Computational Biomechanics for Prediction of Osteoporotic Vertebral Fracture Risk

描述(由申请人提供)：申请者最初的培训是工程、计算机科学和物理，具有多孔介质计算建模的背景。他计划将他在定量分析方面的优势和目前在有限元分析方面的博士后经验与NIH的相关研究结合起来。他的长期职业目标是通过非侵入性成像和多学科方法促进对疾病的理解、诊断和管理。通过这一定量核磁共振和生物力学指导研究/培训的协调计划，短期目标是完成申请者作为骨质疏松性骨折独立研究人员的准备，并通过多学科合作进一步提高他建立生物医学研究新方向的能力。为了实现这些目标，本提案中概述的指导性研究将得到正式培训计划的补充，包括教学课程；参加会议、研讨会和研讨会；以及与其他研究人员、合作者和受训人员的定期互动。申请者将接受世界知名科学家的指导，包括骨成像磁共振技术(Felix W.Wehrli教授，博士)、代谢性骨疾病的内分泌学方面(Peter J.Snyder教授，医学博士)以及基于图像的生物力学建模和机械测试(X.Edward Guo教授，博士)。这项拟议的研究涉及开发一种生物力学框架，用于早期预测脊椎骨折，这是影响老年人的骨质疏松症最常见的后果之一。脊椎畸形的早期检测很重要，因为众所周知，患有这种畸形的患者进一步骨折的风险更高。脊柱侧位X线片是一种有许多局限性的方法，但仍被用作诊断这些畸形的临床标准。假设脊柱的机械能力受损与远端骨骼部位的变化相似，并且可以通过在体远端肢体的高分辨率磁共振(MMR)图像的生物力学分析来预测脊椎骨折状态，这一假设将得到验证。这项研究利用了98名骨质疏松患者的桡骨远端和胫骨远端的MMR图像和正中矢状位的MR图像。具体目标包括1)验证从身体胫骨基于mMRI的有限元建模获得的与骨骼力学特性相关的参数，2)扩展先前开发的基于mMRI的有限元技术的功能，以分析患者的桡骨远端和胫骨远端的MMR图像，以及3)开发一个框架，通过探索脊柱变形与MRI得出的与远端肢体骨质量相关的参数之间的假设关联来预测脊柱骨折状态。这项拟议的研究有可能为脊椎骨折的早期预测提供一种改进的技术，并将很好地作为申请者成为独立研究人员的发射台。

DESCRIPTION (provided by applicant): The applicant's original training is in engineering, computer science, and physics with a background in computational modeling of porous media. He plans to integrate his strengths in quantitative analysis and current postdoctoral experience in finite-element analysis with NIH-relevant research. His long-term career goal is to advance the understanding, diagnosis, and management of diseases through non-invasive imaging and multi-disciplinary approaches. The short-term objective through this coordinated program of mentored research/training in quantitative MRI and biomechanics is to complete the preparation of the applicant as an independent researcher of osteoporotic fractures and further his capacity to establish new directions for biomedical research through multi-disciplinary collaborations. To achieve these goals, the mentored research outlined in this proposal will be complemented by a formal training program involving didactic coursework; participation at conferences, seminars, and workshops; and regular interactions with other investigators, collaborators, and trainees. The applicant will be mentored by world renowned scientists on MR techniques for bone imaging (Prof. Felix W. Wehrli, Ph.D.), endocrinological aspects of metabolic bone diseases (Prof. Peter J. Snyder, M.D.), and image-based biomechanical modeling and mechanical testing (Prof. X. Edward Guo, Ph.D.). The proposed research involves the development of a biomechanical framework for early prediction of vertebral fractures, which are among the most common outcomes of osteoporosis affecting the elderly. The early detection of vertebral deformities is important because patients with such deformities are known to be at elevated risk for further fractures. Lateral radiographic projections of the spine, an approach which has many limitations, are still used as the clinical standard for diagnosis of these deformities. The hypothesis that the impaired mechanical competence at the spine parallels similar changes at distal skeletal sites and the vertebral fracture status can be predicted on the basis of biomechanical analysis via in-vivo high-resolution magnetic resonance (mMR) images of distal extremities will be tested. The proposed study makes use of mMR images of distal radius and distal tibia and midline-sagittal spine MR images in ninety eight patients with osteoporosis. The specific objectives involve 1) validation of parameters relating to bone's mechanical properties derived from mMRI-based finite-element modeling using cadaveric human tibia, 2) expansion of capabilities of the mMRI-based FE-technology developed previously to analyze mMR images of distal radius and distal tibia in patients, and 3) development of a framework to predict vertebral fracture status by exploring the hypothesized association between spinal deformity and MRI-derived parameters relating to bone quality at distal extremities. The proposed research holds the potential to provide an improved technique for early prediction of vertebral fractures and will serve well as a launching pad for the applicant to become an independent researcher.