Vogelbacher Christoph, Bopp Miriam H A, Schuster Verena, Herholz Peer, Jansen Andreas, Sommer Jens
Laboratory for Multimodal Neuroimaging, Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany.
Center for Mind, Brain and Behavior, Marburg, Germany.
Front Neurosci. 2019 Jul 3;13:688. doi: 10.3389/fnins.2019.00688. eCollection 2019.
Image characteristics of magnetic resonance imaging (MRI) data (e.g., signal-to-noise ratio, SNR) may change over the course of a study. To monitor these changes a quality assurance (QA) protocol is necessary. QA can be realized both by performing regular phantom measurements and by controlling the human MRI datasets (e.g., noise detection in structural or movement parameters in functional datasets). Several QA tools for the assessment of MRI data quality have been developed. Many of them are freely available. This allows in principle the flexible set-up of a QA protocol specifically adapted to the aims of one's own study. However, setup and maintenance of these tools takes substantial time, in particular since the installation and operation often require a fair amount of technical knowledge. In this article we present a light-weighted virtual machine, named , which provides scripts for fully automated QA analyses of phantom and human datasets. This virtual machine is ready for analysis by starting it the first time. With minimal configuration in the guided web-interface the first analysis can start within 10 min, while adapting to local phantoms and needs is easily possible. The usability and scope of is illustrated using a data set from the QA protocol of our lab. With we hope to provide an easy-to-use toolbox that is able to calculate QA statistics without high effort.
磁共振成像(MRI)数据的图像特征(如信噪比,SNR)可能会在研究过程中发生变化。为了监测这些变化,需要一个质量保证(QA)方案。QA既可以通过定期进行体模测量来实现,也可以通过控制人类MRI数据集(如在结构数据集中检测噪声或在功能数据集中检测运动参数)来实现。已经开发了几种用于评估MRI数据质量的QA工具。其中许多工具都是免费提供的。这原则上允许灵活设置专门适用于自身研究目标的QA方案。然而,这些工具的设置和维护需要大量时间,特别是因为安装和操作通常需要相当多的技术知识。在本文中,我们展示了一个名为 的轻量级虚拟机,它提供了用于对体模和人类数据集进行全自动QA分析的脚本。这个虚拟机首次启动时即可进行分析。在引导式网络界面中进行最少的配置后,首次分析可以在10分钟内开始,同时很容易适应本地体模和需求。我们使用来自实验室QA方案的数据集来说明 的可用性和范围。通过 ,我们希望提供一个易于使用的工具箱,能够轻松计算QA统计数据。