Creating Individual-specific Biomechanical Models of the Breast for Medical Image Analysis
Creating Individual-specific Biomechanical Models of the Breast for Medical Image Analysis
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DOI：
10.1016/j.acra.2008.07.017
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发表时间：
2008-11-01
影响因子：
4.8
通讯作者：
Nielsen, Poul M. F.
中科院分区：
文献类型：
--
作者：
Rajagopal, Vijay;Lee, Angela;Nielsen, Poul M. F.
理由和目标。乳房的解剖学上真实的生物力学模型潜在地提供了在医学图像（诸如乳房X线照片、磁共振成像（MRI）和超声）上映射组织位置的可靠方式。这项工作提出了一个新的建模框架，使我们能够创建个性化的乳房生物力学模型。我们证明了框架的能力，通过创建两个志愿者的左乳房的模型，并跟踪其变形的MRI。材料和方法。我们生成定制的有限元模型，自动拟合几何模型分割的数据从乳房MRI，并表征在体内的机械性能（假设同质性）的乳房组织。每一位志愿者我们通过在中性浮力（浸入水中）下获取乳房的MRI来识别未加载的配置。这种成像在临床环境中显然是不实用的;然而，这些以前无法获得的数据为我们提供了重要的数据，以验证乳腺生物力学模型。在中性浮力图像中识别内部组织特征并跟踪到建模框架。该模型预测变形的均方根误差为4.2和3.6毫米，预测皮肤表面的重力负荷状态为每个志愿者。每个志愿者的内部组织特征的平均误差为3.7和4.7 mm。该模型以临床可接受的精度捕获图像中的乳房形状和内部变形。进一步完善的框架和纳入更多的解剖细节将使这些模型用于乳腺癌的诊断。
Rationale and Objectives. Anatomically realistic biomechanical models of the breast potentially provide a reliable way of mapping tissue locations across medical images, such as mammograms, magnetic resonance imaging (MRI), and ultrasound. This work presents a new modeling framework that enables us to create biomechanical models of the breast that are customized to the individual. We demonstrate the framework's capabilities by creating models of the left breasts of two volunteers and tracking their deformations across MRIs.Materials and Methods. We generate customized finite element models by automatically fitting geometrical models to segmented data from breast MRIs, and characterizing the in vivo mechanical properties (assuming homogeneity) of the breast tissues. For each volunteer. we identified the unloaded configuration by acquiring MRIs of the breast under neutral buoyancy (immersed in water). Such imaging is clearly not practical in the clinical setting; however, these previously unavailable data provide us with important data with which to validate models of breast biomechanics. Internal tissue features were identified in the neutral buoyancy images and tracked to the modelling framework.Results. The models predicted deformations with root-mean-square errors of 4.2 and 3.6 mm in predicting the skin surface of the gravity-loaded state for each volunteer. Internal tissue features were tracked with a mean error of 3.7 and 4.7 mm for each volunteer.Conclusions. The models capture breast shape and internal deformations across the images with clinically acceptable accuracy. Further refinement of the framework and incorporation of more anatomic detail will make these models useful for breast cancer diagnosis.