Methodology based on genetic heuristics for in-vivo characterizing the patient-specific biomechanical behavior of the breast tissues.
Methodology based on genetic heuristics for in-vivo characterizing the patient-specific biomechanical behavior of the breast tissues.
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DOI：
10.1016/j.eswa.2015.05.058
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发表时间：
2015-11-30
影响因子：
8.5
通讯作者：
Monserrat C
中科院分区：
文献类型：
--
作者：
Lago MA;Rúperez MJ;Martínez-Martínez F;Martínez-Sanchis S;Bakic PR;Monserrat C
本文提出了一种新的方法来在体内估计的弹性常数的本构模型，提出了表征乳腺组织的力学行为。构建了一种基于遗传算法的迭代搜索算法，仅使用医学图像在体内估计这些参数，从而避免了对乳腺组织机械响应的侵入性测量。对于第一次，重叠和距离系数的组合用于乳房的变形MRI和该变形的模拟之间的相似性的评价。该方法使用乳腺软件phanomaly进行虚拟临床试验，压缩模拟MRI引导活检。选择表征乳腺组织的生物力学模型是各向异性neo-Hookean超弹性模型。分析结果表明，该算法能够找到该模型本构方程的弹性常数，平均相对误差约为10%。此外，参考变形和模拟变形之间的重叠约为95%，表明所提出的方法具有良好的性能。这种方法可以很容易地扩展到表征乳房组织的真实的生物力学行为，这意味着在乳房行为的模拟领域中的一个很大的新奇，用于诸如手术规划、手术引导或癌症诊断的应用。这揭示了所提出的工作的影响和相关性。
This paper presents a novel methodology to in-vivo estimate the elastic constants of a constitutive model proposed to characterize the mechanical behavior of the breast tissues. An iterative search algorithm based on genetic heuristics was constructed to in-vivo estimate these parameters using only medical images, thus avoiding invasive measurements of the mechanical response of the breast tissues. For the first time, a combination of overlap and distance coefficients were used for the evaluation of the similarity between a deformed MRI of the breast and a simulation of that deformation. The methodology was validated using breast software phantoms for virtual clinical trials, compressed to mimic MRI-guided biopsies. The biomechanical model chosen to characterize the breast tissues was an anisotropic neo-Hookean hyperelastic model. Results from this analysis showed that the algorithm is able to find the elastic constants of the constitutive equations of the proposed model with a mean relative error of about 10%. Furthermore, the overlap between the reference deformation and the simulated deformation was of around 95% showing the good performance of the proposed methodology. This methodology can be easily extended to characterize the real biomechanical behavior of the breast tissues, which means a great novelty in the field of the simulation of the breast behavior for applications such as surgical planing, surgical guidance or cancer diagnosis. This reveals the impact and relevance of the presented work.