Version 1.2 updated January 14 2019
Qoala-T is developed in the Brain and development research center by Lara Wierenga, PhD and Eduard Klapwijk, PhD
Qoala-T is a supervised learning tool that asseses accuracy of manual quality control of T1 imaging scans and their automated neuroanatomical labeling processed in FreeSurfer. It is particularly intended to use in developmental datasets. This package contains data and R code as described in Klapwijk et al., (2019) see https://doi.org/10.1016/j.neuroimage.2019.01.014. The protocol of our in house developed manual QC procedure can be found here.
We have also developed an app using R Shiny by which the Qoala-T model can be run without having R installed, see the Qoala-T app.
- To be able to run the Qoala-T model, T1 MRI images should be processed in FreeSurfer V6.0.
- Next extract the following output txt files using fswiki: aseg_stats.txt, aparc_thickness_lh.txt, aparc_area_lh.txt, aparc_thickness_rh.txt, aparc_area_rh.txt.
You can also use the following script to extract only the output files necessary for Qoala-T: stats2table_bash_qoala_t.sh, an adapted version from fswiki. - To create an input file for the Qoala-T tool merge these files into one (see example R script).
- With this R script Qoala-T scores for a dataset are estimated using a supervised- learning model. This model is based on 784 T1-weighted imaging scans of subjects aged between 8 and 25 years old (53% females). The manual quality assessment is described in the Qoala-T manual Manual quality control procedure for structural T1 scans, also available in the supplemental material of Klapwijk et al., (2019).
- To run the model-based Qoala-T option open Qoala_T_A_model_based_github.R and follow the instructions. Alternatively you can run this option without having R installed, see the Qoala-T app.
- An example output table (left) and output graph (right) showing the Qoala-T score of each scan are displayed below. The figure shows the number of included and excluded predictions. The grey area represents the scans that are recommended for manual quality assesment.
- With this R script an in-house developed manual QC protocol can be applied on a subset of the dataset (e.g. 10%, the larger the set, the more reliable the results).
- To run the subset-based Qoala-T option open Qoala_T_B_subset_based_github.R and follow the instructions.
A flowchart of these processes can be observed in A and B below.
In order to continuously evaluate the performance of the Qoala-T tool, we will report predictive accuracies for different datasets on this page. We invite researchers who performed both manual QC and used Qoala-T to share their performance metrics and some basic information about their sample. This can be done by creating a pull request for this Github page or by e-mailing to [email protected]. The table below reports predictive accuracies in new datasets when using the BrainTime model (i.e., option A that can be run using the Shiny app).
| General information | Qoala-T predictions | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Sample name or lab name | Institute | Author name(s) | Group characteristics (e.g., developmental, patient group, elderly) | Total N | Age range (years) | Field strength | T1 sequence type (e.g., MPRAGE, T13D), field of view, dimensions of voxels | doi | Qoala-T version used (current = v1.2) | Accuracy | Specificity | Sensitivity | Manual QC protocol used (e.g., Qoala-T protocol, in-house) | Manual QC distribution (i.e., N per quality category) |
| BESD | Leiden University | Moji Aghajani, Eduard Klapwijk et al. | Adolescents with conduct disorder, autism spectrum disorder, and typically developing | 112 | 15-19 | 3T | T1 3D, FOV 224x177x168, voxel size 0.875 x 0.875 x 1.2 mm | https://doi.org/10.1111/jcpp.12498; https://doi.org/10.1016/j.biopsych.2016.05.017 | v1.2 | 0.893 | 0.978 | 0.524 | Qoala-T protocol | excellent=19, good=51, doubtful=21, failed=21 |
| ABIDE (subset) | NITRC | Di Martino et al. | autism spectrum disorders, typically developing controls | 760 | 6-39 | 3T | site-specific, see http://fcon_1000.projects.nitrc.org/indi/abide/abide_I.html | https://doi.org/10.1038/mp.2013.78 | v1.2 | 0.809 | 0.815 | 0.783 | from MRIQC project: T1 images were rated aided by FreeSurfer surface reconstructions | good/accept=608, doubtful=14, failed/exclude=138 |
| MCN Basel | University of Basel | David Coynel | healthy young adults | 1773 | 18-35 | 3T | MPRAGE, 256x256x176, 1mm3 | http://dx.doi.org/10.1523/ENEURO.0222-17.2018 | v1.1 | 0.963 | 0.985 | 0.524 | in-house visual inspection of raw data | good/excellent: N=1691; doubtful/bad: N=82 |
If you have any question or suggestion don't hesitate to get in touch. Please leave a message at the Issues page.
When using Qoala-T please include the following citation:
Klapwijk, E.T., van de Kamp, F., Meulen, M., Peters, S. and Wierenga, L.M. (2019). Qoala-T: A supervised-learning tool for quality control of FreeSurfer segmented MRI data. NeuroImage, 189, 116-129. https://doi.org/10.1016/j.neuroimage.2019.01.014
Eduard T. Klapwijk, Ferdi van de Kamp, Mara van der Meulen, Sabine Peters, and Lara M. Wierenga
Copyright (C) 2017-2019 Lara Wierenga - Leiden University, Brain and Development Research Center
All rights reserved
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