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

用于预测骨再生形态、图案和强度的计算模型

基本信息

批准号：
10727940
负责人：
Kevin Hoffseth
金额：
$ 13.69万
依托单位：
LOUISIANA STATE UNIV AGRICULTURAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-15 至 2025-08-14
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10727940
关键词：
3-Dimensional Acceleration Address Aftercare Agreement Algorithms Amputation Animal Model Architecture Area Behavior Biological Models Biomechanics Bone Injury Bone Regeneration Child Chromosome Mapping Complex Computer Models Data Data Set Defect Development Digit structure Elasticity Elements Emerging Technologies Ensure Evaluation Exhibits Failure Fracture Future Gene Expression Geometry Growth High Performance Computing Human Immature Bone Inferior Injury Investigation Limb structure Link Measurement Measures Mechanics Methods Modeling Modulus Morphology Mus Natural regeneration Osteogenesis Outcome Pattern Pattern Formation Performance Physiologic Ossification Process Property Prosthesis Quality of life Reproducibility Research Resolution Sampling Scanning Shapes Structure Testing Time Tissues Treatment outcome Validation biomechanical test bone bone strength comparative computerized tools computing resources density digit regeneration image processing improved in vivo infancy insight limb regeneration mechanical behavior mechanical properties model development nanoindentation novel phenomenological models predictive modeling prevent regeneration function regeneration model regenerative regenerative treatment repaired simulation spatiotemporal tissue regeneration tomography tool translational pipeline trend

项目摘要

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.

项目概要/摘要对肢体再生过程的研究正在快速增长。主要关注数量，但研究再生后的结构质量和功能严重缺乏组织结构来评估再生结果和治疗，特别是在再生骨的结构质量和功能方面，开发新工具以更好地改善再生骨的结构质量和功能。评估和理解复杂的肢体再生过程直接有助于提高能力利用新兴技术再生人体四肢，以改善婴儿和儿童的生活质量肢体缺陷和改善假肢性能对于小动物模型至关重要。开发针对受损骨骼的再生治疗方法以及供人类使用的转化管道。对于开发计算工具以改善再生骨的生物力学评估至关重要，并从空间上了解骨骼形成发生的地点和时间以及如何加强我们的数据。表明小鼠手指截肢模型提供了高度可重复的骨再生模型该模型中的截肢再生可以通过重复的体内高分辨率微计算进行跟踪。地形 (μCT) 随着时间的推移进行扫描，提供构建多尺度所需的独特有价值的数据此外，我们发现我们能够合并局部化的空间。在此基础上根据 µCT 体积密度数据计算出杨氏模量等机械性能。我们将开发一种 µCT 到 FE 计算方法，并集成随机增长算法我们将验证再生骨的时空模式和生长。使用 µCT 数据测量的实际再生结果来模拟生长，然后应用我们的整体预测和测试全骨手指力量和现象学再损伤结果的方法。将计算结果与物理测试的骨骼进行比较，并确定可能会影响骨骼的特定区域我们认为我们的有限元模型将能够在空间上进行强化，以防止因骨折而再次受伤。根据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将提高严格性。小鼠手指再生模型的研究，提高我们对肢体再生治疗的理解结果和方法。