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Abstract #3692

ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): Composite Python Library for ASL Image Processing

Yifan Shuai1, Petra J van Houdt2, Jan Petr3, Luis A Torres4, David L Thomas5, Ben R Dickie6, Lucy E Kershaw7, Limin Zhou8, Azeez Adebimpe9, Zhiliang Wei10, and María Guadalupe Mora Álvarez11
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States, 2Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands, 3Department of Positron Emission Tomography, Institute of Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany, 4Scientific Solutions Engineering, Minneapolis, MN, United States, 5Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, 6Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, United Kingdom, 7Edinburgh Imaging and Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom, 8Philips Healthcare, Cambridge, MA, United States, 9Bayer, Whippany, NJ, United States, 10Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, 11Developing Brain Institute, Children’s National Hospital, Washington, DC, United States

Synopsis

Keywords: Arterial Spin Labelling, Arterial spin labelling

Motivation: The ISMRM-Open Science Initiative for Perfusion Imaging (OSIPI) aims to facilitate collaboration, reproducibility, and transparency in perfusion imaging research by providing a shared platform for tools and methodologies.

Goal(s): To develop an open-source library of functions and scripts for Arterial Spin Labeled (ASL) imaging preprocessing and analysis.

Approach: We harmonized the code collected during our first roadmap into a Python library, PyASL, supporting both human and preclinical brain data.

Results: PyASL currently includes two human data pipelines, two preclinical data pipelines, a deep learning-based denoising function, and a command line utility integration. Each function is fully documented for user reference.

Impact: PyASL provides unified, open-source functions that enable researchers to apply standardized methods across studies, enhancing reproducibility and transparency. It also reduces redundant development, allowing scientists to focus on addressing new challenges in ASL perfusion imaging.

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Abstract

Keywords