An automated and simple tool for fast quality analysis of animal MRI
- 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
Download the repository => Install Python 3.6 (Anaconda) => Import AIDAqc conda environment aidaqc.yamlMain function: ParsingData
See the full manual here.
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).
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
https://gin.g-node.org/Aswendt_Lab/testdata_aidaqc Aref Kalantari (aref.kalantari-sarcheshmehATuk-koeln.de) and Markus Aswendt (markus.aswendtATuk-koeln.de)