Measurement of the hyperelastic properties of 44 pathological ex vivo breast tissue samples
Measurement of the hyperelastic properties of 44 pathological ex vivo breast tissue samples
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DOI：
10.1088/0031-9155/54/8/020
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发表时间：
2009-04-21
影响因子：
3.5
通讯作者：
Samani, Abbas
中科院分区：
文献类型：
--
作者：
O'Hagan, Joseph J.;Samani, Abbas
生物软组织的弹性和超弹性特性一直是医学界感兴趣的问题。在一些生物医学应用中，表征这些特性的参数对于可靠的临床结果至关重要。这些应用包括手术规划、针吸活检和近距离治疗，其中涉及组织生物力学建模。另一个重要的应用是解释非线性弹性成像图像。虽然已经对小组织样品的线性弹性模量的测量进行了大量的研究，但是很少对测量表征组织切片样品中所包括的组织的非线性弹性的参数进行研究。本工作介绍了44个病理性离体乳腺组织样本的超弹性测量结果。对于每个样本，使用了五种超弹性模型，包括Yeoh，N = 2多项式，N = 1 Ogden，Arruda-Boyce和Veronda-Westmann模型。结果表明，杨元庆模型、多项式模型和奥格登模型对实验数据的拟合精度最高。结果表明，几乎所有对应于病理组织的参数都比正常组织的参数大两倍至两个数量级以上，其中C-11显示出最显著的差异。此外，统计分析表明，Yeoh模型的C-02和多项式模型的C-11和C-20具有很好的癌症分类潜力，因为它们对各种癌症类型，特别是浸润性小叶癌显示出统计学上的显著差异。除了在癌症分类中使用的潜力之外，所呈现的数据对于诸如手术规划和基于虚拟现实的临床医生培训系统的应用是非常重要的，其中需要精确的非线性组织反应建模。
The elastic and hyperelastic properties of biological soft tissues have been of interest to the medical community. There are several biomedical applications where parameters characterizing such properties are critical for a reliable clinical outcome. These applications include surgery planning, needle biopsy and brachtherapy where tissue biomechanical modeling is involved. Another important application is interpreting nonlinear elastography images. While there has been considerable research on the measurement of the linear elastic modulus of small tissue samples, little research has been conducted for measuring parameters that characterize the nonlinear elasticity of tissues included in tissue slice specimens. This work presents hyperelastic measurement results of 44 pathological ex vivo breast tissue samples. For each sample, five hyperelastic models have been used, including the Yeoh, N = 2 polynomial, N = 1 Ogden, Arruda-Boyce, and Veronda-Westmann models. Results show that the Yeoh, polynomial and Ogden models are the most accurate in terms of fitting experimental data. The results indicate that almost all of the parameters corresponding to the pathological tissues are between two times to over two orders of magnitude larger than those of normal tissues, with C-11 showing the most significant difference. Furthermore, statistical analysis indicates that C-02 of the Yeoh model, and C-11 and C-20 of the polynomial model have very good potential for cancer classification as they show statistically significant differences for various cancer types, especially for invasive lobular carcinoma. In addition to the potential for use in cancer classification, the presented data are very important for applications such as surgery planning and virtual reality based clinician training systems where accurate nonlinear tissue response modeling is required.