Computational Biomechanics for Prediction of Osteoporotic Vertebral Fracture Risk

描述（由申请人提供）：申请人的原始培训是在工程，计算机科学和物理与多孔介质的计算建模的背景。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相关研究相结合。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。通过这个指导研究/培训定量MRI和生物力学的协调计划的短期目标是完成申请人作为一个独立的研究人员的准备，并进一步他的能力，通过多学科合作建立生物医学研究的新方向。为了实现这些目标，本提案中概述的指导研究将得到正式培训计划的补充，该计划涉及教学课程;参加会议，研讨会和讲习班;以及与其他研究人员，合作者和学员的定期互动。申请人将由世界著名的科学家指导骨骼成像的MR技术（Felix W。Wehrli，Ph.D.），代谢性骨疾病的内分泌学方面（Peter J. Snyder教授，医学博士），和基于图像的生物力学建模和力学测试（教授X。Edward Guo，Ph.D.）。拟议的研究涉及开发一个生物力学框架，用于早期预测椎骨骨折，这是影响老年人骨质疏松症的最常见结果之一。脊椎畸形的早期发现是很重要的，因为已知有这种畸形的患者发生进一步骨折的风险较高。脊柱的侧位X线片投影，一种有许多局限性的方法，仍然被用作诊断这些畸形的临床标准。将对脊柱机械能力受损与远端骨骼部位的类似变化平行的假设进行检验，并通过远端肢体的体内高分辨率磁共振（mMR）图像，基于生物力学分析预测椎骨骨折状态。本研究利用98例骨质疏松症患者桡骨远端和胫骨远端的mMR图像以及脊柱正中矢状面MR图像。具体目标包括1）验证与使用尸体胫骨的基于mMRI的有限元建模得出的骨骼力学性能相关的参数，2）扩展之前开发的基于mMRI的FE技术的功能，以分析患者桡骨远端和胫骨远端的mMR图像，和3）通过探索脊柱畸形和MRI之间的假设联系，建立一个预测椎体骨折状态的框架，导出的参数与远端四肢的骨质有关。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，并将作为申请人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.