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.

抽象的 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几年中，公共数据集的可用性。 收集平片、MRI 基因组学和患者报告结果等数据的技术使得 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 此外，定量 MRI 和医学图像的最新进展。 处理允许提取极其丰富的异构信息数组 肌肉骨骼系统，包括软骨和骨形态、骨形状特征、生物力学和 软骨生化成分。 骨关节炎是一种多基因、多因素疾病，具有多种表型特征， 似乎是多维分析和精准医学的完美候选者，但是，要实现这一目标。 雄心勃勃的任务，将需要来自不同评估的复杂分析和多因素数据集成 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生化成分和 形态特征并应用新的多维机器学习来研究 OA 该“独立之路”奖励申请包括一份指导性职业发展计划，以 将候选人 Valentina Pedoia 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的拟议技术发展 主要导师 Sharmila Majumdar 博士是 OA 领域的领先专家。 用于 OA 研究的 MRI，以及共同导师 Adam Ferguson 博士和 Ramakrishna Akella 博士的定量研究， 在机器学习和拓扑数据分析应用于大数据方面拥有丰富的经验。 多样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。 候选人瓦伦蒂娜·佩多亚 (Valentina Pedoia) 博士目前处于博士后级别职位（联合 加州大学旧金山分校 (UCSF) 的专家，开发 MR 图像后处理 指导和职业发展计划将补充她的图像处理背景。 具有宝贵的机器学习、大数据分析、流行病学研究设计经验， 跨学科合作促进她向独立的医学成像和数据科学家过渡 最终，她的目标是成为放射学或生物工程的教员。 研究所，她可以在那里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。