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Combining statistical and biomechanical models for estimation of anatomical deformations.

机译：结合统计模型和生物力学模型来估计解剖变形。

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Approaches that combine the use of biomechanical and statistical models to estimate or predict the deformation of anatomical soft tissues are explored. Through training samples generated via biomechanical simulations or extracted from medical images, statistical models can learn the relationships between a deformation of interest and variables that affect this deformation. This allows a variety of paradigms for estimating these deformations and fitting them to imaging data in a number of important clinical applications.; This work presents several important contributions. First, the developed methods are demonstrated primarily through their use to register brain atlases to radiological images of brain tumor patients with gross deformities and topological changes introduced by the tumor. Solving this largely unexplored deformable registration problem makes the rich information collected from a large number of individuals available for aiding neurosurgical planning on individual patients. Second, an accurate three-dimensional finite element model of brain tissue deformation induced by growing tumors is developed. Unlike similar models in the literature, the presented model includes the mass-effect of peri-tumor edema as well as the bulk tumor, and includes methods that allow the simulation of the large deformations caused by some real tumors.; Although the developed statistical methods may depend on biomechanical models for training, the former provide the advantages of being faster and being able to solve ill-posed inverse problems which are otherwise not possible to solve using forward biomechanical models. Furthermore, to address some of the problems associated with statistical learning in high dimensional shape spaces based on a relatively small number of training samples, a multi-scale analysis approach using the wavelet packets library and best basis selection is proposed. Through the natural idea of analyzing shapes at multiple scales independently, the accuracy of the proposed statistical estimators is improved.
机译：探索了结合使用生物力学和统计模型来估计或预测解剖软组织变形的方法。通过训练通过生物力学模拟生成或从医学图像中提取的样本，统计模型可以了解感兴趣的变形与影响该变形的变量之间的关系。在许多重要的临床应用中，这提供了各种范式来估计这些变形并使它们适合成像数据。这项工作提出了一些重要的贡献。首先，主要通过其用于将脑图谱配准到具有严重畸形和肿瘤引起的拓扑变化的脑肿瘤患者的放射影像中来证明所开发的方法。解决这个很大程度上无法探索的可变形配准问题，可以使从大量个人那里收集到的丰富信息可用于帮助对个别患者进行神经外科手术计划。其次，建立了由生长中的肿瘤引起的脑组织变形的精确三维有限元模型。与文献中的类似模型不同，所提出的模型包括肿瘤周围水肿以及块状肿瘤的质量效应，并包括允许模拟由某些真实肿瘤引起的大变形的方法。尽管已开发的统计方法可能依赖于生物力学模型进行训练，但前者具有以下优点：速度更快，并且能够解决不适定的逆问题，而使用正向生物力学模型则无法解决这些问题。此外，为了解决基于较少数量训练样本的高维形状空间中与统计学习相关的一些问题，提出了一种使用小波包库和最佳基础选择的多尺度分析方法。通过自然地分析多个尺度上的形状的自然思想，所提出的统计估计量的准确性得以提高。

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