Quantifying, controlling, and monitoring image quality is an essential prerequisite for ensuring the validity and reproducibility of many types of neuroimaging data analyses. Implementation of quality control (QC) procedures is the key to ensuring that neuroimaging data are of high-quality and their validity in the subsequent analyses. We introduce the QC system of the Laboratory of Neuro Imaging (LONI): a web-based system featuring a workflow for the assessment of various modality and contrast brain imaging data. The design allows users to anonymously upload imaging data to the LONI-QC system. It then computes an exhaustive set of QC metrics which aids users to perform a standardized QC by generating a range of scalar and vector statistics. These procedures are performed in parallel using a large compute cluster. Finally, the system offers an automated QC procedure for structural MRI, which can flag each QC metric as being ‘good’ or ‘bad.’ Validation using various sets of data acquired from a single scanner and from multiple sites demonstrated the reproducibility of our QC metrics, and the sensitivity and specificity of the proposed Auto QC to ‘bad’ quality images in comparison to visual inspection. To the best of our knowledge, LONI-QC is the first online QC system that uniquely supports the variety of functionality where we compute numerous QC metrics and perform visual/automated image QC of multi-contrast and multi-modal brain imaging data. The LONI-QC system has been used to assess the quality of large neuroimaging datasets acquired as part of various multi-site studies such as the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). LONI-QC’s functionality is freely available to users worldwide and its adoption by imaging researchers is likely to contribute substantially to upholding high standards of brain image data quality and to implementing these standards across the neuroimaging community.

中文翻译：

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LONI QC 系统：一个半自动化、基于网络且免费提供的环境，用于神经影像数据的全面质量控制

量化、控制和监测图像质量是确保多种类型神经影像数据分析的有效性和可重复性的重要先决条件。质量控制（QC）程序的实施是确保神经影像数据高质量及其在后续分析中的有效性的关键。我们介绍神经影像实验室 (LONI) 的 QC 系统：一个基于网络的系统，具有用于评估各种模态和对比脑成像数据的工作流程。该设计允许用户匿名上传成像数据到 LONI-QC 系统。然后，它计算一组详尽的 QC 指标，通过生成一系列标量和矢量统计数据来帮助用户执行标准化 QC。这些过程是使用大型计算集群并行执行的。最后，该系统提供了结构 MRI 的自动化 QC 程序，可以将每个 QC 指标标记为“好”或“坏”。使用从单个扫描仪和多个站点获取的各种数据集进行的验证证明了我们的质量控制指标的可重复性，以及与目视检查相比，拟议的自动质量控制对“不良”质量图像的敏感性和特异性。据我们所知，LONI-QC 是第一个在线 QC 系统，它独特地支持各种功能，我们可以在其中计算大量 QC 指标并对多对比度和多模态脑成像数据执行视觉/自动图像 QC。LONI-QC 系统已用于评估作为各种多站点研究的一部分获得的大型神经影像数据集的质量，例如创伤性脑损伤的研究和临床知识转化 (TRACK-TBI) 研究和阿尔茨海默病神经影像计划 (阿德尼）。LONI-QC 的功能免费提供给全世界的用户，成像研究人员采用它可能会对维护脑图像数据质量的高标准以及在整个神经成像界实施这些标准做出重大贡献。