Abstract #2714

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

Udunna Anazodo^1,2, Joana Pinto³, Flora A. Kennedy McConnell^4,5,6, 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. Croal^4,5

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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