Multidimensional MRI-based Big Data Analytics to Study Osteoarthritis

基于多维 MRI 的大数据分析研究骨关节炎

• 批准号: 9385849
• 负责人: Valentina Pedoia
• 金额: $ 9.4万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9385849
• 关键词: Algorithms; Atlases; Award; Big Data; Biochemical; Biochemistry; Biomechanics; Biomedical Engineering; California; Cartilage; Chronology; Clinical; Complex; Computer Vision Systems; Data; Data Analyses; Data Analytics; Data Science; Data Set; Degenerative polyarthritis; Development; Development Plans; Diagnostic radiologic examination; Dimensions; Discipline; Disease; Disease Progression; Early Diagnosis; Elements; Etiology; Event; Exposure to; Faculty; Gait; Genetic; Genomics; Goals; Health; Human; Hybrids; Image; Image Analysis; Imagery; Institutes; Knee; Knee Osteoarthritis; Knee joint; Knowledge; Learning; Lesion; Longitudinal Studies; Machine Learning; Magnetic Resonance Imaging; Measurement; Medical; Medical Imaging; Mentors; Modeling; Morphology; Musculoskeletal System; Natural History; Outcome; Pathogenesis; Pathway interactions; Patient Outcomes Assessments; Phase; Phenotype; Positioning Attribute; Postdoctoral Fellow; Radiology Specialty; Relaxation; Research; Research Design; Research Personnel; Risk Factors; Role; San Francisco; Scanning; Sex Characteristics; Shapes; Source; Specialist; Statistical Data Interpretation; Symptoms; Syndrome; Techniques; Thick; Time; Tissues; Training; Universities; Validation; Variant; arthropathies; base; bioimaging; bone; career development; cartilage degradation; connectome; data integration; design; epidemiology study; experience; image processing; image registration; imaging Segmentation; imaging biomarker; imaging scientist; interdisciplinary collaboration; kinematics; member; modifiable risk; morphometry; musculoskeletal imaging; parallel processing; precision medicine; quantitative imaging; racial difference; repository; shape analysis; soft tissue; three-dimensional modeling; tool

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.

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（OA）的发病机制和自然史。在过去的几年里，公共数据集的可用性 通过收集平片、MRI基因组学和患者报告的结果等数据， 研究疾病病因，潜在的治疗途径和长期结果的预测因素，显示了一个 MRI的作用越来越大。此外，定量MRI和医学图像的最新进展 处理允许提取有关的非常丰富的异构信息阵列 肌肉骨骼系统，包括软骨和骨形态，骨形状特征，生物力学， 软骨生化组成。 骨关节炎是一种多基因、多因素的疾病，具有多种表型， 似乎是多维分析和精准医疗的完美候选人。但是，要做到这一点 这是一项雄心勃勃的任务，需要从各种评估中进行复杂的分析和多因素数据集成 涵盖形态学、生物化学和生物力学特征。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查软骨的生化成分， 形态特征，并应用新的多维机器学习来研究OA 这个“独立之路”奖申请包括一个指导性的职业发展计划， 过渡候选人，博士瓦伦蒂娜Pedoia，到一个独立的调查员的位置，以及 随附的研究计划，说明大数据应用的拟议技术发展 分析到OA的研究。主要导师Sharmila Majumdar博士是该领域的领先专家， 定量磁共振成像用于OA的研究，以及共同导师，亚当·弗格森博士和拉玛克里希纳·阿凯拉博士， 在机器学习和拓扑数据分析应用于大数据方面有丰富的经验。的 多元化的培训计划和这些导师的互补背景将使候选人能够 在肌肉骨骼成像领域发展独特的跨学科概况。 候选人Valentina Pedoia博士目前担任博士后职位（联合 加州大学弗朗西斯科分校（UCSF）的专家），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 有价值的接触机器学习，大数据分析，流行病学研究设计， 跨学科合作，以促进她向独立的医学成像和数据科学家的过渡 调查员职位。最终，她的目标是成为放射学或生物工程学的教员 研究所，在那里她可以进一步研究技术生物医学成像和机器学习的发展 应用于肌肉骨骼系统。