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Abstract #1521

Developing a Public Dataset for 15O-H2O PET/MRI with ASL Perfusion in Healthy Controls and Moyamoya Patients: Methods, Validation & Applications

BIN JIANG1, Moss Zhao2, Audrey Fan3, Mehdi Khalighi4, Gary K Steinberg4, and Greg Zaharchuk2
1Radiology, Stanford University, Stanford, CA, United States, 2Stanford University, Stanford, CA, United States, 3UC Davis, Davis, CA, United States, 4Stanford University, stanford, CA, United States

Synopsis

Keywords: PET/MR, Arterial spin labelling, perfusion imaging, PET, dataset, open source

Motivation: Create a publicly available dataset combining non-invasive ASL perfusion imaging and simultaneous 15O-H2O PET/MRI to facilitate research replication, deep learning model training, and advance open science initiatives.

Goal(s): Introduce and validate a dataset comparing ASL perfusion imaging against gold-standard 15O-H2O PET/MRI to improve reproducibility studies and support research in healthy controls and Moyamoya patients.
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Approach: Imaging data from 70 healthy controls and 50 Moyamoya patients using 3T PET/MRI covered structural, DWI, ASL perfusion, DSC, and 15O-H2O PET data. Data were harmonized and validated per BIDS specifications.

Results: The dataset passed BIDs validation and included demographics, raw data, BIDS-compliant metadata, and derivatives(CBF maps).

Impact: This dataset can significantly advance neuroimaging research by enabling reproducibility studies, validating ASL imaging against PET, and supporting artificial intelligence approaches. It provides a critical resource for scientists, clinicians, and data-driven innovations.

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Keywords