Strain and Bone Fracture Healing: Image-Based Mechanics Models to Redefine the Rules

项目总结 这项研究的长期目标是了解影响骨折愈合的力学因素。 在大型动物和人类身上。在骨折愈合的早期阶段，骨折的碎片可以移动 相互之间的关系。这些微小的运动拉伸了参与早期骨折修复的软组织， 产生一种称为应变的机械效应。自20世纪70年代以来，菌株一直与生物学密切相关 骨折修复的概念框架，但在骨愈合背景下解释应变的概念框架还没有 经过了四十年的进化。今天，骨科医生敏锐地意识到劳损调节骨折愈合， 但他们不能在他们的病人身上测量它。权威的临床教科书中充斥着不特定的、令人担忧的、 以及关于在恶劣的紧张环境下固定骨折的风险的不切实际的建议。在没有明确的情况下 指导，外科医生学会依靠生物力学的经验法则来确定如何选择正确的植入物 骨折的类型。几十年来关于劳损和骨骼愈合的混杂信息和间接讨论已经 在临床培训和植入物设计方面造成了重大的创新障碍。现在有一个主要的未得到满足的需求 开发创新的新研究工具，为机械应变如何调节骨骼提供见解 治愈。为了满足这一需求，我们将汇集一套复杂的基于物理的模型和图像 到目前为止还不可能做到的分析技术：直接在组织水平上评估应变并显示 它与骨折愈合过程有关。这项研究有两个技术目标。对于第一个目标，我们 将使用微型计算机断层扫描(µCT)来创建绵羊胫骨的3D虚拟重建 骨折在手术后痊愈。我们将模拟骨骼上由步态引起的载荷，并使用这些模型 测量断裂线及其周围的应变。从模型测量的应变将在空间上与 新的骨形成和允许应变的阈值将被确定。在第二个目标中，重点是 发生在愈合骨折附近的老骨头上的适应性变化。基于图像的模型将再次成为 过去用于测量应变，但现在将使用来自图像的高分辨率数据的空间互相关来 评估陈旧骨外表面的应变是否与骨矿物质的内部丢失有关 与受伤前的密度进行比较。这个项目的结果将为新的范例铺平道路 思考劳损和骨骼愈合的问题。尽管我们将研究绵羊，但开创性的方法 为该项目开发的具有很高的翻译潜力，可用于临床研究。相同类型的 建模和图像数据挖掘技术可用于研究人类患者的骨折愈合情况。这将是 最终有助于改善复杂骨折治疗的临床决策，在这些骨折中，仍有 在外科医生中，关于多少张力对骨折愈合是最优的，存在着相当大的争论。

PROJECT SUMMARY The long-term goal of this research is to understand the mechanical factors that influence bone fracture healing in large animals and humans. In the early stages of bone healing, the fragments of a broken bone can move relative to one another. These small movements stretch the soft tissues that are involved in early fracture repair, producing a mechanical effect known as strain. Since the 1970s, strain has been strongly linked with the biology of fracture repair, but the conceptual framework for explaining strain in the context of bone healing has not evolved in four decades. Today, orthopaedic surgeons are keenly aware that strain regulates fracture healing, but they cannot measure it in their patients. Authoritative clinical textbooks are riddled with nonspecific, alarming, and impractical advice about the risks of fixing a fracture with a bad strain environment. In the absence of clear guidance, surgeons learn to rely on biomechanical rules of thumb for how to select the right implant for certain types of fractures. Decades of mixed messaging and indirect discussion about strain and bone healing have created significant barriers to innovation in clinical training and implant design. There is now a major unmet need to develop innovative new research tools that can provide insights on how mechanical strain regulates bone healing. To address this need, we will bring together a suite of sophisticated physics-based models and image analysis techniques to do what has been impossible until now: directly assess strain at the tissue level and show its association with the processes of fracture healing. This research has two technical aims. For the first aim, we will use micro-computed tomography (µCT) scans to create 3D virtual reconstructions of the shinbones of sheep with fractures that healed after surgery. We will simulate gait-induced loads on the bones and use the models to measure strain in and around the fracture line. Strain measured from the models will be spatially correlated with the new bone formation and a threshold for allowable strain will be determined. In the second aim, the focus will be on adaptive changes that occur in old bone near a healing fracture. The image-based models will again be used to measure strain, but now spatial cross-correlation of high-resolution data from the images will be used to assess whether strain on the outer surface of the old bone is associated with an internal loss of bone mineral density compared to before the injury. The results from this project will pave the way for a new paradigm of thinking about strain and bone healing. Although we will be studying sheep, the groundbreaking methodologies developed for this project have high translational potential for use in clinical research. The same types of modeling and image data-mining techniques could be used to study fracture healing in human patients. This will ultimately help improve clinical decision-making for treatment of complex fractures, where there is still considerable debate among surgeons about how much strain is biomechanically optimal for fracture healing.