Literature DB >> 18347824

Determination of human arterial wall parameters from clinical data.

Jonas Stålhand1.   

Abstract

This study suggests a method to compute the material parameters for arteries in vivo from clinically registered pressure-radius signals. The artery is modelled as a hyperelastic, incompressible, thin-walled cylinder and the membrane stresses are computed using a strain energy. The material parameters are determined in a minimisation process by tuning the membrane stress to the stress obtained by enforcing global equilibrium. In addition to the mechanical model, the study also suggests a preconditioning of the pressure-radius signal. The preconditioning computes an average pressure-radius cycle from all consecutive cycles in the registration and removes, or reduces, undesirable disturbances. The effect is a robust parameter identification that gives a unique solution. The proposed method is tested on clinical data from three human abdominal aortas and the results show that the material parameters from the proposed method do not differ significantly (p < 0.01) from the corresponding parameters obtained by averaging the result from consecutive cycles.

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Mesh:

Year:  2008        PMID: 18347824     DOI: 10.1007/s10237-008-0124-3

Source DB:  PubMed          Journal:  Biomech Model Mechanobiol        ISSN: 1617-7940


  12 in total

1.  Determination of the layer-specific distributed collagen fibre orientations in human thoracic and abdominal aortas and common iliac arteries.

Authors:  Andreas J Schriefl; Georg Zeindlinger; David M Pierce; Peter Regitnig; Gerhard A Holzapfel
Journal:  J R Soc Interface       Date:  2011-12-14       Impact factor: 4.118

2.  Quantitative assessment of collagen fibre orientations from two-dimensional images of soft biological tissues.

Authors:  Andreas J Schriefl; Andreas J Reinisch; Sethuraman Sankaran; David M Pierce; Gerhard A Holzapfel
Journal:  J R Soc Interface       Date:  2012-07-04       Impact factor: 4.118

3.  An automated approach for three-dimensional quantification of fibrillar structures in optically cleared soft biological tissues.

Authors:  Andreas J Schriefl; Heimo Wolinski; Peter Regitnig; Sepp D Kohlwein; Gerhard A Holzapfel
Journal:  J R Soc Interface       Date:  2012-12-26       Impact factor: 4.118

4.  Estimation of in vivo constitutive parameters of the aortic wall using a machine learning approach.

Authors:  Minliang Liu; Liang Liang; Wei Sun
Journal:  Comput Methods Appl Mech Eng       Date:  2018-12-28       Impact factor: 6.756

5.  Characterizing heterogeneous properties of cerebral aneurysms with unknown stress-free geometry: a precursor to in vivo identification.

Authors:  Xuefeng Zhao; Madhavan L Raghavan; Jia Lu
Journal:  J Biomech Eng       Date:  2011-05       Impact factor: 2.097

6.  Estimation of in vivo mechanical properties of the aortic wall: A multi-resolution direct search approach.

Authors:  Minliang Liu; Liang Liang; Wei Sun
Journal:  J Mech Behav Biomed Mater       Date:  2017-10-20

7.  Novel Methodology for Characterizing Regional Variations in the Material Properties of Murine Aortas.

Authors:  Matthew R Bersi; Chiara Bellini; Paolo Di Achille; Jay D Humphrey; Katia Genovese; Stéphane Avril
Journal:  J Biomech Eng       Date:  2016-07-01       Impact factor: 2.097

8.  Arterial pulse attenuation prediction using the decaying rate of a pressure wave in a viscoelastic material model.

Authors:  J Menacho; L Rotllant; J J Molins; G Reyes; A A García-Granada; M Balcells; J Martorell
Journal:  Biomech Model Mechanobiol       Date:  2017-11-22

9.  Mechanical Characterization of the Vessel Wall by Data Assimilation of Intravascular Ultrasound Studies.

Authors:  Gonzalo D Maso Talou; Pablo J Blanco; Gonzalo D Ares; Cristiano Guedes Bezerra; Pedro A Lemos; Raúl A Feijóo
Journal:  Front Physiol       Date:  2018-03-28       Impact factor: 4.566

10.  Identification of in vivo nonlinear anisotropic mechanical properties of ascending thoracic aortic aneurysm from patient-specific CT scans.

Authors:  Minliang Liu; Liang Liang; Fatiesa Sulejmani; Xiaoying Lou; Glen Iannucci; Edward Chen; Bradley Leshnower; Wei Sun
Journal:  Sci Rep       Date:  2019-09-10       Impact factor: 4.996

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