Non-invasive assessment of bone strength

本研究的长期目标是开发无创评估骨关节疾病患者骨质量和强度的方法。该建议的核心是使用新建立的人体微计算机断层扫描（µCT）和临床CT来生成可以确定骨骼强度的患者特定有限元（FE）模型。为了改善骨质疏松症患者的护理，该研究项目有四个具体目标，这些目标是基于十年的工作。首先，工作将推进我们定制的有限元代码，这些代码设计用于使用标准桌面工作站以快速，有效的方式解决超大模型（数百万个元素的10个）。这与以前使用“超级计算机”的必要性有了很大的不同，使这项技术更容易获得。重要的建模进展将包括用于改进骨强度估计的复杂非线性材料模型。其次，将开发分析复杂3D CT图像中包含的人类股骨和/或椎骨的能力，并形成自动化过程的核心，从而能够从CT中估计骨骼强度。在第三个目标中，利用潜在的材料结构信息丰富CT数据将基于一种新的骨图谱的发展，该图谱包含详细的微结构模式，以增强骨强度的有限元预测。与目前使用CT密度值来假设材料特性的简单方法相比，这种方法将是一个重大的进步。最后，将开发机器学习工具来利用基于图像的骨强度有限元模型预测，以便可以计算特定主题的骨强度估计。这项研究的重点是重要的，因为它将建立一种新的方法，通过结合最先进的成像技术和专门的有限元建模来早期检测和监测骨强度。这种方法将为更好地理解骨质量和骨折负荷之间的关系提供基础，并为更好地管理骨质疏松症等破坏性疾病提供急需的工程贡献。

The long-term goal of this research is to develop methods for non-invasive assessment of bone quality and strength in patients suffering from bone and joint diseases. The core of this proposal uses newly established human micro-computed tomography (µCT) and clinical CT to generate patient-specific finite element (FE) models that can determine bone strength. There are four specific aims of the research program working towards the goal of improving care for patients with osteoporosis, and these are based on a decade of work. First, work will advance our custom FE code designed to solve extremely large models (10's of millions of elements) in a fast, efficient manner using standard desktop workstations. This is a major departure from the previous necessity of using 'super computers' and makes the technology significantly more accessible. Important modeling advances will include sophisticated non-linear material models for an improved estimate of bone strength. Second, the ability to analyse a human femur and/or vertebrae that is contained in complex 3D CT images will be developed, and form the core of an automated process to enable bone strength estimation from CT. In the third aim, enriching CT data with underlying material fabric information will be based on the development of a novel bone atlas that contains detailed microarchitectural patterns to enhance the FE prediction of bone strength. This approach will be a major advancement over the currently simplistic approach of using CT density values to assume material properties. Finally, machine-learning tools will be developed to utilize the image-based FE model predictions of bone strength so that a subject-specific estimate of bone strength can be calculated. This research focus is important, as it will establish a new approach for early detection and monitoring of bone strength by combining state-of-the-art imaging with specialized FE modeling. This approach will provide a basis for better understanding of the relationship between bone quality and fracture load, and provide a much-needed engineering contribution to better management of devastating diseases such as osteoporosis.