Abstract #4869

ISMRM - Open Science Initiative for Perfusion Imaging (OSIPI): The multi-delay Arterial Spin Labeling Challenge

Icaro A F Oliveira¹, Sriranga Kashyap¹, Henk JMM Mutsaerts^2,3, Jan Petr⁴, Joana Pinto⁵, Joseph G Woods⁶, Moss Y Zhao⁷, and Andre Monteiro Paschoal⁸

¹Krembil Brain Institute, University Health Network, Toronto, ON, Canada, ²Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, Netherlands, ³Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands, ⁴Helmholtz-Zentrum Dresden-Rossendorf, Desden, Germany, ⁵Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom, ⁶Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, ⁷Department of Neurosurgery, Stanford University, Stanford, CA, United States, ⁸Institute of Physics, University of Campinas, Campinas, Brazil

Synopsis

Keywords: Arterial Spin Labelling, Arterial spin labelling, Challenge; multi-PLD ASL

Motivation: The OSIPI ASL Challenge is a community initiative motivated by open science principles that aim to establish good practices in ASL image analysis and Cerebral Blood Flow (CBF) quantification.

Goal(s): The second ASL challenge's main goal is to provide a thorough comparison of existing post-processing pipelines focusing on Multi-PLD methodology.

Approach: The second roadmap will provide different datasets; a population dataset which will bring real variability to the challenge and synthetic data which allows straightforward ground truth comparison.

Results: The second edition of the ASL Challenge will contribute to gaining new insights into the potential sources of variability within the multi-PLD analysis pipeline.

Impact: Through the second edition of the ASL Challenge, we seek to enhance our understanding of multi-PLD analysis in the ASL community. Its success could establish a consensus on the processing of multi-PLD ASL data, positively influencing clinical and scientific practices.

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