Multidimensional MRI-based Big Data Analytics to Study Osteoarthritis

摘要 该项目概述了技术医学图像处理和机器学习的发展，以研究 骨关节炎的发病机制和自然病史。在过去的几年里，公共数据集的可用性 通过收集平片、核磁共振基因组学和患者报告的结果等数据， 对疾病病因、潜在治疗途径和长期结果预测因素的研究表明， 核磁共振的作用日益重要。此外，定量磁共振成像和医学图像的最新进展 处理允许提取非常丰富的异类信息阵列 肌肉骨骼系统，包括软骨和骨骼形态、骨骼形状特征、生物力学和 软骨生化成分。 骨关节炎是一种多基因、多因素的疾病，以几种表型为特征， 似乎是多维分析和精确医学的完美候选者。然而，要做到这一点 雄心勃勃的任务将需要复杂的分析和来自不同评估的多因素数据集成 具有形态、生化和生物力学特征。在这个项目中，我们建议填补这一空白 开发自动后处理算法来检测软骨的生化成分和 形态特征及多维机器学习在骨性关节炎研究中的应用 这项“独立之路”奖项申请包括一项有指导的职业发展计划，以 将候选人瓦伦蒂娜·佩多亚博士转变为独立调查员职位，以及 随附的研究计划描述了大数据应用方面的拟议技术发展 对办公自动化研究的分析。主要导师莎米拉·马琼达尔博士是该领域的领先专家 以及共同导师Adam Ferguson博士和Ramakrishna Akella博士， 在大数据的机器学习和拓扑数据分析应用方面有丰富的经验。这个 多样化的培训计划和这些导师的互补背景将使应聘者能够 在肌肉骨骼成像领域开发独特的跨学科配置文件。 候选人瓦伦蒂娜·佩多亚博士目前担任博士后职位(美联社 专家)在加州大学旧金山分校(UCSF)工作，开发MR图像后处理 算法。指导和职业发展计划将补充她的图像处理背景 对机器学习、大数据分析、流行病学研究设计和 跨学科协作，帮助她过渡到独立的医学成像和数据科学家 调查员位置。最终，她的目标是成为放射学或生物工程专业的教员。 在那里，她可以进一步研究生物医学成像和机器学习的技术发展 应用于肌肉骨骼系统。

ABSTRACT This project outlines technical medical image processing and machine learning developments to study the pathogenesis and natural history of osteoarthritis (OA). In the past few years, the availability of public datasets that collect data such as plain radiographs, MRI genomics and patients reported outcomes has allowed the study of disease etiology, potential treatment pathways and predictors of long-range outcomes, showing an increasingly important role of the MRI. Moreover, recent advances in quantitative MRI and medical image processing allow for the extraction of extraordinarily rich arrays of heterogeneous information on the musculoskeletal system, including cartilage and bone morphology, bone shape features, biomechanics, and cartilage biochemical composition. Osteoarthritis, being a polygenic and multifactorial disease characterized by several phenotypes, seems the perfect candidate for multidimensional analysis and precision medicine. However, accomplish this ambitious task, will require complex analytics and multifactorial data-integration from diverse assessments spanning morphological, biochemical, and biomechanical features. In this project, we propose to fill this gap developing automatic post-processing algorithms to examine cartilage biochemical compositional and morphological features and to apply new multidimensional machine learning to study OA This “Pathway to Independence” award application includes a mentored career development plan to transition the candidate, Dr. Valentina Pedoia, into an independent investigator position, as well as an accompanying research plan describing the proposed technical developments for the application of big data analytics to the study of OA. The primary mentor, Dr. Sharmila Majumdar, is a leading expert in the field of quantitative MRI for the study of OA, and the co-mentors, Dr. Adam Ferguson and Dr. Ramakrishna Akella, have extensive experience in the application of machine learning and topological data analysis to big data. The diversified plan of training and the complementary background of these mentors will allow the candidate to develop a unique interdisciplinary profile in the field of musculoskeletal imaging. The candidate, Dr. Valentina Pedoia, is currently in a post-doctoral level position (Associated Specialist) at the University of California at San Francisco (UCSF), developing MR image post-processing algorithms. The mentoring and career development plan will supplement her image processing background with valuable exposure to machine learning, big data analysis, epidemiological study design, and interdisciplinary collaboration to facilitate her transition to a medical imaging and data scientist independent investigator position. Ultimately, she aims to become a faculty member in a radiology or bioengineering institute, where she can further research technical biomedical imaging and machine learning developments applied to the musculoskeletal system.