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 20 世纪 90 年代初发明以来，功能性磁共振成像 (fMRI) 彻底改变了我们的生活。 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“休息”（休息状态功能磁共振成像），这会产生所谓的“功能连接”后者图。 这可能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。 发现此类连接图的潜在研究和临床用途是这方面的一个重大实际障碍。 然而，难以确保功能磁共振成像实验以一致且可重复的方式进行 特别是，通常不可能确保 MR 的执行相同。 跨站点操作不同 MR 扫描仪的测量（脉冲序列） 此外，甚至图像。 重建和数据处理方法可能很难协调，特别是在不同的 MR 方案中 这使得直接比较站点之间的结果或“汇集”多个站点的数据变得具有挑战性。 提高统计能力并获得罕见的临床状况。 我们将为功能磁共振成像重新构建和传播一个真正协调的、跨供应商的、灵活的环境。 确保跨站点数据采集和图像重建一致的搜索是我们的框架的基础。 在开源 MR 序列开发平台上，该平台允许任意任意 MR 脉冲序列被解调 在 Matlab 或 Python 中“离线”签名并导出为独立于供应商的文件格式，可以直接移植 不同厂家的扫描仪（目前支持通用电气和西门子，但也支持其他厂家的扫描仪） 由于这种开放脉冲序列结构，也将有可能组成一个统一的脉冲序列。 基于当前开源库的图像重建环境，我们将基于此技术。 为 fMRI 研究社区提供完整且可移植的 fMRI 数据采集和图像工作负载 重建，以综合质量控制程序为后盾。