Abstract #0245
The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): A Challenge for Reproducible DCE-MRI AI-based Analysis
Soudabeh Kargar1, Lucy Kershaw2,3, Anahita Fathi Kazerooni4, Laura Bell5, Rianne Van der Heijden6, Henk-Jan Mutsaerts7,8, Oliver Gurney-Champion9,10, Eve Shalom11, Andre Paschoal12, Mu-Lan Jen13, Safa Hoodeshenas14, Natalie Serkova15, Petra Van Houdt16, Yuriko Suzuki17, and Harrison Kim18
1Cancer Center, University of Colorado, Aurora, CO, United States, 2Edinburgh Imaging, The University of Edinburgh, Edinburgh, United Kingdom, 3Centre for Cardiovascular Science, The University of Edinburgh, Edinburgh, United Kingdom, 4Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, United States, 5Clinical Imaging Group, Genentech, South San Francisco, CA, United States, 6Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, Netherlands, 7Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam, Netherlands, 8Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands, 9Department of Radiology and Nuclear Medicine, University of Amsterdam, Amsterdam, Netherlands, 10Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, Netherlands, 11School of Physics and Astronomy, University of Leeds, Leeds, United Kingdom, 12Institute of Physics, University of Campinas, Campinas, Brazil, 13Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 14Department of Radiology, Mayo Clinic, Rochester, MN, United States, 15Department of Radiology, University of Colorado, Aurora, CO, United States, 16Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands, 17Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, 18Radiology, University of Alabama in Birmingham, Birmingham, AL, United States
Synopsis
Keywords: Data Processing, DSC & DCE Perfusion, Deep Learning
Motivation: There is a need for reproducibility, repeatability, and accuracy. Previously, OSIPI organized a challenge for benchmarking DCE software. As the use of artificial intelligence grows, we now set out to repeat the challenge, focusing on deep learning techniques.
Goal(s): To encourage researchers put their quantitative methods to test and stimulate collaboration and to charter the heterogeneity of DCE analysis software.
Approach: To use deep learning techniques to estimate perfusion parameters in DCE-MRI of the uterus. We share repeated in-vivo data to assess the algorithm’s precision, and simulated DCE-data to test the accuracy.
Results: Top three winners may present their method at ISMRM 2025.
Impact: As quantitative perfusion MRI receives more importance and attention, the need for reproducibility, repeatability, and accuracy is inevitable. A public challenge within the MRI community is a great way to highlight the quantification of DCE-MRI.
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