Computational Biomechanics for Prediction of Osteoporotic Vertebral Fracture Risk

描述（由申请人提供）：申请人最初的培训是工程，计算机科学和物理，具有多孔介质计算建模的背景。他计划将自己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。通过定量MRI和生物力学的指导研究/培训项目，短期目标是完成申请人作为骨质疏松性骨折独立研究人员的准备工作，并通过多学科合作进一步提高他的能力，为生物医学研究建立新的方向。为了实现这些目标，本提案中概述的指导研究将辅以正式的培训计划，包括教学课程；参加会议、研讨会和讲习班；并与其他研究者、合作者和受训者进行定期互动。申请人将受到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（X. Edward Guo教授，博士）。拟议的研究包括开发一个生物力学框架，用于早期预测椎体骨折，这是影响老年人骨质疏松症最常见的结果之一。早期发现椎体畸形是很重要的，因为已知有这种畸形的患者发生进一步骨折的风险较高。脊柱侧位放射投影，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。假设脊柱的机械能力受损与远端骨骼部位的相似变化和椎体骨折状态可以通过远端肢体的活体高分辨率磁共振（mMR）图像进行生物力学分析来预测，这一假设将得到验证。本研究对98例骨质疏松症患者进行了桡骨远端、胫骨远端和脊柱中线矢状位磁共振成像。具体目标包括：1)验证与骨力学特性相关的参数，这些参数来自基于mmri的人体胫骨尸体有限元建模；2)扩展先前开发的基于mmri的fe技术的能力，用于分析患者桡骨远端和胫骨远端mMR图像；3)通过探索脊柱畸形与远端骨质量相关的mri衍生参数之间的假设关联，开发预测椎体骨折状态的框架。拟议的研究有可能为椎体骨折的早期预测提供一种改进的技术，并将为申请人成为独立研究人员提供良好的跳板。

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.