A harmonized vendor-agnostic environment for multi-site functional MRI studies

用于多站点功能 MRI 研究的与供应商无关的协调环境

• 批准号: 10306940
• 负责人: Jon-Fredrik Nielsen
• 金额: $ 55.58万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10306940
• 关键词: 3-Dimensional; Active Sites; Adoption; Algorithms; Back; Brain; Brain region; Clinical; Clinical Research; Cognition; Communities; Complex; Computer software; Data; Data Pooling; Development; Documentation; Education; Ensure; Environment; Fatty acid glycerol esters; Functional Magnetic Resonance Imaging; Future; Human; Image; Improve Access; Institution; Knowledge; Libraries; Magnetic Resonance Imaging; Manufacturer Name; Maps; Measurement; Methods; Modeling; Motor; Multicenter Studies; Neurosciences Research; Performance; Physiologic pulse; Procedures; Protocols documentation; Pythons; Quality Control; Reproducibility; Research; Research Personnel; Resources; Rest; Scanning; Signal Transduction; Site; Structure; Techniques; Technology; Training; Travel; Variant; Vendor; base; cognitive task; computerized data processing; data acquisition; data quality; design; experimental study; file format; flexibility; image reconstruction; imaging study; interest; invention; magnetic field; open source; portability; quality assurance

Since its invention in the early 90s, functional magnetic resonance imaging (fMRI) has revolutionized our un- derstanding of the human brain. Functional MRI may be used to observe brain function during a specific motor or cognitive task, or at “rest” (resting-state fMRI). The latter produces so-called “functional connectivity” maps that may provide a new window into human cognition. There is currently a large, world-wide effort underway to discover potential research and clinical uses of such connectivity maps. A significant practical barrier in this effort, however, is the difficulty in ensuring that fMRI experiments are conducted in a consistent and reproducible manner across different centers. In particular, it is generally not possible to ensure identical execution of MR measurements (pulse sequences) across sites operating different MR scanners. Furthermore, even the image reconstruction and data processing methods can be difficult to harmonize, particularly across different MR ven- dors. This makes it challenging to directly compare results between sites, or “pool” data from multiple sites to increase statistical power and gain access to rare clinical conditions. We will assemble and disseminate a truly harmonized, cross-vendor, and flexible environment for fMRI re- search that ensures consistent data acquisition and image reconstruction across sites. Our framework is based on an open-source MR sequence development platform that allows any arbitrary MR pulse sequence to be de- signed “off-line” in Matlab or Python and exported to a vendor-independent file format, that can be ported directly to scanners from different manufacturers (at present, General Electric and Siemens are supported, but others may follow in the future). Due to this open pulse sequence structure it will also be possible to compose a unified image reconstruction environment based on current open-source libraries. Based on this technology, we will provide the fMRI research community with a complete and portable workflow for fMRI data acquisition and image reconstruction, backed up by integrated quality control procedures.

自90年代初发明以来，功能性磁共振成像（fMRI）已经彻底改变了我们的非... 了解人类大脑。功能性MRI可用于观察特定运动过程中的大脑功能。 或认知任务，或在“休息”（静息状态功能磁共振成像）。后者产生所谓的“功能连接”图 这可能会为人类认知提供一个新的窗口。目前世界范围内正在进行一项大规模的努力， 以发现这种连接图的潜在研究和临床用途。这是一个重大的实际障碍， 然而，努力是确保fMRI实验以一致和可重复的方式进行的困难。 不同中心的方式。特别地，通常不可能确保MR的相同执行 操作不同MR扫描仪的站点之间的测量（脉冲序列）。此外，即使图像 重建和数据处理方法可能难以协调，特别是在不同的MR静脉之间， 多尔。这使得直接比较研究中心之间的结果或从多个研究中心“汇集”数据以 增加统计功效并获得对罕见临床病症的访问。 我们将组装和传播一个真正和谐的，跨供应商，和灵活的环境，功能磁共振成像重新， 搜索，确保一致的数据采集和图像重建跨网站。我们的框架基于 在一个开源的MR序列开发平台上，允许任何任意的MR脉冲序列， 在Matlab或Python中进行“离线”签名，并导出为可直接移植的独立于供应商的文件格式 到不同制造商的扫描仪（目前，通用电气和西门子支持，但其他 未来可能会有）。由于这种开放脉冲序列结构，还可以组成统一的艾德 基于当前开源库的图像重建环境。基于这项技术，我们将 为fMRI研究社区提供了一个完整的和便携式的工作流程，用于fMRI数据采集和成像 重建，由综合质量控制程序支持。