ICI, High Performance Computing Institute, Ecole Centrale de Nantes, France.
ICI, High Performance Computing Institute, Ecole Centrale de Nantes, France.
Comput Methods Programs Biomed. 2019 Mar;170:95-106. doi: 10.1016/j.cmpb.2019.01.003. Epub 2019 Jan 11.
This paper focuses on computer simulation aspects of Digital Twin models in the medical framework. In particular, it addresses the need of fast and accurate simulators for the mechanical response at tissue and organ scale and the capability of integrating patient-specific anatomy from medical images to pinpoint the individual variations from standard anatomical models.
We propose an automated procedure to create mechanical models of the human liver with patient-specific geometry and real time capabilities. The method hinges on the use of Statistical Shape Analysis to extract the relevant anatomical features from a database of medical images and Model Order Reduction to compute an explicit parametric solution for the mechanical response as a function of such features. The Sparse Subspace Learning, coupled with a Finite Element solver, was chosen to create low-rank solutions using a non-intrusive sparse sampling of the feature space.
In the application presented in the paper, the statistical shape model was trained on a database of 385 three dimensional liver shapes, extracted from medical images, in order to create a parametrized representation of the liver anatomy. This parametrization and an additional parameter describing the breathing motion in linear elasticity were then used as input in the reduced order model. Results show a consistent agreement with the high fidelity Finite Element models built from liver images that were excluded from the training dataset. However, we evidence in the discussion the difficulty of having compact shape parametrizations arising from the extreme variability of the shapes found in the dataset and we propose potential strategies to tackle this issue.
A method to represent patient-specific real-time liver deformations during breathing is proposed in linear elasticity. Since the proposed method does not require any adaptation to the direct Finite Element solver used in the training phase, the procedure can be easily extended to more complex non-linear constitutive behaviors - such as hyperelasticity - and more general load cases. Therefore it can be integrated with little intrusiveness to generic simulation software including more sophisticated and realistic models.
本文主要关注医学框架中数字孪生模型的计算机模拟方面。特别是，它需要针对组织和器官尺度的机械响应开发快速且精确的模拟器，并具备从医学图像中整合患者特定解剖结构的能力，以确定标准解剖模型的个体差异。
我们提出了一种自动化程序，用于创建具有患者特定几何形状和实时功能的人类肝脏机械模型。该方法的关键在于使用统计形状分析从医学图像数据库中提取相关解剖特征，并使用模型降阶技术计算机械响应的显式参数化解，作为这些特征的函数。稀疏子空间学习与有限元求解器相结合，用于通过对特征空间进行非侵入式稀疏采样来创建低秩解。
在本文所呈现的应用中，统计形状模型是在一个包含 385 个三维肝脏形状的数据库上进行训练的，这些形状是从医学图像中提取出来的，以便创建肝脏解剖结构的参数化表示。然后，将这种参数化表示和描述线性弹性中呼吸运动的附加参数用作降阶模型的输入。结果与从训练数据集排除的肝脏图像建立的高保真有限元模型具有一致的吻合度。然而，我们在讨论中证明了由于数据集内形状的极端可变性，很难得到紧凑的形状参数化，并提出了潜在的策略来解决这个问题。
提出了一种在线性弹性中表示患者特定的实时肝脏变形的方法。由于所提出的方法不需要对训练阶段中直接使用的有限元求解器进行任何适应，因此可以很容易地将其扩展到更复杂的非线性本构行为，如超弹性，以及更一般的加载情况。因此，它可以与包括更复杂和现实模型的通用仿真软件进行集成，而无需过多的干扰。
