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

自90年代初发明以来，功能磁共振成像(FMRI)已经彻底改变了我们的思维方式。 对人脑的理解。功能核磁共振可以用来观察特定fic运动时的脑功能。 或认知任务，或处于“休息”状态(静息态功能磁共振成像)。后者生成所谓的“功能连通性”地图 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 以发现这种连接地图的潜在研究和临床用途。在这一点上，一个明显的fi无法实现的障碍 然而，努力是在确保功能核磁共振实验在一致和可重复性的情况下进行的。 横跨不同中心的方式。具体地说，通常不可能确保执行相同的MR 操作不同MR扫描仪的站点之间的测量(脉冲序列)。而且，就连这张照片 重建和数据处理方法可能很难协调，特别是在不同的MR-Win-fi之间。 多斯。这使得直接比较站点之间的结果或将来自多个站点的数据“汇集”到 增加统计能力，并获得罕见的临床情况。 我们将组装和传播一个真正协调的、跨供应商的和fl可伸缩的环境，用于功能磁共振再- 确保跨站点一致的数据采集和图像重建的搜索。我们的框架基于 在允许对任意MR脉冲序列进行解压缩的开源MR序列开发平台上 使用MatLab或PYTHON进行离线签名，并导出为独立于供应商的fiLE格式，可直接移植 不同制造商的扫描仪(目前支持通用电气和西门子，但其他 可能会在未来跟进)。由于这种开放脉冲序列结构，还可以构成单一fi边缘 基于当前开源库的图像重建环境。基于这项技术，我们将 为功能磁共振研究社区提供完整和便携的工作flow，用于功能磁共振数据采集和图像 重建，以综合质量控制程序为后盾。

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.