LONI Quality Control

The LONI Neuroimaging Quality Control System (LONI QC) is an imaging data review and assessment system for human neuroimaging research studies involving one or more centers. LONI QC allows users to anonymously download imaging data from the LONI IDA and run a standardized quality control check via an automated pre-processing system specifically designed to generate a range of vector statistics and derived images.

This rigorous process assessing your image quality is easy to complete and can be performed by an expert neuroimaging researcher, a post-doctoral fellow, or a student. Users will receive a detailed report of their image quality.


How LONI QC Works

Log in to LONI QC using your LONI IDA account credentials
Select subjects from your existing IDA collections and submit them for automatic QC processing
Review and assess the quality of the various QC measurements and images
Save your QC results and submit to the IDA


User Requirements

  • You must have an IDA/DX account to create QC reports
  • To submit reports back to IDA, you must contact the IDA administrators for permission

Data Format Requirements

  • DICOM image data only
  • Structural MRI (SPGR, MPRAGE)
  • Functional MRI (BOLD EPI)
  • Diffusion Tensor Imaging (DTI)


Types of Quality Control Analysis

  • Structural MRI (sMRI) image analysis
  • Time series based functional MRI (fMRI)
  • Diffusion Tensor Imaging (DTI) reconstructions

About Your Quality Control Results and Ratings

As the data owner, you have the final say on image quality.
Using the computed LONI QC metrics and summary images, you can rate your data according to the following:

Indicates your approval of the data and its readiness for further analysis.
The data needs further review and consideration before it can progress to further analysis.
The image data are not suitable for further analysis.

Contact Us About Your Project Needs

Tell us a little about your project or ask us questions here

Selected Publications on Neuroimaging Quality Control

Aja-Fernandez, S., Estepar, R. S., Alberola-Lopez, C. & Westin, C. F. Image quality assessment based on local variance. Conf Proc IEEE Eng Med Biol Soc 1, 4815-4818, doi:10.1109/iembs.2006.2592516 (2006).
Christodoulou, A. G. et al. A quality control method for detecting and suppressing uncorrected residual motion in fMRI studies. Magn Reson Imaging 31, 707-717, doi:10.1016/j.mri.2012.11.007 (2013).
Gedamu, E. L., Collins, D. L., & Arnold, D. L. Automated quality control of brain MR images. J Magn Reson Imaging 28, 308-319, doi:10.1002/jmri.21434 (2008).
Geissler, A. et al. Contrast-to-noise ratio (CNR) as a quality parameter in fMRI. J Magn Reson Imaging 25, 1263-1270, doi:10.1002/jmri.20935 (2007).
Hasan, K. M. A framework for quality control and parameter optimization in diffusion tensor imaging: theoretical analysis and validation. Magn Reson Imaging 25, 1196-1202, doi:10.1016/j.mri.2007.02.011 (2007).
Ilhalainen, T. M. et al. MRI quality assurance using the ACR phantom in a multi-unit imaging center. Acta Oncol 50, 966-972, doi:10.3109/0284186x.2011.582515 (2011).
Liu, Z. et al. Quality Control of Diffusion Weighted Imagins. Proc Soc Photo Opt Instrum Eng 7628, doi:10.1117/12.844748 (2010).
Menze, B. H., Kelm, B. M., Weber, M. A., Bachert, P. & Hamprecht, F. A. Mimicking the human expert: pattern recognition for an automated assessment of data quality in MR spectroscopic images. Magn Reson Med 59, 1457-1466, doi:10.1002/mrm.21519 (2008).
Mortament, B. et al. Automatics quality assessment in structural brain magnetic resonance imaging. Magn Reson Med 62, 365-372, doi:10.1002/mrm.21992 (2009).
Oguz, I. et al. DTIPrep: quality control of diffusion-weighted images. Front Neuroinform 8, 4, doi:10.3389/fninf.2014.00004 (2014)
Verde, A. R. et al. UNC-Utah NA-MIC framework for DTI fiber tract analysis. Front Neuroinform 7, 51, doi:10.3389/fninf.2014.00004 (2014)
Yoshimaru, E., Totenhagen, J., Alexander, G. E. & Trouard, T. P. Design, manufacture and analysis of customized phantoms for enhanced quality control in small animal MRI systems. Magn Reson Med, doi:10.1002/mrm.24678 (2013).

Download the Source Materials

Everything in the LONI QC system is freely distributed. Install the workflows and tools on any machine, run QC analyses with your resources, and visualize results using your preferred methods. We have provided detailed setup instructions that explain how to install each component to get your own QC system up and running quickly