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: 10683143
• 负责人: Sharmila Majumdar
• 金额: $ 121.01万
• 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/10683143
• 关键词: Academia; Acceleration; Activities of Daily Living; Adult; Advanced Development; Aftercare; Agreement; Anatomy; Back Pain; Biomedical Engineering; Brain; Cartilage; Central Nervous System; Characteristics; Chronic; Chronic low back pain; Clinical; Clinical Management; Complement; Complex; Corpus striatum structure; Data Analyses; Data Analytics; Detection; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Dimensions; Disease; Environment; Etiology; Evaluation; Exercise Therapy; Functional Imaging; Functional Magnetic Resonance Imaging; Functional disorder; 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; Maps; Measures; Medial; Meniscus structure of joint; Methods; Modeling; Molecular; Morbidity - disease rate; Morphology; Muscle; Musculoskeletal; Musculoskeletal Pain; Nerve; Nerve Block; 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; Severities; Site; Societies; Source; Spinal; Spinal Diseases; Spinal nerve structure; Staging; Stenosis; Structure; Structure-Activity Relationship; Symptoms; Techniques; Technology; Time; Tissues; Translations; United States National Institutes of Health; Vendor; Vertebral column; Visualization; Work; autoencoder; bone turnover; brain based; 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; structural imaging; synaptic function; 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 脊柱疾病对社会有着巨大的影响;无论是身体上通过患病的发病率 个人和经济上，通过生产力的损失和医疗保健费用的增加。尽管意义重大 在这一问题中，许多患者的症状病因多样且不明确， 前瞻性地确定患者护理的适当过程并客观地评估 各种干预措施的有效性。造成这一重大医疗保健困境的挑战包括 背部疼痛的多种来源，使用任何单一成像难以可视化责任组织 技术和困难的定位疼痛和贡献的分子过程。磁共振 磁共振成像（MR）已被用于表征椎间盘、肌肉、神经和正电子发射断层扫描（PET） 已被用于研究骨转换和下背痛患者的小关节疾病。 UH 2/UH 3中提出的研究和工具开发在临床上迈出了关键的下一步。 翻译更快，定量磁共振成像（MR）的患者腰痛。新 需要优化的技术和患者研究来研究其临床潜力， 表征与下背痛有关的组织，以及对疼痛的客观评价。我们提出的 NIH背痛联盟（BACPAC）的多学科技术研究中心（Tech Site）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-基于机器学习的更快MR采集方法的开发，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。我们 将开发、验证和部署端到端基于深度学习的技术（TTM），以加速映像 重建，组织分割，检测脊柱退变，以便于自动化，鲁棒性 评估脊柱特征，神经认知疼痛反应， 和患者报告的结果。为了完成这一重要项目，我们组建了一个经验丰富的 多学科研究团队结合了丰富的专业知识MR生物工程，先进的MRI数据 分析，放射学，神经科学，神经外科，整形外科，多维分析， 与工业界的现有研究协议。研究设施和环境包括临床和 成功完成拟议的翻译项目所需的研究基础设施。该团队已经 在向学术界传播工具之前，与工业界密切合作，并有动力完全与 随着财团计划的发展，BACPAC。

项目成果

Automatic detection and voxel-wise mapping of lumbar spine Modic changes with deep learning.

• DOI: 10.1002/jsp2.1204
• 发表时间: 2022-06
• 期刊: JOR SPINE
• 影响因子: 3.7
• 作者: Gao, Kenneth T.;Tibrewala, Radhika;Hess, Madeline;Bharadwaj, Upasana U.;Inamdar, Gaurav;Link, Thomas M.;Chin, Cynthia T.;Pedoia, Valentina;Majumdar, Sharmila
• 通讯作者: Majumdar, Sharmila

The Back Pain Consortium (BACPAC) Research Program: Structure, Research Priorities, and Methods.

• DOI: 10.1093/pm/pnac202
• 发表时间: 2023-08-04
• 期刊: PAIN MEDICINE
• 影响因子: 3.1
• 作者: Mauck, Matthew C.;Lotz, Jeffrey;Psioda, Matthew A.;Carey, Timothy S.;Clauw, Daniel J.;Majumdar, Sharmila;Marras, William S.;Vo, Nam;Aylward, Ayleen;Hoffmeyer, Anna;Zheng, Patricia;Ivanova, Anastasia;McCumber, Micah;Carson, Christiane;Anstrom, Kevin J.;Bowden, Anton E.;Dalton, Diane;Derr, Leslie;Dufour, Jonathan;Fields, Aaron J.;Fritz, Julie;Hassett, Afton L.;Harte, Steven E.;Hue, Trisha F.;Krug, Roland;Loggia, Marco L.;Mageswaran, Prasath;McLean, Samuel A.;Mitchell, Ulrike H.;O'Neill, Conor;Pedoia, Valentina;Quirk, David Adam;Rhon, Daniel, I;Rieke, Viola;Shah, Lubdha;Sowa, Gwendolyn;Spiegel, Brennan;Wasan, Ajay D.;Wey, Hsiao-Ying (Monica);LaVange, Lisa
• 通讯作者: LaVange, Lisa