• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

非线性弹性成像逆问题的解:不可压缩情形。

Solution of the nonlinear elasticity imaging inverse problem: The incompressible case.

作者信息

Goenezen Sevan, Barbone Paul, Oberai Assad A

机构信息

Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute,110, 8th St., Troy, NY 12180, USA.

出版信息

Comput Methods Appl Mech Eng. 2011 Mar 1;200(13-16):1406-1420. doi: 10.1016/j.cma.2010.12.018.

DOI:10.1016/j.cma.2010.12.018
PMID:21603066
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3096531/
Abstract

We have recently developed and tested an efficient algorithm for solving the nonlinear inverse elasticity problem for a compressible hyperelastic material. The data for this problem are the quasi-static deformation fields within the solid measured at two distinct overall strain levels. The main ingredients of our algorithm are a gradient based quasi-Newton minimization strategy, the use of adjoint equations and a novel strategy for continuation in the material parameters. In this paper we present several extensions to this algorithm. First, we extend it to incompressible media thereby extending its applicability to tissues which are nearly incompressible under slow deformation. We achieve this by solving the forward problem using a residual-based, stabilized, mixed finite element formulation which circumvents the Ladyzenskaya-Babuska-Brezzi condition. Second, we demonstrate how the recovery of the spatial distribution of the nonlinear parameter can be improved either by preconditioning the system of equations for the material parameters, or by splitting the problem into two distinct steps. Finally, we present a new strain energy density function with an exponential stress-strain behavior that yields a deviatoric stress tensor, thereby simplifying the interpretation of pressure when compared with other exponential functions. We test the overall approach by solving for the spatial distribution of material parameters from noisy, synthetic deformation fields.

摘要

我们最近开发并测试了一种高效算法,用于求解可压缩超弹性材料的非线性逆弹性问题。该问题的数据是在两个不同的整体应变水平下测量得到的固体内部的准静态变形场。我们算法的主要组成部分包括基于梯度的拟牛顿最小化策略、伴随方程的使用以及材料参数延续的新策略。在本文中,我们展示了对该算法的几种扩展。首先,我们将其扩展到不可压缩介质,从而将其适用性扩展到在缓慢变形下几乎不可压缩的组织。我们通过使用基于残差的、稳定的、混合有限元公式来求解正向问题来实现这一点,该公式规避了拉迪任斯卡娅 - 巴布斯卡 - 布雷zzi 条件。其次,我们展示了如何通过对方程组进行预处理或通过将问题拆分为两个不同步骤来改善非线性参数空间分布的恢复。最后,我们提出了一种具有指数应力 - 应变行为的新应变能密度函数,该函数产生偏应力张量,与其他指数函数相比,简化了压力的解释。我们通过从有噪声的合成变形场中求解材料参数的空间分布来测试整体方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/275282d94772/nihms269316f21.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/a8d8e8eae08f/nihms269316f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/f7926e286cd2/nihms269316f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/5c68ed45c658/nihms269316f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/4a5d4a3248d3/nihms269316f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/c396a8fc11ac/nihms269316f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/c51c544789bf/nihms269316f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/5b321e590c0b/nihms269316f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/cab4844dc3e4/nihms269316f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/f9d78685fb0c/nihms269316f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/3afe46d79168/nihms269316f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/3f6133d0ea2f/nihms269316f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/c524c51dd1ca/nihms269316f12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/7eb04f0fa43d/nihms269316f13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/bf8566b34b48/nihms269316f14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/50eeb66d59da/nihms269316f15.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/b3467ad71bfd/nihms269316f16.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/c7a9785e1f7b/nihms269316f17.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/4824176a9145/nihms269316f18.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/4b94e904621a/nihms269316f19.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/01814ff1cc5c/nihms269316f20.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/275282d94772/nihms269316f21.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/a8d8e8eae08f/nihms269316f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/f7926e286cd2/nihms269316f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/5c68ed45c658/nihms269316f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/4a5d4a3248d3/nihms269316f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/c396a8fc11ac/nihms269316f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/c51c544789bf/nihms269316f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/5b321e590c0b/nihms269316f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/cab4844dc3e4/nihms269316f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/f9d78685fb0c/nihms269316f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/3afe46d79168/nihms269316f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/3f6133d0ea2f/nihms269316f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/c524c51dd1ca/nihms269316f12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/7eb04f0fa43d/nihms269316f13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/bf8566b34b48/nihms269316f14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/50eeb66d59da/nihms269316f15.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/b3467ad71bfd/nihms269316f16.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/c7a9785e1f7b/nihms269316f17.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/4824176a9145/nihms269316f18.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/4b94e904621a/nihms269316f19.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/01814ff1cc5c/nihms269316f20.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3621/3096531/275282d94772/nihms269316f21.jpg

相似文献

1
Solution of the nonlinear elasticity imaging inverse problem: The incompressible case.非线性弹性成像逆问题的解:不可压缩情形。
Comput Methods Appl Mech Eng. 2011 Mar 1;200(13-16):1406-1420. doi: 10.1016/j.cma.2010.12.018.
2
Finite element methods for the biomechanics of soft hydrated tissues: nonlinear analysis and adaptive control of meshes.用于水合软组织生物力学的有限元方法:网格的非线性分析与自适应控制
Crit Rev Biomed Eng. 1992;20(3-4):279-313.
3
Transversely isotropic elasticity imaging of cancellous bone.松质骨的横向各向同性弹性成像
J Biomech Eng. 2011 Jun;133(6):061002. doi: 10.1115/1.4004231.
4
Mixed and Penalty Finite Element Models for the Nonlinear Behavior of Biphasic Soft Tissues in Finite Deformation: Part II - Nonlinear Examples.双相软组织在有限变形下非线性行为的混合罚函数有限元模型:第二部分 - 非线性示例
Comput Methods Biomech Biomed Engin. 1998;1(2):151-170. doi: 10.1080/01495739708936700.
5
Sparsity regularization in dynamic elastography.动态弹性成像中的稀疏正则化。
Phys Med Biol. 2012 Oct 7;57(19):5909-27. doi: 10.1088/0031-9155/57/19/5909. Epub 2012 Sep 7.
6
Unified three-dimensional finite elements for large strain analysis of compressible and nearly incompressible solids.用于可压缩和近不可压缩固体大应变分析的统一三维有限元
Mech Adv Mat Struct. 2023 Jul 6;31(1):117-137. doi: 10.1080/15376494.2023.2229832. eCollection 2024.
7
Mixed and Penalty Finite Element Models for the Nonlinear Behavior of Biphasic Soft Tissues in Finite Deformation: Part I - Alternate Formulations.有限变形下双相软组织非线性行为的混合罚有限元模型:第一部分 - 交替公式
Comput Methods Biomech Biomed Engin. 1997;1(1):25-46. doi: 10.1080/01495739708936693.
8
Stabilization approaches for the hyperelastic immersed boundary method for problems of large-deformation incompressible elasticity.用于大变形不可压缩弹性问题的超弹性浸入边界方法的稳定化方法。
Comput Methods Appl Mech Eng. 2020 Jun 15;365. doi: 10.1016/j.cma.2020.112978. Epub 2020 Apr 18.
9
A quasi-incompressible and quasi-inextensible finite element analysis of fibrous soft biological tissues.纤维状软生物组织的拟不可压缩和拟不可伸展有限元分析。
Biomech Model Mechanobiol. 2020 Dec;19(6):2357-2373. doi: 10.1007/s10237-020-01344-1. Epub 2020 Jun 15.
10
Identifiability of soft tissue constitutive parameters from in-vivo macro-indentation.基于体内宏观压痕的软组织本构参数可识别性
J Mech Behav Biomed Mater. 2023 Apr;140:105708. doi: 10.1016/j.jmbbm.2023.105708. Epub 2023 Feb 3.

引用本文的文献

1
[Reconstruction of elasticity modulus distribution base on semi-supervised neural network].基于半监督神经网络的弹性模量分布重建
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2024 Apr 25;41(2):262-271. doi: 10.7507/1001-5515.202306008.
2
Identification of a cantilever beam's spatially uncertain stiffness.确定悬臂梁的空间不确定刚度。
Sci Rep. 2023 Jan 20;13(1):1169. doi: 10.1038/s41598-023-27755-5.
3
Analyses of internal structures and defects in materials using physics-informed neural networks.利用物理信息神经网络对材料内部结构和缺陷进行分析。
Sci Adv. 2022 Feb 18;8(7):eabk0644. doi: 10.1126/sciadv.abk0644. Epub 2022 Feb 16.
4
Three-Dimensional Traction Microscopy with a Fiber-Based Constitutive Model.基于纤维本构模型的三维牵引显微镜
Comput Methods Appl Mech Eng. 2019 Dec 1;357. doi: 10.1016/j.cma.2019.112579. Epub 2019 Aug 17.
5
C-Elastography: In Vitro Feasibility Phantom Study.C 型弹性成像:体外可行性仿体研究。
Ultrasound Med Biol. 2020 Jul;46(7):1738-1754. doi: 10.1016/j.ultrasmedbio.2020.02.005. Epub 2020 Apr 18.
6
Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization.基于光学方法和元启发式优化的材料与结构的力学识别
Materials (Basel). 2019 Jul 2;12(13):2133. doi: 10.3390/ma12132133.
7
Volumetric quantitative optical coherence elastography with an iterative inversion method.基于迭代反演方法的体积定量光学相干弹性成像
Biomed Opt Express. 2019 Jan 3;10(2):384-398. doi: 10.1364/BOE.10.000384. eCollection 2019 Feb 1.
8
Efficient Sensitivity Based Reconstruction Technique to Accomplish Breast Hyperelastic Elastography.基于效率的敏感性重建技术在乳腺超弹性弹性成像中的应用。
Biomed Res Int. 2018 Nov 25;2018:3438470. doi: 10.1155/2018/3438470. eCollection 2018.
9
Data-Driven Elasticity Imaging Using Cartesian Neural Network Constitutive Models and the Autoprogressive Method.基于笛卡尔神经网络本构模型和自累进方法的数据驱动弹性成像。
IEEE Trans Med Imaging. 2019 May;38(5):1150-1160. doi: 10.1109/TMI.2018.2879495. Epub 2018 Nov 5.
10
In vivo estimation of elastic heterogeneity in an infarcted human heart.在体估计梗死人心室弹性异质性。
Biomech Model Mechanobiol. 2018 Oct;17(5):1317-1329. doi: 10.1007/s10237-018-1028-5. Epub 2018 May 17.

本文引用的文献

1
Measurement of the hyperelastic properties of 44 pathological ex vivo breast tissue samples.44例病理性离体乳腺组织样本超弹性特性的测量。
Phys Med Biol. 2009 Apr 21;54(8):2557-69. doi: 10.1088/0031-9155/54/8/020. Epub 2009 Apr 6.
2
Linear and nonlinear elasticity imaging of soft tissue in vivo: demonstration of feasibility.体内软组织的线性和非线性弹性成像:可行性证明
Phys Med Biol. 2009 Mar 7;54(5):1191-207. doi: 10.1088/0031-9155/54/5/006. Epub 2009 Jan 30.
3
Analysis of collagen fibre shape changes in breast cancer.乳腺癌中胶原纤维形状变化的分析
Phys Med Biol. 2008 Dec 7;53(23):6641-52. doi: 10.1088/0031-9155/53/23/001. Epub 2008 Nov 7.
4
Tensional homeostasis and the malignant phenotype.张力稳态与恶性表型
Cancer Cell. 2005 Sep;8(3):241-54. doi: 10.1016/j.ccr.2005.08.010.
5
A unified view of imaging the elastic properties of tissue.组织弹性特性成像的统一观点。
J Acoust Soc Am. 2005 May;117(5):2705-12. doi: 10.1121/1.1880772.
6
A method to measure the hyperelastic parameters of ex vivo breast tissue samples.一种测量离体乳腺组织样本超弹性参数的方法。
Phys Med Biol. 2004 Sep 21;49(18):4395-405. doi: 10.1088/0031-9155/49/18/014.
7
Evaluation of the adjoint equation based algorithm for elasticity imaging.基于伴随方程的弹性成像算法评估。
Phys Med Biol. 2004 Jul 7;49(13):2955-74. doi: 10.1088/0031-9155/49/13/013.
8
Selected methods for imaging elastic properties of biological tissues.用于成像生物组织弹性特性的选定方法。
Annu Rev Biomed Eng. 2003;5:57-78. doi: 10.1146/annurev.bioeng.5.040202.121623. Epub 2003 Apr 10.
9
Elastography: ultrasonic estimation and imaging of the elastic properties of tissues.弹性成像:组织弹性特性的超声评估与成像
Proc Inst Mech Eng H. 1999;213(3):203-33. doi: 10.1243/0954411991534933.
10
Elastic moduli of breast and prostate tissues under compression.乳腺和前列腺组织在压缩状态下的弹性模量。
Ultrason Imaging. 1998 Oct;20(4):260-74. doi: 10.1177/016173469802000403.