A computational model for prediction of morphology, patterning, and strength in bone regeneration

项目摘要/摘要 关于肢体再生过程的研究正在以快速的速度增长。对再生结果的评估 主要集中在数量上，但对再生后的结构质量和功能进行了调查 组织结构严重缺乏。需要新的工具来评估再生结果和治疗， 尤其是关于再生骨的结构质量和功能。开发新工具以更好地 评估和了解复杂的肢体再生过程直接有助于提高 使用新兴技术再生人类四肢，改善婴幼儿的生活质量 肢体缺陷，并使假体性能得到改善。小动物模型对于 针对受损骨骼的再生治疗的发展和人类使用的翻译管道。它是 必须开发计算工具来改进再生骨的生物力学评估，并 在空间上了解骨形成发生的地点和时间，以及如何加强骨形成。我们的数据 表明小鼠断指模型提供了一种重复性很高的骨再生模型 截肢。这个模型中的再生可以通过重复的、活体的、高分辨率的微型计算机进行跟踪 地形(µCT)随时间推移进行扫描，提供构建多尺度所需的独特有价值的数据 足趾的有限元(FE)模型。此外，我们发现我们能够合并空间局部化 力学性能，如根据µCT体积密度数据计算的杨氏弹性系数。以此为基础 我们将开发一种从µCT到FE的计算方法，并集成随机增长算法来 在时空上模拟再生骨的形态图样和生长。我们将验证 使用通过µCT数据测量的实际再生结果模拟增长，然后应用我们的 预测和测试全骨指力和现象学再损伤结果的方法。我们会 将计算结果与物理测试的骨骼进行比较，并确定骨骼的特定区域 加强治疗，防止因骨折而再次受伤。我们假设我们的有限元模型将能够在空间上 基于输入预测骨形成模式、整体骨强度和现象学再损伤行为 与初始形态和再生过程相关的参数。拟议中的项目将提高严格性 研究小鼠手指再生模型，提高我们对肢体再生治疗的认识 结果和方法。

Project Summary/Abstract Research on limb regeneration processes is growing at rapid pace. Evaluation of regenerative outcomes has primarily focused on quantity, but investigation into resulting structural quality and function of the regenerated tissue structure is critically lacking. New tools are needed to assess regenerative outcomes and treatments, especially with regard to the structural quality and function of regenerated bone. Developing new tools to better evaluate and understand complex limb regeneration processes directly serves to advance the ability to regenerate human limbs using emerging technology, to improve the quality of life of babies and children with limb defects, and to enable improved prosthesis performance. Small animal models are essential to the development of regenerative treatments targeting injured bone and the translational pipeline for human use. It is essential to develop computational tools to improve biomechanical evaluation of regenerated bone, and to spatially understand where and when bone formation is occurring and how it can be strengthened. Our data show that the mouse digit amputation model provides a highly reproducible model of bone regeneration after amputation. Regeneration in this model can be tracked with repeated, in vivo, high resolution micro-computed topography (µCT) scans over time, providing uniquely valuable data that is needed to construct a multiscale finite element (FE) model of the digit. Further, we found that we are able to incorporate spatially localized mechanical properties such as Young’s modulus computed from µCT volumetric density data. Building on this we will develop a µCT to FE computational approach with integrated stochastic growth algorithms to spatiotemporally simulate the morphological patterning and outgrowth of regenerated bone. We will validate simulated growth using actual regenerative outcomes as measured by µCT data, and then apply our whole approach to predict and test whole-bone digit strength and phenomenological reinjury outcomes. We will compare computational results against physically tested bone, and identify specific areas of the bone that may be strengthened to prevent reinjury through fracture. We hypothesize that our FE model will be able to spatially predict bone formation patterning, whole bone strength, and phenomenological reinjury behavior based on input parameters linked to initial morphology and regeneration processes. The proposed project will improve the rigor of the digit regeneration model in the mouse and improve our understanding of limb regeneration treatment outcomes and methods.