Abstract
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.
Highlights
To ensure the highest standards of research quality, reliability, validity, and reproducibility in brain imaging studies, investigators who acquire and/or analyze neuroimaging data are required to test and monitor all facets of image acquisition
Acquisition protocols with relatively long scanning times, such as diffusion tensor imaging (DTI) and functional magnetic resonance imaging, may be sensitive to substantial noise or artifacts during scanning – for instance, artifacts related to subject motion during relatively long duration acquisitions
To provide an illustration of the LONI quality control (QC) system, we evaluate various datasets including imaging data scanned with different imaging modalities, sequences (T1-weighted, T2-weighed, fluid-attenuated inversion recovery (FLAIR)) and different acquisition parameters
Summary
To ensure the highest standards of research quality, reliability, validity, and reproducibility in brain imaging studies, investigators who acquire and/or analyze neuroimaging data are required to test and monitor all facets of image acquisition. Adherence to standardized protocol compliance may be inconsistent Such neuroimaging challenges become more germane in imaging studies of children (Yoshida et al, 2013) and adolescents (Satterthwaite et al, 2012); the confounding influence of head motion on resting-state functional connectivity and DTI structural connectivity (Lauzon et al, 2013; Yoshida et al, 2013) have received substantial attention (Power et al, 2012; Satterthwaite et al, 2012; Van Dijk et al, 2012). Similar effects (Reuter et al, 2015) are evident in 3D acquisitions of structural MRI (sMRI)
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