Technology Research Site for Advanced, Faster Quantitative Imaging for BACPAC

BACPAC 先进、更快的定量成像技术研究网站

• 批准号: 10683487
• 负责人: Sharmila Majumdar
• 金额: $ 15.58万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-26 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10683487
• 关键词: Academia; Activities of Daily Living; Adult; Aftercare; Agreement; Anatomy; Back Pain; Base of the Brain; Biomedical Engineering; Brain; Cartilage; Characteristics; Chronic; Chronic low back pain; Clinical; Clinical Management; Clinical Research; Complement; Complex; Corpus striatum structure; Data Analyses; Data Analytics; Detection; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Environment; Etiology; Evaluation; Exercise Therapy; Functional Imaging; Functional Magnetic Resonance Imaging; Functional disorder; Gold; Health Care Costs; Healthcare; Image; Image Analysis; Imaging Techniques; Individual; Industry; Interdisciplinary Study; Intervention; Intervertebral disc structure; Knee; Knee Osteoarthritis; Laboratories; Lesion; Link; Low Back Pain; Machine Learning; Magnetic Resonance Imaging; Measures; Medial; Meniscus structure of joint; Methods; Modeling; Molecular; Morbidity - disease rate; Morphology; Muscle; Musculoskeletal; Musculoskeletal Pain; Nerve; Nerve Block; Neuraxis; Neurocognitive; Neurosciences; Orthopedic Surgery; Outcome; Outcome Measure; Pain; Pain management; Patient Care; Patient Outcomes Assessments; Patient imaging; Patients; Pattern; Performance; Persons; Phase; Play; Positron-Emission Tomography; Process; Productivity; Protocols documentation; Radiology Specialty; Research; Research Infrastructure; Research Personnel; Rest; Role; Sampling; Severities; Site; Societies; Source; Spinal; Spinal Diseases; Staging; Stenosis; Structure; Structure-Activity Relationship; Symptoms; Techniques; Technology; Time; Tissues; Translations; United States National Institutes of Health; Variant; Vendor; Vertebral column; Visualization; Work; autoencoder; base; bone turnover; brain magnetic resonance imaging; clinical imaging; clinical infrastructure; clinical translation; clinically relevant; cohort; cost estimate; data space; deep learning; deep learning model; disability; domain mapping; effectiveness evaluation; experience; feature detection; gray matter; hands-on learning; human subject; image reconstruction; imaging Segmentation; improved; intervertebral disk degeneration; multi-task learning; multidisciplinary; neurosurgery; novel; prevent; programs; prospective; quantitative imaging; radiological imaging; reconstruction; research and development; research facility; response; scoliosis; synaptic function; task analysis; technology research and development; tool; tool development

PROJECT SUMMARY/ABSTRACT Disorders of the spine have a tremendous impact on society; both physically through the morbidity of afflicted individuals, and financially, through lost productivity and increased health care costs. Despite the significance of this problem, the etiology of symptoms is diverse and unclear in many patients, and there are few reliable methods by which to prospectively determine the appropriate course of patient care and to objectively evaluate the effectiveness of various interventions. Challenges contributing to this major healthcare dilemma include numerous sources of back pain, difficulty in visualization of responsible tissues using any single imaging technique and difficulty in the localization of pain and contributing molecular processes. Magnetic Resonance imaging (MR) has been used to characterize disc, muscle, nerves and Positron Emission Tomography (PET) has been used to study bone turnover, and facet disease in subjects with lower back pain. The research and tool development proposed in this UH2/UH3 takes the critical next step in the clinical translation of faster, quantitative magnetic resonance imaging (MR) of patients with lower back pain. New optimized techniques and patient studies are required to investigate its clinical potential for quantitatively characterizing the tissues implicated in lower back pain, and objective evaluation of pain. Our proposed multidisciplinary Technology Research Site (Tech Site) of the NIH Back Pain Consortium (BACPAC) will develop Phase IV TTMs (Research and Development for Technology Optimization) to leverage two key technical advancements – development of machine learning based faster MR acquisition methods, and machine learning for image segmentation and extraction of objective disease related features from images. We will develop, validate, and deploy end-to-end deep learning-based technologies (TTMs) for accelerated image reconstruction, tissue segmentation, detection of spinal degeneration, to facilitate automated, robust assessment of structure-function relationships between spine characteristics, neurocognitive pain response, and patient reported outcomes. To accomplish this important project, we have assembled a highly-experienced multidisciplinary research team combining extensive expertise MR bioengineering, advanced MRI data analysis, radiology, neuroscience, neurosurgery, orthopedic surgery, multi-dimensional analytics and have existing research agreements with industry. The research facilities and environment include the clinical and research infrastructure required for successful completion of the proposed translational project. The team has disseminated tools before to academia, worked closely with industry and are motivated to totally work with BACPAC as the plans of the consortium evolve.

项目摘要/摘要 脊柱的疾病对社会产生了巨大影响；两者都是通过折磨的发病率 个人以及财务上的生产力损失并增加医疗保健成本。尽管很重要 在这个问题中，症状的病因在许多患者中存在分歧和不清楚，而且很少有可靠的 通过前瞻性确定患者护理过程并客观评估的方法 各种干预措施的有效性。促成这一重大医疗保健困境的挑战包括 许多背痛的来源，在使用任何单一成像的负责时间可视化负责任的时间方面很难 技术和难度在疼痛定位和贡献分子过程中。磁共振 成像（MR）已用于表征椎间盘，肌肉，神经和正电子发射断层扫描（PET） 已用于研究骨骼更新和面部疼痛受试者的小方疾病。 在本uh2/uh3中提出的研究和工具开发采取了临床的关键下一步 下背痛患者的更快，定量磁共振成像（MR）的翻译。新的 需要优化的技术和患者研究来研究其定量的临床潜力 表征下背部疼痛中实现的组织以及对疼痛的客观评估。我们提出的 NIH背痛财团（BACPAC）的多学科技术研究地点（技术站点）将 开发IV阶段TTM（技术优化的研究和开发）来利用两个关键 技术进步 - 基于机器学习的开发更快的MR采集方法，以及 图像分割和客观疾病与图像中相关特征提取的机器学习。我们 将开发，验证和部署端到端的基于深度学习的技术（TTM），以加速图像 重建，组织分割，检测脊柱变性，以促进自动化，稳健 评估脊柱特征，神经认知疼痛反应之间的结构功能关系， 患者报告了结果。为了完成这个重要的项目，我们组装了一个高度经验的 多学科研究团队结合了广泛的专家生物工程先生，高级MRI数据 分析，放射学，神经科学，神经外科，骨科手术，多维分析，具有 与行业的现有研究协议。研究设施和环境包括临床和 成功完成拟议的翻译项目需要研究基础设施。团队有 在学术界之前传播工具，与行业紧密合作，并有动力与 BACPAC作为财团进化的计划。