Quality control procedures and metrics for resting-state functional MRI.
Quality control procedures and metrics for resting-state functional MRI.
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DOI：
10.3389/fnimg.2023.1072927
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发表时间：
2023
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影响因子：
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通讯作者：
Birn, Rasmus M
中科院分区：
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作者：
Birn, Rasmus M
数据质量的监测和评估是功能磁共振成像（fMRI）数据采集和分析的重要步骤。理想情况下，在采集数据时进行数据质量监测，受试者仍在MRI扫描仪中，以便及早发现并解决任何错误。在处理管道中的多个点执行数据质量评估也很重要。当分析来自多个研究者和/或机构的大量受试者的数据集时，尤其如此。这些质量控制程序不仅应监测原始数据和经处理数据的质量，而且还应监测采集参数的准确性和一致性。采集参数中的位置间差异可以指导某些处理步骤的选择（例如，从倾斜方向恢复，空间平滑）。各种质量控制指标可以确定从组分析中排除哪些受试者，并且还可以指导可能必要的附加处理步骤。本文描述了定性和定量评估的结合来确定fMRI数据的质量。使用AFNI数据分析包进行处理。定性评估包括目视检查结构T1加权和fMRI回波平面图像，功能连接图，功能连接强度，以及从所有受试者连接到电影格式的时间信噪比图。定量度量包括采集参数、关于受试者运动水平的统计、时间信噪比、数据的平滑度和平均功能连接强度。在处理流水线中的不同步骤评估这些测量，以捕捉数据中的总体异常，并确定采集参数的偏差、与模板空间的对准、头部运动的水平和其他噪声源。我们还评估了不同定量QC截止值的效果，特别是运动审查阈值，以及带通滤波的影响。然后，这些定性和定量指标可以提供关于在分析大型数据集时排除哪些主题以及更仔细地检查哪些主题的信息。
The monitoring and assessment of data quality is an essential step in the acquisition and analysis of functional MRI (fMRI) data. Ideally data quality monitoring is performed while the data are being acquired and the subject is still in the MRI scanner so that any errors can be caught early and addressed. It is also important to perform data quality assessments at multiple points in the processing pipeline. This is particularly true when analyzing datasets with large numbers of subjects, coming from multiple investigators and/or institutions. These quality control procedures should monitor not only the quality of the original and processed data, but also the accuracy and consistency of acquisition parameters. Between-site differences in acquisition parameters can guide the choice of certain processing steps (e.g., resampling from oblique orientations, spatial smoothing). Various quality control metrics can determine what subjects to exclude from the group analyses, and can also guide additional processing steps that may be necessary. This paper describes a combination of qualitative and quantitative assessments to determine the quality of fMRI data. Processing is performed using the AFNI data analysis package. Qualitative assessments include visual inspection of the structural T1-weighted and fMRI echo-planar images, functional connectivity maps, functional connectivity strength, and temporal signal-to-noise maps concatenated from all subjects into a movie format. Quantitative metrics include the acquisition parameters, statistics about the level of subject motion, temporal signal-to-noise ratio, smoothness of the data, and the average functional connectivity strength. These measures are evaluated at different steps in the processing pipeline to catch gross abnormalities in the data, and to determine deviations in acquisition parameters, the alignment to template space, the level of head motion, and other sources of noise. We also evaluate the effect of different quantitative QC cutoffs, specifically the motion censoring threshold, and the impact of bandpass filtering. These qualitative and quantitative metrics can then provide information about what subjects to exclude and what subjects to examine more closely in the analysis of large datasets.