Characterization of the stiffness distribution in two and three dimensions using boundary deformations: a preliminary study

Luo, Ping; Mei, Ying; Kotecha, Maulik; Abbasszadehrad, Amirhossein; Rabke, Stephen; Garner, Geoffrey; Goenezen, Sevan

doi:10.1557/mrc.2018.98

Cited by 13 publications

(9 citation statements)

References 13 publications

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“…Such an approach of finding relative elastography from displacement boundary conditions is common in the elastography. [ 41–43 ] The extension and implication of the same framework with a force or stress boundary condition are discussed later.…”

Section: Resultsmentioning

confidence: 99%

Image‐Based Elastography of Heterochromatin and Euchromatin Domains in the Deforming Cell Nucleus

Ghosh

Cuevas

Seelbinder

et al. 2021

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Chromatin of the eukaryotic cell nucleus comprises microscopically dense heterochromatin and loose euchromatin domains, each with distinct transcriptional ability and roles in cellular mechanotransduction. While recent methods are developed to characterize the mechanics of nucleus, measurement of intranuclear mechanics remains largely unknown. Here, the development of “nuclear elastography,” which combines microscopic imaging and computational modeling to quantify the relative elasticity of the heterochromatin and euchromatin domains, is described. Using contracting murine embryonic cardiomyocytes, nuclear elastography reveals that the heterochromatin is almost four times stiffer than the euchromatin at peak deformation. The relative elasticity between the two domains changes rapidly during the active deformation of the cardiomyocyte in the normal physiological condition but progresses more slowly in cells cultured in a mechanically stiff environment, although the relative stiffness at peak deformation does not change. Further, it is found that the disruption of the Klarsicht, ANC‐1, Syne Homology domain of the Linker of Nucleoskeleton and Cytoskeleton complex compromises the intranuclear elasticity distribution resulting in elastically similar heterochromatin and euchromatin. These results provide insight into the elastography dynamics of heterochromatin and euchromatin domains and provide a noninvasive framework to further investigate the mechanobiological function of subcellular and subnuclear domains limited only by the spatiotemporal resolution of the acquired images.

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Section: Resultsmentioning

confidence: 99%

Image‐Based Elastography of Heterochromatin and Euchromatin Domains in the Deforming Cell Nucleus

Ghosh

Cuevas

Seelbinder

et al. 2021

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“…We note that the relative match is in the order of the noise level as shown in our previous publications. 15,22 Solving for angular orientations and mechanical parameters for each grain Next, we delineate grain boundaries from image contrasts in the reconstructions of Figure 6. We illustrate this on a grain sample from Figure 7(a) with a pink overlayed mesh and extracted in Figure 7(b) for the elastic parameter C XYZ 11 .…”

Section: Reconstructions By Solving An Inverse Problemmentioning

confidence: 99%

“…We reiterate that we are minimizing equation (15) for two angles at each grain, thus, we are minimizing for 2 Ã g unknowns, where g represents the number of grains. We used lsqnonlin from the optimization toolbox and selected therein the trust-region-reflective optimization algorithm in MATLAB optimization script to solve for unknowns by minimizing our objective function.…”

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confidence: 99%

Identification of the 3D crystallographic orientation using 2D deformations

Goenezen

Kotecha

Reddy

2021

The Journal of Strain Analysis for Engineering Design

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Polycrystalline materials consist of grains (crystals) oriented at different angles resulting in a heterogeneous and anisotropic mechanical behavior at that micro-length scale. In this study, a novel method is proposed for the first time to determine the [Formula: see text] crystal orientations of grains in a [Formula: see text] domain, using solely [Formula: see text] deformation fields. The grain boundaries are assumed to be unknown and delineated from the reconstructed changes in the crystallographic orientation. Further, the constitutive equations that describe the mechanical behavior of the domain in [Formula: see text] under plane stress conditions are derived, assuming that the material is transversely isotropic in 3D. Finite element based algorithms are utilized to discretize the inverse problem. The in-house written inverse problem solver is coupled with Matlab-based optimization scripts to solve for the mechanical property distributions. The performance of this method is tested at different noise levels with synthetic displacements that were used as measured data. The reconstructions deteriorate as the noise level is increased. This work presents a first milestone in the verification of this novel technology with synthetic data.

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“…Similarly, the inverse model developed by Liu et al 15 for the estimation of nodule depth in prostate required a priori knowledge of the force feedback of and identical sample without a nodule, which may not be realized in practice. Notably, surface displacement as results of indentation was also used to inversely derive the distribution of shear modulus in soft tissue with stiff inclusions 16,17 and the results have shown promising capability of such methods even when high noise levels, although artificially created, were present. Other inverse methods, such as those based on artificial neural networks, [18][19][20] also require a priori knowledge of the nodule geometry or the stress distribution in the tissue.…”

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confidence: 99%

Identification of tumor nodule in soft tissue: An inverse finite‐element framework based on mechanical characterization

Candito

Palacio-Torralba

Jiménez‐Aguilar

et al. 2020

Numer Methods Biomed Eng

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Identification and characterization of nodules in soft tissue, including their size, shape, and location, provide a basis for tumor identification. This study proposes an inverse finite‐element (FE) based computational framework, for characterizing the size of examined tissue sample and detecting the presence of embedded tumor nodules using instrumented palpation, without a priori anatomical knowledge. The inverse analysis was applied to a model system, the human prostate, and was based on the reaction forces which can be obtained by trans‐rectal mechanical probing and those from an equivalent FE model, which was optimized iteratively, by minimizing an error function between the two cases, toward the target solution. The tumor nodule can be identified through its influence on the stress state of the prostate. The effectiveness of the proposed method was further verified using a realistic prostate model reconstructed from magnetic resonance (MR) images. The results show the proposed framework to be capable of characterizing the key geometrical indices of the prostate and identifying the presence of cancerous nodules. Therefore, it has potential, when combined with instrumented palpation, for primary diagnosis of prostate cancer, and, potentially, solid tumors in other types of soft tissue.

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Characterization of the stiffness distribution in two and three dimensions using boundary deformations: a preliminary study

Abstract: Abstract

Cited by 13 publications

References 13 publications

Image‐Based Elastography of Heterochromatin and Euchromatin Domains in the Deforming Cell Nucleus

Image‐Based Elastography of Heterochromatin and Euchromatin Domains in the Deforming Cell Nucleus

Identification of the 3D crystallographic orientation using 2D deformations

Identification of tumor nodule in soft tissue: An inverse finite‐element framework based on mechanical characterization

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