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

1
Log in to LONI QC using your LONI IDA account credentials
2
Select subjects from your existing IDA collections and submit them for automatic QC processing
3
Review and assess the quality of the various QC measurements and images
4
Save your QC results and submit to the IDA

Requirements

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)

Analysis

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:

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

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

Tools used by the LONI QC system are freely distributed. Download and install the tools on any linux machine to run QC analyses with your resources and visualize results using your preferred methods.
 

Frequently Asked Questions

  1. Does LONI QC work on all browsers?
    For the best experience it is best to use the Google Chrome browser for using the LONI QC website.
    There are known issues on other browsers: Webgl technology to render 3D images is incompatible with Safari.

  2. How long does it take for the LONI QC System to process and extract QA metrics from each scan modality (i.e. sMRI, CT, fMRI, DTI)?
    The workflow used to process the image data can take a varying amount of time depending on several factors, such as the speed and load of the server running it as well as the file sizes of the images. On the LONI cranium server, the process can take anywhere from 10 minutes to about an hour.

  3. What is the difference between "Save" and "Finalize" in reference to saving QC evaluations?
    At the bottom of the full QC report for any image there are 2 options, "Save" and "Finalize". "Save" means that the results will be saved to the QC database so that the evaluation can be continued later. "Finalize" saved the evaluations to the database and also uploads the overall evaluation to the IDA if the user has proper permissions.

  4. Do I have to fill out or edit any of the acquisition parameter data such as "Research Group" and "Scan Date"?
    No, these are extracted from the dicom headers by the QC workflow automatically if they are available. If they cannot be extracted, there is currently no way to fill in that data.

  5. What does invalid data mean? What do I do about the error messages produced by invalid data such as "the number of DICOM does not match the expected amount from the header…"?
    Images that cannot be processed by the QC workflow are put into the invalid data category and a message is displayed describing what the problem was. The errors can come about for a variety of reasons, for example the data provided was not a complete data set - it might be a functional scan that only has 1 image volume, or the header data is not correct. If you recieve an error about your data but disagree that there is anything wrong with the data set, it could be that one of the tools in the workflow had a problem processing that data. You can try resubmitting the data, but if you continue to recieve the error, please contact us.

  6. What types of image formats can LONI QC accept/process?
    If you are processing images from the IDA, the images can be in either DICOM or NIFTI format. If you are locally uploading your data, currently it must be in DICOM format but the system will soon support NIFTI images as well.

  7. Why is [feature] missing from the QC website?
    We are currently working on a variety of features, updates, and bug fixes for the LONI QC website, so it is possible that we are working on adding the functionality you desire. If you want to bring some important feature to our attention, please contact us.



 

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