Development of patient-specific biomechanical models for predicting large breast deformation
Development of patient-specific biomechanical models for predicting large breast deformation
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DOI：
10.1088/0031-9155/57/2/455
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发表时间：
2012-01-21
影响因子：
3.5
通讯作者：
Hawkes, David J.
中科院分区：
文献类型：
--
作者：
Han, Lianghao;Hipwell, John H.;Hawkes, David J.
乳房大变形的物理真实感仿真对于许多医学应用，如癌症诊断、图像配准、手术规划和图像引导手术，具有极大的意义。为了在临床环境中支持快速、大变形的乳房模拟，我们提出了一个针对乳房的患者特定生物力学建模框架，该框架基于一个基于开源图形处理单元的、显式的、动态的、非线性有限元（FE）求解器。组织分类的半自动分割方法与全自动FE网格生成方法相结合，实现了快速的患者特定FE模型生成。为解决有限元模拟中在体软组织材料参数难以确定的问题，提出了一种新的乳腺建模方法，同时对在体软组织材料模型参数进行了优化。通过迭代更新材料模型参数以最大化FE预测的MR图像与实验获取的乳腺MR图像之间的图像相似性来获得优化的变形预测。通过模拟和分析平板压缩下的乳房变形实验，对所提出的方法进行了验证和检验。通过计算界标位移误差来评价其预测精度。结果表明，软组织的异质性和各向异性在预测钢板压缩下的乳房大变形方面是必不可少的。作为一种通用的方法，该方法可用于医学图像分析和手术模拟中软组织的快速变形分析。
Physically realistic simulations for large breast deformation are of great interest for many medical applications such as cancer diagnosis, image registration, surgical planning and image-guided surgery. To support fast, large deformation simulations of breasts in clinical settings, we proposed a patient-specific biomechanical modelling framework for breasts, based on an open-source graphics processing unit-based, explicit, dynamic, nonlinear finite element (FE) solver. A semi-automatic segmentation method for tissue classification, integrated with a fully automated FE mesh generation approach, was implemented for quick patient-specific FE model generation. To solve the difficulty in determining material parameters of soft tissues in vivo for FE simulations, a novel method for breast modelling, with a simultaneous material model parameter optimization for soft tissues in vivo, was also proposed. The optimized deformation prediction was obtained through iteratively updating material model parameters to maximize the image similarity between the FE-predicted MR image and the experimentally acquired MR image of a breast. The proposed method was validated and tested by simulating and analysing breast deformation experiments under plate compression. Its prediction accuracy was evaluated by calculating landmark displacement errors. The results showed that both the heterogeneity and the anisotropy of soft tissues were essential in predicting large breast deformations under plate compression. As a generalized method, the proposed process can be used for fast deformation analyses of soft tissues in medical image analyses and surgical simulations.