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

拉伤和骨折愈合：基于图像的力学模型重新定义规则

基本信息

批准号：
10510045
负责人：
Hannah Dailey
金额：
$ 14.83万
依托单位：
LEHIGH UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10510045
关键词：
3-Dimensional Address Animals Autopsy Awareness Biology Biomechanics Bone Density Bone callus Caring Clinical Clinical Research Complex Coupled Data Data Aggregation Decision Making Development Elements Engineering Environment Equation Fracture Gait Goals Heart Human Image Image Analysis Immature Bone Implant Injury Learning Libraries Link Mathematics Measures Mechanics Methodology Methods Modeling Motion Movement Operative Surgical Procedures Orthopedics Osteogenesis Osteotomy Patients Pattern Physics Physiological Process Publications Research Resolution Risk Role Sampling Scanning Sheep Stains Stimulus Stretching Surface Surgeon Techniques Textbooks Thinking Three-Dimensional Imaging Tissues Training Uncertainty Work base bone bone fracture repair bone healing bone loss bone repair clinical decision-making cortical bone curve fitting data mining demineralization density design experience healing imaging modality implant design improved in vivo innovation insight kinematics microCT mineralization online resource patient response reconstruction simulation soft tissue theories tibia tool translational potential virtual

项目摘要

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.

项目概要这项研究的长期目标是了解影响骨折愈合的机械因素在大型动物和人类中，在骨骼愈合的早期阶段，骨折的碎片可以移动。这些微小的运动会拉伸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，应变就与生物学密切相关。骨折修复，但在骨愈合的背景下解释应变的概念框架还没有如今，骨科医生敏锐地意识到应变调节骨折愈合，但他们无法在患者身上进行测量。权威的临床教科书充满了非特异性的、令人震惊的、在缺乏明确的情况下，关于在不良应变环境下修复骨折的风险的不切实际的建议。在指导下，外科医生学会依靠生物力学经验法则来为某些特定情况选择合适的植入物几十年来关于应变和骨愈合的混合信息和间接讨论已经产生。对临床培训和种植体设计的创新造成了重大障碍，目前存在一个重大的未满足的需求。开发创新的新研究工具，可以提供有关机械应变如何调节骨骼的见解为了满足这一需求，我们将汇集一套复杂的基于物理的模型和图像。分析技术可以完成迄今为止不可能完成的任务：直接评估组织水平的应变并显示它与骨折愈合过程的关系对于第一个目标，我们有两个技术目标。将使用微计算机断层扫描 (μCT) 扫描来创建绵羊胫骨的 3D 虚拟重建对于手术后愈合的骨折，我们将模拟步态引起的骨骼负载，并使用模型来测量断裂线内和周围的应变从模型测量的应变将与空间相关。第二个目标的重点是确定新骨的形成和允许应变的阈值。基于图像的模型将再次研究愈合骨折附近旧骨中发生的适应性变化。用于测量应变，但现在图像中高分辨率数据的空间互相关将用于评估旧骨外表面的应变是否与骨矿物质的内部损失有关与受伤前相比，该项目的结果将为新的范例铺平道路。尽管我们将研究绵羊，但突破性的方法论是关于拉伤和骨骼愈合的。为该项目开发的同类药物在临床研究中具有很高的转化潜力。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。最终有助于改善复杂骨折治疗的临床决策，而复杂骨折仍然存在关于多大的应变对于骨折愈合来说在生物力学上是最佳的，外科医生之间存在着相当大的争论。