arefks
README.md
Species: Mouse C57BL/6
Animal model: Demyelination by curpizone (2%) feeding
Study groups: Control (healthy), Cuprizone treated, cuprizone-clemastine treated mice
Scanner: Bruker USR117/16 with advance III electronics
Field: 11.7 T
RF transmission: Bruker volume coil (RF 72mm Vol Coil 500 1H , Rat Body)
RF reception: Bruker surface coil ( RF 500 1H – Mouse Brain SUC) / Neoos Biotech mouse Brain surface coil
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| Title | Automated quality control of small animal MR neuroimaging data |
| Authors |
Kalantari,Aref;University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Cologne, Germany ;ORCID:0000-0003-3050-0930
Shahbazi,Mehrab;Hamedan University of Technology, Faculty of Medical Engineering, Hamedan, Iran Schneider,Marc;University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Cologne, Germany Frazão,Victor Vera;University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Cologne, Germany Bhattrai,Avnish;Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA Carnevale,Lorenzo;IRCCS INM Neuromed, Department of AngioCardioNeurology and Translational Medicine, Pozzilli, Italy Diao,Yujian;Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland, and CIBM Center for Biomedical Imaging, Lausanne, Switzerland Franx,Bart A. A.;Biomedical MR Imaging and Spectroscopy group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands Gammaraccio,Francesco;Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy Goncalves,Lisa-Marie;Leibniz Institute for Neurobiology (LIN), Combinatorial Neuroimaging Core Facility (CNI), Magdeburg, Germany Lee,Susan;Center for Behavioral Neuroscience, Neuroscience Institute, Advanced Translational Imaging Facility, Georgia State University, Atlanta, Georgia, USA van Leeuwen,Esther M.;Biomedical MR Imaging and Spectroscopy group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands Michalek,Annika;Leibniz Institute for Neurobiology (LIN), Combinatorial Neuroimaging Core Facility (CNI), Magdeburg, Germany Mueller,Susanne;Department 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 Olvera,Alejandro Rivera;Ikerbasque, Basque Foundation for Science, Bilbao, Spain Padro,Daniel;Center for Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, Spain Raikes,Adam C.;Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA Selim,Mohamed Kotb;Instituto de Neurociencias, CSIC/UMH, San Juan de Alicante, 03550 Alicante, Spain van der Toorn,Annette;Biomedical MR Imaging and Spectroscopy group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands Varriano,Federico;Laboratory of Surgical and Experimental Neuroanatomy, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain Vrooman,Roël;Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands Wenk,Patricia;Leibniz Institute for Neurobiology (LIN), Combinatorial Neuroimaging Core Facility (CNI), Magdeburg, Germany Albers,H Elliott;Center for Behavioral Neuroscience, Neuroscience Institute, Advanced Translational Imaging Facility, Georgia State University, Atlanta, Georgia, USA Boehm-Sturm,Philipp;Department 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 Budinger,Eike;Leibniz Institute for Neurobiology (LIN), Combinatorial Neuroimaging Core Facility (CNI), Magdeburg, Germany Canals,Santiago;Instituto de Neurociencias, CSIC/UMH, San Juan de Alicante, 03550 Alicante, Spain Santis,Silvia De;Instituto de Neurociencias, CSIC/UMH, San Juan de Alicante, 03550 Alicante, Spain Brinton,Roberta Diaz;Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA Dijkhuizen,Rick M.;Biomedical MR Imaging and Spectroscopy group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands Eixarch,Elisenda;BCNatal 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 Forloni,Gianluigi;Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Department of Neuroscience, Milan, Italy Grandjean,Joanes;Donders 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 Hekmatyar,Khan;Center for Behavioral Neuroscience, Neuroscience Institute, Advanced Translational Imaging Facility, Georgia State University, Atlanta, Georgia, USA Jacobs,Russell E.;Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA Jelescu,Ileana;Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland, and CIBM Center for Biomedical Imaging, Lausanne, Switzerland Kurniawan,Nyoman D.;Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St. Lucia, Australia Lembo,Giuseppe;IRCCS INM Neuromed, Department of AngioCardioNeurology and Translational Medicine, Pozzilli, Italy, and Sapienza University of Rome, Department of Molecular Medicine, Rome, Italy Longo,Dario Livio;Institute of Biostructures and Bioimaging (IBB), National Research Council of Italy (CNR), Turin, Italy Sta Maria,Naomi S.;Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA Micotti,Edoardo;Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Department of Neuroscience, Milan, Italy Muñoz-Moreno,Emma;MRI Core Facility, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain Ramos-Cabrer,Pedro;Center for Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, Spain Reichardt,Wilfried;Medical Physics, Department of Radiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany Soria,Guadalupe;CIBER 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 Ielacqua,Giovanna D.;Preclinical Research Center (PRC), Max-Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany Aswendt,Markus;University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Cologne, Germany ;ORCID:0000-0003-1423-0934 |
| 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. |
| License | CC BY-NC-SA 4.0 (https://creativecommons.org/licenses/by-nc-sa/4.0/) |
| References |
Kalantari, Aref et. al 2024, Automated quality control of small animal MR neuroimaging data, Imaging Neuroscience, first submission on 23.12.2023 [doi:tba] (IsSupplementTo)
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| 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 |
| Keywords |
Quality control
MRI Preclinical imaging DataLad Animal models Imaging fMRI analysis Machine learning Motion analysis Functional imaging Diffusion weighted imaging Image quality assessment |
| Resource Type |
Dataset |