Automated quality control of small animal MR neuroimaging data.
Aref Kalantari, Mehrab Shahbazi, Marc Schneider, Adam C Raikes, Victor Vera Frazão, Avnish Bhattrai, Lorenzo Carnevale, Yujian Diao, Bart A A Franx, Francesco Gammaraccio, Lisa-Marie Goncalves, Susan Lee, Esther M van Leeuwen, Annika Michalek, Susanne Mueller, Alejandro Rivera Olvera, Daniel Padro, Mohamed Kotb Selim, Annette van der Toorn, Federico Varriano, Roël Vrooman, Patricia Wenk, H Elliott Albers, Philipp Boehm-Sturm, Eike Budinger, Santiago Canals, Silvia De Santis, Roberta Diaz Brinton, Rick M Dijkhuizen, Elisenda Eixarch, Gianluigi Forloni, Joanes Grandjean, Khan Hekmatyar, Russell E Jacobs, Ileana Jelescu, Nyoman D Kurniawan, Giuseppe Lembo, Dario Livio Longo, Naomi S Sta Maria, Edoardo Micotti, Emma Muñoz-Moreno, Pedro Ramos-Cabrer, Wilfried Reichardt, Guadalupe Soria, Giovanna D Ielacqua, Markus Aswendt
Author Information
Aref Kalantari: University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Cologne, Germany.
Mehrab Shahbazi: Hamedan University of Technology, Faculty of Medical Engineering, Hamedan, Iran.
Marc Schneider: University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Cologne, Germany.
Adam C Raikes: Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA.
Victor Vera Frazão: University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Cologne, Germany.
Avnish Bhattrai: Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA.
Lorenzo Carnevale: IRCCS INM Neuromed, Department of AngioCardioNeurology and Translational Medicine, Pozzilli, Italy.
Yujian Diao: Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland.
Bart A A Franx: Translational Neuroimaging group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.
Francesco Gammaraccio: Institute of Biostructures and Bioimaging (IBB), National Research Council of Italy (CNR), Turin, Italy.
Lisa-Marie Goncalves: Leibniz Institute for Neurobiology (LIN), Combinatorial Neuroimaging Core Facility (CNI), Magdeburg, Germany.
Susan Lee: Center for Behavioral Neuroscience, Neuroscience Institute, Advanced Translational Imaging Facility, Georgia State University, Atlanta, Georgia, USA.
Esther M van Leeuwen: Translational Neuroimaging group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.
Annika Michalek: Leibniz Institute for Neurobiology (LIN), Combinatorial Neuroimaging Core Facility (CNI), Magdeburg, Germany.
Susanne Mueller: Charité 3R | Replace, Reduce, Refine, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Alejandro Rivera Olvera: Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands.
Daniel Padro: Center for Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, Spain.
Mohamed Kotb Selim: Instituto de Neurociencias, CSIC/UMH, San Juan de Alicante, 03550 Alicante, Spain.
Annette van der Toorn: Translational Neuroimaging group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.
Federico Varriano: 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.
Roël Vrooman: Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands.
Patricia Wenk: Leibniz Institute for Neurobiology (LIN), Combinatorial Neuroimaging Core Facility (CNI), Magdeburg, Germany.
H Elliott Albers: Center for Behavioral Neuroscience, Neuroscience Institute, Advanced Translational Imaging Facility, Georgia State University, Atlanta, Georgia, USA.
Philipp Boehm-Sturm: Charité 3R | Replace, Reduce, Refine, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Eike Budinger: Leibniz Institute for Neurobiology (LIN), Combinatorial Neuroimaging Core Facility (CNI), Magdeburg, Germany.
Santiago Canals: Instituto de Neurociencias, CSIC/UMH, San Juan de Alicante, 03550 Alicante, Spain.
Silvia De Santis: Instituto de Neurociencias, CSIC/UMH, San Juan de Alicante, 03550 Alicante, Spain.
Roberta Diaz Brinton: Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA.
Rick M Dijkhuizen: Translational Neuroimaging group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.
Elisenda Eixarch: Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
Gianluigi Forloni: Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Joanes Grandjean: Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands.
Khan Hekmatyar: Center for Behavioral Neuroscience, Neuroscience Institute, Advanced Translational Imaging Facility, Georgia State University, Atlanta, Georgia, USA.
Russell E Jacobs: Sapienza University of Rome, Department of Molecular Medicine, Rome, Italy.
Ileana Jelescu: Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland.
Nyoman D Kurniawan: Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy.
Giuseppe Lembo: IRCCS INM Neuromed, Department of AngioCardioNeurology and Translational Medicine, Pozzilli, Italy.
Dario Livio Longo: Ikerbasque, Basque Foundation for Science, Bilbao, Spain.
Naomi S Sta Maria: Sapienza University of Rome, Department of Molecular Medicine, Rome, Italy.
Edoardo Micotti: Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Emma Muñoz-Moreno: Division of Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Pedro Ramos-Cabrer: Center for Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, Spain.
Wilfried Reichardt: Laboratory of Surgical and Experimental Neuroanatomy, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain.
Guadalupe Soria: 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.
Giovanna D Ielacqua: Center for Behavioral Brain Sciences, Magdeburg, Germany.
Markus Aswendt: University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Cologne, Germany.
Magnetic resonance imaging (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 datasets for potential poor-quality outliers can be a challenge. We present AIDAqc, a machine-learning-assisted automated Python-based command-line tool for small animal MRI quality assessment. 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 for a given dataset combines the interquartile range and the machine-learning methods one-class support vector machine, isolation forest, local outlier factor, and elliptic envelope. To evaluate the reliability of individual quality control metrics, a simulation of noise (Gaussian, salt and pepper, speckle) and motion was performed. In outlier detection, single scans with induced artifacts were successfully identified by AIDAqc. AIDAqc was challenged in a large heterogeneous dataset collected from 19 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 agreement (mean Fleiss Kappa score 0.17) is low 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, precision was high (>70%). The outlier data can have a significant impact on further postprocessing, 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.
