https://github.com/aswendt-lab/aidaqc
An automated and simple tool for fast quality analysis of animal MRI
Science Score: 26.0%
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○Scientific vocabulary similarity
Low similarity (16.1%) to scientific vocabulary
Keywords
Repository
An automated and simple tool for fast quality analysis of animal MRI
Basic Info
Statistics
- Stars: 5
- Watchers: 3
- Forks: 4
- Open Issues: 8
- Releases: 0
Topics
Metadata Files
README.md
AIDAqc
An automated and simple tool for fast quality analysis of animal MRI
Features
- Input: Bruker raw data or NIFTY (T2-weighted MRI, diffusion-weighted MRI, or DTI, and rs-fMRI)
- Calculations: SNR, tSNR, movement variability, data quality categorization (finds bad quality outliers)
- Output Format: CSV sheets, PDFs, & images
See the poster for all details
Installation
Download the repository => Install Python 3.6 (Anaconda) => Import AIDAqc conda environment aidaqc.yaml
Main function: ParsingData
See the full manual here.
Docker/Apptainer Usage
```{bash}
Build
docker build aidaqc:2.1 .
Running the main ParsingData.py:
docker run -v /your/project/data:/data -v /your/project/qc aidaqc:2.1 -i /data -o /qc -f raw
```
For installation in a apptainer container for GNU/Linux: ```{bash}
Download the repository
git clone https://github.com/Aswendt-Lab/AIDAqc.git cd AIDAqc
Create a new apptainer container
apptainer build aidaqc.sif apptainer.def
Get into a bash shell in the container
apptainer shell aidaqc.sif
```
Tutorial
To guide you through running the pipeline, please watch the YouTube tutorial.
The story behind this tool
It can be challenging to acquire MR images of consistent quality or to decide between good vs. bad quality data in large databases. Manual screening without quantitative criteria is strictly user-dependent and for large databases is neither practical nor in the spirit of good scientific practice. In contrast to clinical MRI, in animal MRI, there is no consensus on the standardization of quality control measures or categorization of good vs. bad quality images. As we were forced to screen hundreds of scans for a recent project, we decided to automate this process as part of our Atlas-based Processing Pipeline (AIDA).
Validation and Datasets
This tool has been validated and used in the following publication: Publication Link
A total of 23 datasets from various institutes were used for validation and testing. These datasets can be found via: Datasets Link
Download test dataset
CONTACT
Aref Kalantari (aref.kalantari-sarcheshmehATuk-koeln.de) and Markus Aswendt (markus.aswendtATuk-koeln.de)
LICENSE
Owner
- Name: Aswendt-Lab
- Login: Aswendt-Lab
- Kind: organization
- Location: Germany
- Repositories: 3
- Profile: https://github.com/Aswendt-Lab
GitHub Events
Total
- Issues event: 4
- Watch event: 2
- Issue comment event: 2
- Push event: 3
- Pull request review event: 3
- Pull request review comment event: 14
- Pull request event: 3
Last Year
- Issues event: 4
- Watch event: 2
- Issue comment event: 2
- Push event: 3
- Pull request review event: 3
- Pull request review comment event: 14
- Pull request event: 3
Dependencies
- alive-progress *
- dipy *
- joblib *
- lmfit *
- matplotlib *
- natsort *
- nibabel *
- nilearn *
- nipype *
- numpy *
- pandas *
- progressbar *
- pydicom *
- scikit-image *
- scipy *
- seaborn *