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

基本信息

批准号：
10268200
负责人：
Sharmila Majumdar
金额：
$ 121.7万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
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/10268200
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 Diagnostic radiologic examination 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 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 clinical imaging clinical infrastructure clinical translation clinically relevant cohort cost estimate data space deep learning 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 reconstruction research and development research facility response scoliosis synaptic function task analysis technology research and development tool tool development

项目摘要

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.

项目概要/摘要脊柱疾病对社会产生巨大影响，无论是在身体上还是在患病率上。尽管意义重大，但在个人和经济上，生产力下降和医疗保健费用增加。对于这个问题，许多患者症状的病因多种多样且不清楚，而且很少有可靠的前瞻性地确定适当的患者护理过程并客观评估的方法导致这一重大医疗保健困境的挑战包括：背痛的来源众多，使用任何单一成像难以可视化相关组织疼痛定位和分子过程的技术和困难。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) 已用于研究腰痛受试者的骨转换和小关节疾病。 UH2/UH3 中提出的研究和工具开发在临床上迈出了关键的下一步对腰痛患者进行更快的定量磁共振成像 (MR) 翻译新。需要优化技术和患者研究来定量研究其临床潜力表征与腰痛有关的组织，并对疼痛进行客观评估。 NIH 背痛联盟 (BACPAC) 的多学科技术研究站点（技术站点）将开发第四阶段 TTM（技术优化研究与开发）以利用两个关键技术进步 – 开发基于机器学习的更快 MR 采集方法，以及用于图像分割和从图像中提取客观疾病相关特征的机器学习。将开发、验证和部署用于加速图像的端到端深度学习技术（TTM）重建、组织分割、脊柱退变检测，以促进自动化、鲁棒性评估脊柱特征、神经认知疼痛反应之间的结构-功能关系，为了完成这个重要的项目，我们组建了一支经验丰富的团队。多学科研究团队广泛结合 MR 生物工程专业知识、先进 MRI 数据分析、放射学、神经科学、神经外科、骨科手术、多维分析与工业界现有的研究协议。研究设施和环境包括临床和环境。该团队拥有成功完成拟议转化项目所需的研究基础设施。之前向学术界传播了工具，与工业界密切合作，并有动力与 BACPAC 随着联盟计划的发展而变化。