MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites
MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites
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DOI：
10.1371/journal.pone.0184661
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发表时间：
2017-09-25
期刊：
影响因子：
3.7
通讯作者：
Gorgolewski, Krzysztof J.
中科院分区：
文献类型：
--
作者：
Esteban, Oscar;Birman, Daniel;Gorgolewski, Krzysztof J.
MRI的质量控制对于排除有问题的采集和避免后续图像处理和分析中的偏差至关重要。对于大规模数据集，视觉检查是主观的和不切实际的。虽然自动化质量评估已经在单站点数据集上得到了证明，但目前还不清楚解决方案是否可以推广到在新站点获取的看不见的数据。在这里，我们介绍了MRI质量控制工具（MRIQC），用于提取质量指标和拟合二元（接受/排除）分类器的工具。我们的工具可以在本地运行，也可以通过OpenNeuro作为免费在线服务运行。org portal.该分类器是在公开可用的多站点数据集（17个站点，N = 1102）上训练的。我们进行模型选择，评估不同的归一化和特征排除方法，旨在最大限度地提高跨站点的泛化能力，并使用leave-one-site-out交叉验证，估计新站点的准确率为76%-13%。我们确认了保留数据集（2个研究中心，N = 265）的结果，也获得了76%的准确度。即使经过训练的分类器的性能在统计上高于机会，我们表明它容易受到网站的影响，无法解释特定于新网站的文物。MRIQC在站点内预测中具有高精度，但在看不见的站点上的性能仍有改进的空间，这可能需要更多的标记数据和新的方法来处理站点间的变异性。克服这些局限性对于更客观地评估神经影像学数据的质量至关重要，并且能够分析非常大和多站点的样本。
Quality control of MRI is essential for excluding problematic acquisitions and avoiding bias in subsequent image processing and analysis. Visual inspection is subjective and impractical for large scale datasets. Although automated quality assessments have been demonstrated on single-site datasets, it is unclear that solutions can generalize to unseen data acquired at new sites. Here, we introduce the MRI Quality Control tool (MRIQC), a tool for extracting quality measures and fitting a binary (accept/exclude) classifier. Our tool can be run both locally and as a free online service via the OpenNeuro. org portal. The classifier is trained on a publicly available, multi-site dataset (17 sites, N = 1102). We perform model selection evaluating different normalization and feature exclusion approaches aimed at maximizing across-site generalization and estimate an accuracy of 76%-13% on new sites, using leave-one-site-out cross-validation. We confirm that result on a held-out dataset (2 sites, N = 265) also obtaining a 76% accuracy. Even though the performance of the trained classifier is statistically above chance, we show that it is susceptible to site effects and unable to account for artifacts specific to new sites. MRIQC performs with high accuracy in intra-site prediction, but performance on unseen sites leaves space for improvement which might require more labeled data and new approaches to the between-site variability. Overcoming these limitations is crucial for a more objective quality assessment of neuroimaging data, and to enable the analysis of extremely large and multi-site samples.