Dataset
Automated quality control of small animal MR neuroimaging data
- 1University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Cologne, Germany
- 2Hamedan University of Technology, Faculty of Medical Engineering, Hamedan, Iran
- 3Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA
- 4IRCCS INM Neuromed, Department of AngioCardioNeurology and Translational Medicine, Pozzilli, Italy
- 5Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland, and CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- 6Biomedical MR Imaging and Spectroscopy group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
- 7Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
- 8Leibniz Institute for Neurobiology (LIN), Combinatorial Neuroimaging Core Facility (CNI), Magdeburg, Germany
- 9Center for Behavioral Neuroscience, Neuroscience Institute, Advanced Translational Imaging Facility, Georgia State University, Atlanta, Georgia, USA
- 10Department of Experimental Neurology and Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany; Charité 3R | Replace, Reduce, Refine, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; and NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Berlin, Germany
- 11Ikerbasque, Basque Foundation for Science, Bilbao, Spain
- 12Center for Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, Spain
- 13Instituto de Neurociencias, CSIC/UMH, San Juan de Alicante, 03550 Alicante, Spain
- 14Laboratory of Surgical and Experimental Neuroanatomy, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- 15Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
- 16BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), Universitat de Barcelona; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS); and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Spain
- 17Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Department of Neuroscience, Milan, Italy
- 18Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands, and Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
- 19Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- 20Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St. Lucia, Australia
- 21IRCCS INM Neuromed, Department of AngioCardioNeurology and Translational Medicine, Pozzilli, Italy, and Sapienza University of Rome, Department of Molecular Medicine, Rome, Italy
- 22Institute of Biostructures and Bioimaging (IBB), National Research Council of Italy (CNR), Turin, Italy
- 23MRI Core Facility, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- 24Medical Physics, Department of Radiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- 25CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, and Laboratory of Surgical and Experimental Neuroanatomy, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- 26Preclinical Research Center (PRC), Max-Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
DOI: 10.12751/g-node.q82cjj BROWSE REPOSITORY BROWSE ARCHIVE DOWNLOAD ARCHIVE (ZIP 393 MiB)
Published 25 Jan. 2024 | License Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
Description
MRI is a valuable tool for studying brain structure and function in animal and clinical studies. With the growth of public MRI repositories, access to data has finally become easier. However, filtering large data sets for potential poor quality outliers can be a challenge. We present AIDAqc, a machine learning-assisted automated Python-based command-line tool for the quality assessment of small animal MRI data. Quality control features include signal-to-noise ratio (SNR), temporal SNR, and motion. All features are automatically calculated and no regions of interest are needed. Automated outlier detection is based on the combination of interquartile range and the machine learning methods one-class support vector machine, isolation forest, local outlier factor, and elliptic envelope. AIDAqc was challenged in a large heterogeneous dataset collected from 18 international laboratories, including data from mice, rats, rabbits, hamsters, and gerbils, obtained with different hardware and at different field strengths. The results show that the manual inter-rater variability (mean Fleiss Kappa score 0.17) is high when identifying poor quality data. A direct comparison of AIDAqc results therefore showed only low to moderate concordance. In a manual post-hoc validation of AIDAqc output, agreement was high (>70%). The outlier data can have a significant impact on further post-processing, as shown in representative functional and structural connectivity analysis. In summary, this pipeline optimized for small animal MRI provides researchers with a valuable tool to efficiently and effectively assess the quality of their MRI data, which is essential for improved reliability and reproducibility.
Keywords
| Quality control | MRI | Preclinical imaging | DataLad | Animal models | Imaging | fMRI analysis | Machine learning | Motion analysis | Functional imaging | Diffusion weighted imaging | Image quality assessment |References
- Kalantari, Aref et. al 2024, Automated quality control of small animal MR neuroimaging data, Imaging Neuroscience, first submission on 23.12.2023.
Funding
- DFG 431549029 – SFB 1451
- Friebe Foundation T0498/28960/16
- Italian Minister of Health RRC-2016-2361095, RRC-2017-2364915, RRC-2018-2365796, RCR-2019-23669119_001, RCR 2020-23670067
- Ministry of Economy and Finance CCR-2017-23669078
- Australian National Imaging Facility (NIF)
- Queensland NMR Network (QNN)
- European Union's Horizon 2020 research and innovation program EOSC-Life—Providing an open collaborative space for digital biology in Europe Grant agreement No 824087
- Italian Ministry for Education and Research (FOE funding to the Euro-BioImaging Multi-Modal Molecular Imaging Italian Node)
- NIH S10 OD025016 R01AG057931
- NIH/National Institute on Aging P01AG026572
- Center for Innovation in Brain Science
- Spanish Research Agency Grant PID2020-118546RB-I00
- Horizon Europe programs CANDY under grant agreement nos. 847818
- Dutch Research Council OCENW.KLEIN.334
- Spanish Research Agency PID2021-128158NB-C21, PID2021-128909NA-I00, CEX2021-001165-S
- Spanish Generalitat Valenciana Government PROMETEO/2019/015, CIDEGENT/2021/015
- la Caixa Foundation fellowship code LCF/BQ/DI18/11660067
- Marie Skłodowska-Curie-COFUND agreement Grant No. 713673
- SNSF Eccellenza PCEFP2_194260
- CIBM Center for Biomedical Imaging of the UNIL CHUV, EPFL, HUG, and UNIGE
- DFG project BO 4484/2-1 and EXC-2049-390688087 NeuroCure
- German Federal Ministry of Education and Research (BMBF) under the ERA-NET NEURON scheme 01EW1811 and 01EW2305
- Charité 3R | Replace Reduce, Refine