O'Hagan Joseph J, Samani Abbas
Department of Electrical and Computer Engineering, University of Western Ontario, London, ON, Canada.
Phys Med Biol. 2009 Apr 21;54(8):2557-69. doi: 10.1088/0031-9155/54/8/020. Epub 2009 Apr 6.
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.
生物软组织的弹性和超弹性特性一直是医学界关注的焦点。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、针吸活检和近距离放射治疗，其中涉及组织生物力学建模。另一个重要应用是解释非线性弹性成像图像。虽然在小组织样本的线性弹性模量测量方面已经有了大量研究，但对于测量组织切片标本中组织非线性弹性特征参数的研究却很少。这项工作展示了44个离体病理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模型、N = 2多项式模型、N = 1 Ogden模型、Arruda - Boyce模型和Veronda - Westmann模型。结果表明，就拟合实验数据而言，Yeoh模型、多项式模型和Ogden模型最为准确。结果表明，几乎所有与病理组织对应的参数都比正常组织的参数大两倍到两个数量级以上，其中C(11)的差异最为显著。此外，统计分析表明，Yeoh模型的C(02)以及多项式模型的C(11)和C(20)在癌症分类方面具有很大潜力，因为它们在不同癌症类型中显示出统计学上的显著差异，尤其是浸润性小叶癌。除了在癌症分类中的应用潜力外，所呈现的数据对于手术规划和基于虚拟现实的临床医生培训系统等需要精确非线性组织反应建模的应用也非常重要。
