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https://github.com/FCP-INDI/awesoMRI-QC
A curated list of measures, tools, and references for MRI quality control (QC).
https://github.com/FCP-INDI/awesoMRI-QC
List: awesoMRI-QC
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A curated list of measures, tools, and references for MRI quality control (QC).
- Host: GitHub
- URL: https://github.com/FCP-INDI/awesoMRI-QC
- Owner: FCP-INDI
- License: mit
- Created: 2022-10-18T13:19:15.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2022-10-18T13:43:22.000Z (about 2 years ago)
- Last Synced: 2024-12-02T00:02:49.638Z (20 days ago)
- Topics: awesome-list, mri-analysis, neuroimaging, quality-control
- Language: TeX
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- Stars: 1
- Watchers: 6
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# Awesome MRI QC
A curated list of tools, measures, and references for MRI quality
control (QC).
> Quality Control (QC) refers to “a real-time prospective process to
> ensure imaging quality is maintained by comparing it regularly to a
> defined set of criteria or industry standards” ([Sreedher et al.,
> 2021](#ref-Sreedher2021)) via ([Das et al., 2022](#ref-Das2022)).
Inspired by [awesome-python](https://github.com/vinta/awesome-python)
and other [awesome lists](https://github.com/sindresorhus/awesome).
- [Tools](#tools)
- [QC metric and report generation](#qc-metric-and-report-generation)
- [MRI analysis packages with built-in
QC](#mri-analysis-packages-with-built-in-qc)
- [QC at scale](#qc-at-scale)
- [QC web platforms](#qc-web-platforms)
- [Automated QC with machine
learning](#automated-qc-with-machine-learning)
- [General-purpose image quality
estimation](#general-purpose-image-quality-estimation)
- [Measures](#measures)
- [Structural T1](#structural-t1)
- [Image quality](#image-quality)
- [Tissue segmentation](#tissue-segmentation)
- [Brain extraction](#brain-extraction)
- [Spatial normalization](#spatial-normalization)
- [Surface reconstruction](#surface-reconstruction)
- [Cortical/subcortical
segmentation](#corticalsubcortical-segmentation)
- [Functional BOLD](#functional-bold)
- [Image quality](#image-quality-1)
- [Motion correction](#motion-correction)
- [Co-registration](#co-registration)
- [Functional connectivity](#functional-connectivity)
- [Diffusion weighted imaging (DWI)](#diffusion-weighted-imaging-dwi)
- [Image quality](#image-quality-2)
- [Fiber orientation modeling](#fiber-orientation-modeling)
- [Tractography](#tractography)
- [Structural connectivity](#structural-connectivity)
- [References](#references)
## Tools
### QC metric and report generation
- [MRIQC](https://mriqc.readthedocs.io/): extracts no-reference image
quality metrics from structural and functional MRI data.
- [QAP](http://preprocessed-connectomes-project.org/quality-assessment-protocol/):
The QAP package allows you to obtain spatial and anatomical data
quality measures for your own data. (Precursor to MRIQC.)
- [fBIRN QA tools](https://www.nitrc.org/projects/bxh_xcede_tools):
These tools form the basis of the fBIRN QA procedures ([Glover et al.,
2012](#ref-Glover2012)).
- [MRQy](https://github.com/ccipd/MRQy): A quality assurance and
checking tool for quantitative assessment of MRI data
### MRI analysis packages with built-in QC
- [AFNI](https://afni.nimh.nih.gov/): A suite of programs for looking at
and analyzing MRI brain images at all stages of analysis.
- [`3dToutcount`](https://afni.nimh.nih.gov/pub/dist/doc/program_help/3dToutcount.html):
Calculates number of “outliers” a 3D+time dataset, at each time
point.
- [`3dTqual`](https://afni.nimh.nih.gov/pub/dist/doc/program_help/3dTqual.html):
Computes a “quality index” for each sub-brick in a 3D+time dataset.
- [FSL](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki): A comprehensive library
of analysis tools for FMRI, MRI and DTI brain imaging data.
- [EDDY QC
tools](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/eddyqc/UsersGuide#The_EDDY_QC_tools)
: Generates single-subject and group QC reports for diffusion MRI.
- [FEAT
report](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT/UserGuide): HTML
report for single-subject and group fMRI analysis.
- [`fsl_motion_outliers`](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLMotionOutliers):
command-line tool for analyzing single-subject motion.
- [NiPype](https://nipype.readthedocs.io/): Provides a uniform interface
to existing neuroimaging software and facilitates interaction between
these packages within a single workflow
- [C-PAC](https://fcp-indi.github.io/docs/latest/user/index): A
configurable, open-source, Nipype-based, automated processing pipeline
for resting state functional MRI data
- [C-PAC
QC](https://fcp-indi.github.io/docs/latest/user/pipelines/quality.html):
QC metrics and reports extending
[XCP](https://xcpengine.readthedocs.io/index.html).
- [XCP](https://xcpengine.readthedocs.io/index.html): A free,
open-source software package for processing of multimodal neuroimages
with extensive QC metrics for T1w and BOLD MRI images.
- [DPABI](http://rfmri.org/dpabi): A toolbox for Data Processing &
Analysis for Brain Imaging including GUI-based QC reports.
- [DSI Studio](https://dsi-studio.labsolver.org/): A tractography
software tool that maps brain connections and correlates findings with
neuropsychological disorders. Includes automated QC metrics.
- [QSIprep](https://qsiprep.readthedocs.io/): Configures pipelines for
processing diffusion-weighted MRI (dMRI) data. Includes QC metrics
from [DSI Studio](https://dsi-studio.labsolver.org/).
### QC at scale
- [NiRV](https://github.com/ni-report-viewer/nirv): A modern
neuroimaging report viewer that aggregates participant level HTML
reports for datasets, small and large.
- [NiReports](https://github.com/nipreps/nireports): The NiPreps’
Reporting and Visualization system - report templates and “reportlets”
- [SQAN](https://github.com/IUSCA/SQAN): Scalable Quality Assurance for
Neuroimaging. A full-stack system for extracting, translating,
logging, and visualizing DICOM-formatted medical imaging data.
### QC web platforms
- [MIQA](https://miqa.kitware.com/): Efficient and accurate QC
processing by leveraging modern UI/UX and deep learning techniques
- [MindControl](https://github.com/akeshavan/mindcontrol): An app for
quality control of neuroimaging pipeline outputs, especially
anatomical segmentations
- [Braindr](https://github.com/OpenNeuroLab/braindr): a firebase app for
braindr: Tinder for brains
- [Fibr](https://fibr.dev): An app for quality control of diffusion MRI
images from the Healthy Brain Network
- [dmriprep-viewer](http://www.nipreps.org/dmriprep-viewer): Web app to
visualize local QSIprep and dMRIprep outputs
### Automated QC with machine learning
- [Qoala-T](https://github.com/Qoala-T/QC): Qoala-T is a
supervised-learning tool for quality control of FreeSurfer segmented
MRI data
- [mriqc-learn](https://github.com/nipreps/mriqc-learn): Learning on
MRIQC-generated image quality metrics (IQMs)
### General-purpose image quality estimation
- [sewar](https://github.com/andrewekhalel/sewar): All image quality
metrics you need in one package.
- [MATLAB
IQMs](https://www.mathworks.com/help/images/image-quality-metrics.html):
Full and no-reference image quality metrics implemented in MATLAB.
- [awesome-image-quality-assessment](https://github.com/chaofengc/Awesome-Image-Quality-Assessment):
Awesome list of image quality tools and references.
## Measures
### Structural T1
#### Image quality
| Measure | Summary | Interpretation | References |
|----------------------------------------|------------------------------------------------------------------------------------|----------------------------------|------------------------------------------------------------------------------------------|
| Coefficient of joint variation (CJV) | Larger values indicate head motion and INU artifacts | lower better | [MRIQC](https://mriqc.readthedocs.io/), ([Ganzetti et al., 2016](#ref-Ganzetti2016)) |
| Contrast-to-noise ratio (CNR) | Larger values indicate more GM to WM contrast | higher better | [MRIQC](https://mriqc.readthedocs.io/), ([Magnotta & Friedman, 2006](#ref-Magnotta2006)) |
| Signal-to-noise ratio (SNR) | SNR within brain mask | higher better | [MRIQC](https://mriqc.readthedocs.io/) |
| Dietrich SNR (SNRd) | SNR relative to air background | higher better | [MRIQC](https://mriqc.readthedocs.io/), ([Dietrich et al., 2007](#ref-Dietrich2007)) |
| Mortamet’s quality index 1 (QI1) | Proportion of “corrupted” voxels vs number of background voxels | lower better | [MRIQC](https://mriqc.readthedocs.io/), ([Mortamet et al., 2009](#ref-Mortamet2009)) |
| Mortamet’s quality index 2 (QI2) | Comparison of background noise with $\Chi^2$ distribution after correcting for QI1 | lower better | [MRIQC](https://mriqc.readthedocs.io/), ([Mortamet et al., 2009](#ref-Mortamet2009)) |
| EFC | Shannon entropy as indicator of ghosting due to head motion | lower better | [MRIQC](https://mriqc.readthedocs.io/), ([Atkinson et al., 1997](#ref-Atkinson1997)) |
| FBER | Ratio of “mean energy” within head vs air | higher better | [MRIQC](https://mriqc.readthedocs.io/), ([Shehzad et al., 2015](#ref-Shehzad2015)) |
| INU | Summary stats for INU bias field. Values close to 1 mean less bias. | higher better | [MRIQC](https://mriqc.readthedocs.io/), ([Tustison et al., 2010](#ref-Tustison2010)) |
| White matter to maximum ratio (WM2max) | Median WM intensity divided by 95 percentile intensity | values in `[0.6, 0.8]` are good. | [MRIQC](https://mriqc.readthedocs.io/) |
| FWHM | Estimation of image smoothness. Higher values mean blurry. | lower better | [MRIQC](https://mriqc.readthedocs.io/) |
#### Tissue segmentation
| Measure | Summary | Interpretation | References |
|--------------------------|----------------------------------------------------------------------------|----------------|-------------------------------------------------------------------------------------------------------|
| ICV | Intracranial volume fraction for each tissue type (WM, GM, CSF) | | [MRIQC](https://mriqc.readthedocs.io/) |
| rPVe | Residual partial voluming error for each tissue type | lower better | [MRIQC](https://mriqc.readthedocs.io/) |
| Tissue summary stats | Summary stats for signal within tissue masks (mean, stdev, p05, p95) | | [MRIQC](https://mriqc.readthedocs.io/) |
| Tissue prior overlap | Overlap of estimated tissue probability maps with template priors | higher better | [MRIQC](https://mriqc.readthedocs.io/) |
| Tissue skewness/kurtosis | Skewness and kurtosis of intensity distribution for WM, GM, and background | | [MRIQC](https://mriqc.readthedocs.io/), ([Rosen et al., 2018](#ref-Rosen2018)) |
| WM hypointensities | Voxel count of white matter hypointensities | lower better | [Freesurfer](https://surfer.nmr.mgh.harvard.edu/fswiki), ([Klapwijk et al., 2019](#ref-Klapwijk2019)) |
#### Brain extraction
| Measure | Summary | Interpretation | References |
|-----------------------|------------------------------------------------------------------------------------------------------------|------------------------|----------------------------------------------------------|
| Boundary tissue count | Volume of each tissue type lying on brain mask boundary. Large WM values suggest brain extraction failure. | lower WM values better | ([Alfaro-Almagro et al., 2018](#ref-Alfaro-Almagro2018)) |
#### Spatial normalization
| Measure | Summary | Interpretation | References |
|-------------------------|--------------------------------------------------------------------------------------------------|----------------|--------------------------------------------------------------------------------------------------------------|
| Normalization cost | Cost function between T1 and template under linear and nonlinear alignment | lower better | |
| Normalization magnitude | Amount of nonlinear warping | lower better | ([Alfaro-Almagro et al., 2018](#ref-Alfaro-Almagro2018)) |
| Normalized overlap | Dice or Jaccard overlap coefficient between resampled T1 and template for brain and tissue masks | higher better | [XCP](https://xcpengine.readthedocs.io/index.html), ([Alfaro-Almagro et al., 2018](#ref-Alfaro-Almagro2018)) |
#### Surface reconstruction
| Measure | Summary | Interpretation | References |
|--------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------|---------------------------------------------------------------------------------------------------------------------------------------|
| Euler number | `2 - 2g` where `g` is the number of topological holes in the surface. Computed by the [Euler–Lhulier formula](https://www.quantamagazine.org/topology-101-how-mathematicians-study-holes-20210126/) (`V - E + F`) | higher better | ([Rosen et al., 2018](#ref-Rosen2018)), ([Dale et al., 1999](#ref-Dale1999)), [Freesurfer](https://surfer.nmr.mgh.harvard.edu/fswiki) |
| Local gyrification index (LGI) | Measures degree of cortical folding in neighborhood of each vertex (spatial map). | | [Freesurfer](https://surfer.nmr.mgh.harvard.edu/fswiki/LGI) |
| BBR criterion | Measures the magnitude of WM/GM contrast across the WM surface boundary | higher better | ([Greve & Fischl, 2009](#ref-Greve2009)), [Freesurfer](https://surfer.nmr.mgh.harvard.edu/fswiki) |
#### Cortical/subcortical segmentation
| Measure | Summary | Interpretation | References |
|--------------------|---------------------------------------------------------------|----------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Subcortical volume | Volume of each subcortical region in a segmentation (vector). | | [FIRST](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIRST), [Freesurfer](https://surfer.nmr.mgh.harvard.edu/fswiki) |
| Cortical volume | Volume of each cortical region in a parcellation (vector). | | ([Alfaro-Almagro et al., 2018](#ref-Alfaro-Almagro2018)), [ANTs](https://github.com/ANTsX/ANTs) [Freesurfer](https://surfer.nmr.mgh.harvard.edu/fswiki), [Mindboggle](https://mindboggle.info/) |
| Cortical thickness | Mean thickness of each region in a parcellation (vector). | | ([Rosen et al., 2018](#ref-Rosen2018)), [ANTs](https://github.com/ANTsX/ANTs), [Freesurfer](https://surfer.nmr.mgh.harvard.edu/fswiki) |
### Functional BOLD
#### Image quality
| Measure | Summary | Interpretation | References |
|-----------------------------------|---------------------------------------------------------------------------------|----------------|------------------------------------------------------------------------------------------------------------------|
| EFC | Shannon entropy as indicator of ghosting due to head motion | lower better | [MRIQC](https://mriqc.readthedocs.io/), ([Atkinson et al., 1997](#ref-Atkinson1997)) |
| FBER | Ratio of “mean energy” within head vs air | higher better | [MRIQC](https://mriqc.readthedocs.io/), ([Shehzad et al., 2015](#ref-Shehzad2015)) |
| FWHM | Estimation of image smoothness. Higher values mean blurry. | | [MRIQC](https://mriqc.readthedocs.io/) |
| SNR | SNR within brain mask. | higher better | [MRIQC](https://mriqc.readthedocs.io/) |
| BOLD summary stats | BOLD intensity summary stats (mean, stdev, p95, p05) | | [MRIQC](https://mriqc.readthedocs.io/) |
| Global correlation (GCor) | Average correlation between every voxel and every other voxel | | [AFNI](https://afni.nimh.nih.gov/), [MRIQC](https://mriqc.readthedocs.io/), ([Saad et al., 2013](#ref-Saad2013)) |
| Temporal standard deviation (tSD) | Map of temporal standard deviation | lower better | [MRIQC](https://mriqc.readthedocs.io/), ([Marcus et al., 2013](#ref-Marcus2013)) |
| Temporal SNR (tSNR) | Map of temporal mean divided by standard deviation | higher better | [MRIQC](https://mriqc.readthedocs.io/) |
| Ghost to signal ratio (GSR) | Measures amount of signal in regions prone to ghosting | lower better | [MRIQC](https://mriqc.readthedocs.io/) |
| AFNI outlier ratio (AOR) | Mean fraction of “outliers” per fMRI volume using AFNI `3dToutcount` | lower better | [AFNI](https://afni.nimh.nih.gov/), [MRIQC](https://mriqc.readthedocs.io/) |
| AFNI quality index (AQI) | Mean “quality index”, which for each volume is 1 - correlation to median volume | lower better | [AFNI](https://afni.nimh.nih.gov/), [MRIQC](https://mriqc.readthedocs.io/) |
| Number of dummy scans | Number of non-steady state dummy scans | | [MRIQC](https://mriqc.readthedocs.io/) |
| Carpet plot | BOLD time series for a set of ROIs arranged in a matrix | | [MRIQC](https://mriqc.readthedocs.io/), ([Power, 2017](#ref-Power2017)) |
| Air signal | mean BOLD time series for a set of background/air slices | | [MRIQC](https://mriqc.readthedocs.io/) |
#### Motion correction
| Measure | Summary | Interpretation | References |
|-----------------------------|-----------------------------------------------------------------------------------------------|------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| DVARS | Measures amount of signal change between consecutive time points (time series) | spikes indicate significant motion | [MRIQC](https://mriqc.readthedocs.io/), [NiPype](https://nipype.readthedocs.io/), [`fsl_motion_outliers`](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLMotionOutliers), ([Power et al., 2012](#ref-Power2012)) |
| Framewise displacement (FD) | Sum of absolute translation and rotation displacements in mm at each time point (time series) | spikes indicate significant motion | [MRIQC](https://mriqc.readthedocs.io/), [NiPype](https://nipype.readthedocs.io/), ([Jenkinson et al., 2002](#ref-Jenkinson2002)), ([Power et al., 2012](#ref-Power2012)) |
#### Co-registration
| Measure | Summary | Interpretation | References |
|----------------------|------------------------------------------------------------------------------|----------------|----------------------------------------------------|
| Co-registration cost | Cost function for rigid registration between BOLD and T1 | lower better | [XCP](https://xcpengine.readthedocs.io/index.html) |
| Brain mask overlap | Dice or Jaccard brain mask overlap coefficient between resampled BOLD and T1 | higher better | [XCP](https://xcpengine.readthedocs.io/index.html) |
#### Functional connectivity
**TODO**
### Diffusion weighted imaging (DWI)
#### Image quality
| Measure | Summary | Interpretation | References |
|---------------------------|-------------------------------------------------------------------------------------------|---------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------|
| Mean neighbor correlation | Average Pearson correlation between each diffusion image and its q-space nearest neighbor | expected range `[0.6, 0.8]` | [QSIprep](https://qsiprep.readthedocs.io/), [DSI Studio](https://dsi-studio.labsolver.org/), ([Yeh et al., 2019](#ref-Yeh2019)) |
| Dropout slice count | Count of slices with significant signal dropout | expected less than 0.1% | [QSIprep](https://qsiprep.readthedocs.io/), [DSI Studio](https://dsi-studio.labsolver.org/), ([Yeh et al., 2019](#ref-Yeh2019)) |
| Fiber coherence index | Measures how well fibers are connected to each other | low values indicate flipped b-vectors | [QSIprep](https://qsiprep.readthedocs.io/), [DSI Studio](https://dsi-studio.labsolver.org/), ([Schilling et al., 2019](#ref-Schilling2019)) |
#### Fiber orientation modeling
**TODO**
#### Tractography
**TODO**
#### Structural connectivity
**TODO**
## References
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