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Abstract #2714

The Open Source Initiative for Perfusion Imaging (OSIPI) ASL MRI Challenge 

Udunna Anazodo1,2, Joana Pinto3, Flora A. Kennedy McConnell4,5,6, Maria-Eleni Dounavi7, Cassandra Gould van Praag8, Henk Mutsaerts9, Aaron Oliver Taylor10, André Paschoal11, Jan Petr12, Diego Pineda-Ordóñez13, Joseph G. Woods14, Moss Y. Zhao15, and Paula L. Croal4,5
1Department of Medical Biophysics, University of Western Ontario, London, ON, Canada, 2Imaging Division, Lawson Health Research Institute, London, ON, Canada, 3Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom, 4Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom, 5Radiological Sciences, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, United Kingdom, 6Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham, United Kingdom, 7Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom, 8Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom, 9Amsterdam University Medical Center, Amsterdam, Netherlands, 10Gold Standard Phantoms Limited, London, United Kingdom, 11Department of Radiology, LIM44 - HCFMUSP, Sao Paulo, Brazil, 12Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany, 13Department of Radiology, Clinica Del Country, Bogotá, Colombia, 14Department of Radiology, University of California San Diego, San Diego, CA, United States, 15Department of Radiology, Stanford University, Stanford, CA, United States

The OSIPI ASL MRI Challenge is a community-led initiative aiming to establish the range of approaches used for ASL image analysis and cerebral blood flow (CBF) quantification. Challenge data will consist of population-based and synthetic pseudo-continuous ASL images, with participants analysing the data and submitting resulting CBF maps and mean tissue CBF, along with documentation. Entries will be scored on accuracy, reproducibility and documentation quality. Through documenting the analysis choices made within the community, we will begin to better understand sources of variability, ultimately identifying an optimum pipeline, and moving towards the much-needed consensus of ASL image processing standards.

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