Anisotropic Inverse Mechanics Identifies Regional Changes in Mechanical Anisotropy During Remodelling of Fibroblast-Populated Collagen Cruciforms

2011 ◽  
Author(s):  
Ramesh Raghupathy ◽  
Spencer P. Lake ◽  
Edward A. Sander ◽  
Colleen Witzenburg ◽  
Victor H. Barocas
Keyword(s):  
Anisotropic Material ◽  
Soft Tissues ◽  
Material Behavior ◽  
Mechanical Anisotropy ◽  
Tissue Samples ◽  
Intact Tissue ◽  
Tissue Equivalents ◽  
Standard Tests ◽  
Collagen Tissue

Most elastographic methods applied to soft tissues assume either isotropy or homogeneity in the sample. While this assumption is valid in specific cases, general methods that can identify regional changes in mechanical anisotropy have many advantages. Chiefly, such methods could quantify regional anisotropic material behavior on intact tissue samples especially when the tissue is heterogeneous and too small for standard tests. In this study we use an inverse mechanics method which handles both anisotropy and heterogeneity to track changes in mechanical anisotropy associated with remodeling in cell-compacted collagen tissue equivalents (TE), which are then compared with measurements from polarimetry to estimate the method’s accuracy.

Generalized Anisotropic Inverse Mechanics: Mechanical Anisotropy Correlates With Structural Anisotropy in Collagen Based Tissue Equivalents

2010 ◽  
Author(s):  
Ramesh Raghupathy ◽  
Spencer P. Lake ◽  
Edward A. Sander ◽  
Victor H. Barocas
Keyword(s):  
Blood Vessels ◽  
Soft Tissues ◽  
Mechanical Anisotropy ◽  
Underlying Structure ◽  
Test Case ◽  
Inhomogeneous Materials ◽  
Structural Anisotropy ◽  
Fiber Alignment ◽  
Load Bearing ◽  
Tissue Equivalents

Few elastographic methods handle both anisotropy and inhomogeneity. Much of the focus has been on inhomogeneous materials that are locally isotropic. However, most load-bearing tissues (heart, ligament, blood vessels) are highly anisotropic, and the underlying structure is distinct and essential for function. With disease or damage, this structure is altered, and hence the potential for an elastographic tool that identifies regional changes in anisotropy is high. In this study we present a generalized anisotropic inverse mechanics (GAIM) method that is applicable to soft tissues and demonstrate its performance on tissue equivalents which serve as a convenient test case due to their inhomogeneity and the ease of pre-specifying the fiber alignment pattern.

scholarly journals Identification of Regional Mechanical Anisotropy in Soft Tissue Analogs

2011 ◽  
Vol 133 (9) ◽  
Author(s):  
Ramesh Raghupathy ◽  
Colleen Witzenburg ◽  
Spencer P. Lake ◽  
Edward A. Sander ◽  
Victor H. Barocas
Keyword(s):  
Soft Tissue ◽  
Regional Differences ◽  
Soft Tissues ◽  
Tissue Sample ◽  
Polarized Light ◽  
Simulated Data ◽  
Material Behavior ◽  
Mechanical Anisotropy ◽  
Structural Anisotropy ◽  
Full Field

In a previous work (Raghupathy and Barocas, 2010, “Generalized Anisotropic Inverse Mechanics for Soft Tissues,”J. Biomech. Eng., 132(8), pp. 081006), a generalized anisotropic inverse mechanics method applicable to soft tissues was presented and tested against simulated data. Here we demonstrate the ability of the method to identify regional differences in anisotropy from full-field displacements and boundary forces obtained from biaxial extension tests on soft tissue analogs. Tissue heterogeneity was evaluated by partitioning the domain into homogeneous subdomains. Tests on elastomer samples demonstrated the performance of the method on isotropic materials with uniform and nonuniform properties. Tests on fibroblast-remodeled collagen cruciforms indicated a strong correlation between local structural anisotropy (measured by polarized light microscopy) and the evaluated local mechanical anisotropy. The results demonstrate the potential to quantify regional anisotropic material behavior on an intact tissue sample.

A Sub-Domain Inverse Finite Element Characterization of Hyperelastic Membranes Including Soft Tissues

2003 ◽  
Vol 125 (3) ◽  
pp. 363-371 ◽  
Author(s):  
Padmanabhan Seshaiyer ◽  
Jay D. Humphrey
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Soft Tissues ◽  
Nonlinear Behavior ◽  
Local Stress ◽  
Biological Tissues ◽  
Material Behavior ◽  
Inverse Finite Element Method ◽  
Complicated Geometry ◽  
Element Method

Quantification of the mechanical behavior of hyperelastic membranes in their service configuration, particularly biological tissues, is often challenging because of the complicated geometry, material heterogeneity, and nonlinear behavior under finite strains. Parameter estimation thus requires sophisticated techniques like the inverse finite element method. These techniques can also become difficult to apply, however, if the domain and boundary conditions are complex (e.g. a non-axisymmetric aneurysm). Quantification can alternatively be achieved by applying the inverse finite element method over sub-domains rather than the entire domain. The advantage of this technique, which is consistent with standard experimental practice, is that one can assume homogeneity of the material behavior as well as of the local stress and strain fields. In this paper, we develop a sub-domain inverse finite element method for characterizing the material properties of inflated hyperelastic membranes, including soft tissues. We illustrate the performance of this method for three different classes of materials: neo-Hookean, Mooney Rivlin, and Fung-exponential.

Modeling the Inflation Response of C57BL/6 Mouse Sclera

2011 ◽  
Author(s):  
Kristin M. Myers ◽  
Thao D. Nguyen
Keyword(s):  
Material Properties ◽  
Soft Tissues ◽  
Ganglion Cells ◽  
The United States ◽  
Small Rodent ◽  
Tissue Samples ◽  
Tissue Microstructure ◽  
Material Parameters ◽  
Meaningful Material

Small rodent models have become increasingly useful to investigate how the mechanical properties of soft tissues may influence disease development. These animal models allow access to aged, diseased, or genetically-altered tissue samples, and through comparisons with wild-type or normal tissue it can be explored how each of these variables influence tissue function. The challenges to deriving meaningful material parameters for these small tissue samples include designing physiologically-relevant mechanical testing protocols and interpreting the experimental load-displacement data in an appropriate constitutive framework to quantify material parameters. This study was motivated by determining the possible role of scleral material properties in the development of glaucomatous damage to the retinal ganglion cells (RGC). Glaucoma is one of the leading causes of blindness in the United States and in the world with an estimate of 60 million people affected by this year [1]. Through exploring mouse models, the overall goal of our work is to determine the role of scleral material properties and scleral tissue microstructure in the pathogenesis of glaucoma.

Biomechanics of Soft Tissues: The Role of the Mathematical Model on Material Behavior

2019 ◽  
pp. 301-311
Author(s):  
Carlos Bustamante-Orellana ◽  
Robinson Guachi ◽  
Lorena Guachi-Guachi ◽  
Simone Novelli ◽  
Francesca Campana ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Soft Tissues ◽  
Material Behavior ◽  
The Mathematical Model

Hyperelastic Muscle Simulation

2007 ◽  
Vol 345-346 ◽  
pp. 1241-1244 ◽  
Author(s):  
Mohd. Zahid Ansari ◽  
Sang Kyo Lee ◽  
Chong Du Cho
Keyword(s):  
Skeletal Muscle ◽  
Silicone Rubber ◽  
Soft Tissues ◽  
Material Model ◽  
Incompressible Material ◽  
Material Behavior ◽  
Rubber Tube ◽  
Stress Strain ◽  
Element Analysis ◽  
Strain Behavior

Biological soft tissues like muscles and cartilages are anisotropic, inhomogeneous, and nearly incompressible. The incompressible material behavior may lead to some difficulties in numerical simulation, such as volumetric locking and solution divergence. Mixed u-P formulations can be used to overcome incompressible material problems. The hyperelastic materials can be used to describe the biological skeletal muscle behavior. In this study, experiments are conducted to obtain the stress-strain behavior of a solid silicone rubber tube. It is used to emulate the skeletal muscle tensile behavior. The stress-strain behavior of silicone is compared with that of muscles. A commercial finite element analysis package ABAQUS is used to simulate the stress-strain behavior of silicone rubber. Results show that mixed u-P formulations with hyperelastic material model can be used to successfully simulate the muscle material behavior. Such an analysis can be used to simulate and analyze other soft tissues that show similar behavior.

scholarly journals Measurement of strontium in serum, urine, bone, and soft tissues by Zeeman atomic absorption spectrometry

1997 ◽  
Vol 43 (1) ◽  
pp. 121-128 ◽  
Author(s):  
Patrick C D’Haese ◽  
Glen F Van Landeghem ◽  
Ludwig V Lamberts ◽  
Vera A Bekaert ◽  
Iris Schrooten ◽  
...  
Keyword(s):  
Renal Failure ◽  
Chronic Renal Failure ◽  
Atomic Absorption Spectrometry ◽  
Atomic Absorption ◽  
Soft Tissues ◽  
Absorption Spectrometry ◽  
Serum Samples ◽  
Tissue Samples ◽  
Triton X

Abstract To study the possible accumulation of Sr in chronic renal failure patients, methods were developed for the determination of the element in serum, urine, bone, and soft tissues by using Zeeman atomic absorption spectrometry. Serum samples were diluted 1:4 with a Triton X-100–HNO3 mixture, whereas urine samples were diluted 1:20 with HNO3. Bone samples were digested with concentrated HNO3 in stoppered polytetrafluoroethylene (Teflon®) tubes, whereas soft tissues were dissolved in a tetramethylammonium hydroxide solution in water. For serum and urine we used matrix-matched calibration curves, whereas bone and tissue samples were measured against aqueous calibrators. Atomization was performed from the wall of pyrolytically coated graphite tubes for all of the matrices under study. Both inter- and intraassay CVs were <6% (n = 12, n = 10, respectively), and the recovery of added analyte was close to 100% for all of the biological matrices under study. Detection limits were 1.2 μg/L (serum), 0.3 μg/L (urine), 0.4 μg/g (bone), and 2.2 ng/g (soft tissues), whereas the sensitivity determined by the slope of the calibration curve, i.e., the amount of Sr producing a 0.0044 integrated absorbance change in signal, was 2.4 pg, 2.4 pg, 3.9 pg, and 2.6 pg for these matrices respectively. We conclude that the present methods are precise and accurate and easily applicable for both routine use and research investigations. They will allow us to study the metabolism of the element in chronic renal failure patients and shed some light on the association that was recently noted between increased bone Sr concentrations and the development of osteomalacia in these individuals.

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