

Authors:  H Gao; W G Li; L Cai; C Berry; X Y Luo
Journal:  J Eng Math       Date:  2015-01-30       Impact factor: 1.509

9.  Improved identifiability of myocardial material parameters by an energy-based cost function.

Authors:  Anastasia Nasopoulou; Anoop Shetty; Jack Lee; David Nordsletten; C Aldo Rinaldi; Pablo Lamata; Steven Niederer
Journal:  Biomech Model Mechanobiol       Date:  2017-02-10

10.  In vivo estimation of passive biomechanical properties of human myocardium.

Authors:  Arnab Palit; Sunil K Bhudia; Theodoros N Arvanitis; Glen A Turley; Mark A Williams
Journal:  Med Biol Eng Comput       Date:  2018-02-26       Impact factor: 2.602

  10 in total

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