Aref Kalantari Sarcheshmeh
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README.md
AIDAqc
An automated and simple tool for fast quality analysis of animal MRI
Features
- Input: Bruker raw data or NIFTI (T2-weighted MRI, diffusion-weighted MRI or DTI, and rs-fMRI)
- Calculations: SNR, tSNR, movement variability, data quality categorization (finds bad quality outlier)
- Output Format: CSV sheets & pdf & images
See the poster for all details
Installation
Download the repository => Install Python 3.6 or higher (Anaconda) => Import AIDAqc conda environment aidaqc.yaml or install all necessery libraries via requirements.txt (recommended). Main function: *ParsingData* See the full manual [here](https://gin.g-node.org/Aswendt_Lab/2023_Kalantari_AIDAqc/src/master/code/AIDAqc_Code/docs/AIDAqc_v2_1.pdf).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 for a recent project to screen hundreds of scans, we decided to automate this process as part of our Atlas-based Processing Pipeline (AIDA).
Download test dataset
https://gin.g-node.org/Aswendt_Lab/testdata_aidaqcCONTACT
Aref Kalantari (aref.kalantari-sarcheshmehATuk-koeln.de) and Markus Aswendt (markus.aswendtATuk-koeln.de)
LICENSE
| datacite.yml | |
|---|---|
| Title | Automated quality control of small animal MR neuroimaging data |
| Authors |
Aswendt,Markus;University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Cologne, Germany ;ORCID:0000-0003-1423-0934
Kalantari,Aref;University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Cologne, Germany ;ORCID:0000-0003-3050-0930 |
| Description | This dataset is a super-dataset containing all datasets used to develop the AIDAqc pipeline and corresponds to the following Publication (DOI): tbd |
| License | CC BY-NC-SA 4.0 (https://creativecommons.org/licenses/by-nc-sa/4.0/) |
| References |
Aref Kalantari, Mehrab Shahbazi, Marc Schneider, Victor Vera Frazão, Avnish Bhattrai, Lorenzo Carnevale, Yujian Diao, Bart A. A. Franx, Francesco Gammaraccio, Lisa-Marie Goncalves, Esther M. van Leeuwen, Annika Michalek, Susanne Mueller, Alejandro Rivera Olvera, Daniel Padro, Adam C. Raikes, Mohamed Kotb Selim, Annette van der Toorn, Roël Vrooman, Patricia Wenk, Philipp Boehm-Sturm, Roberta Diaz Brinton, Eike Budinger, Santiago Canals, Elisenda Eixarch, Giuseppe Lembo, Gianluigi Forloni, Joanes Grandjean, Dario Livio Longo, Khan Hekmatyar, Russell E. Jacobs, Ileana Jelescu, Nyoman D. Kurniawan, Naomi S. Sta Maria, Pedro Ramos-Cabrer, Wilfried Reichardt, Guadalupe Soria, Edoardo Micotti, Emma Muñoz-Moreno, Silvia De Santis, Rick M. Dijkhuizen, Giovanna D. Ielacqua, Markus Aswendt*, 'Automated quality control of preclinical MR neuroimaging data', Available at: DOI:tba. [doi:tba] (IsSupplementTo)
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| Funding |
DFG; 431549029 – SFB 1451
Friebe Foundation; T0498/28960/16 |
| Keywords |
Quality-control
MRI Preclinical-imaging DataLad |
| Resource Type |
Dataset |