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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Neuroinform. | doi: 10.3389/fninf.2019.00060

LONI QC system: a semi-automated, web-based and freely-available environment for the comprehensive quality control of neuroimaging data

 Hosung Kim1*,  Andrei Irimia1*,  Samuel M. Hobel1, Rita I. Esquivel Castelo-Blanco1,  Ben Duffy1,  Lu Zhao1, Karen L. Crawford1,  Sook-Lei Liew1,  Kristi Clark1, Meng Law1, pratik Mukherjee2, Geoffrey T. Manley2, John D. Van Horn1 and  Arthur W. Toga1
  • 1Laboratory of Neuro Imaging - University of Southern California, United States
  • 2University of California, San Francisco, United States

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.

Keywords: Quality control, Magnetic Resonance Imaging, Diffusion Tensor Imaging, functional magnetic resonance imaging, LONI Pipeline, Automated QC

Received: 22 Jan 2019; Accepted: 12 Aug 2019.

Copyright: © 2019 Kim, Irimia, Hobel, Esquivel Castelo-Blanco, Duffy, Zhao, Crawford, Liew, Clark, Law, Mukherjee, Manley, Van Horn and Toga. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Prof. Hosung Kim, Laboratory of Neuro Imaging - University of Southern California, Los Angeles, CA 90033, California, United States, ghtjdk@gmail.com
Dr. Andrei Irimia, Laboratory of Neuro Imaging - University of Southern California, Los Angeles, CA 90033, California, United States, Andrei.irimia@loni.usc.edu