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Abstract #5008

Clinical Implementation of Quantitative Susceptibility Mapping: Experience Across Multiple Sites and Scanner Platforms.

Pascal Spincemaille1, Zhe Liu1,2, Shun Zhang1,3, Matteo Ippoliti4, Marcus Makowski4, Richard Watts5, Ludovic de Rochefort6, Vijay Venkatraman7, Patricia Desmond7, Brian Kopell8,9,10,11, Patrice Péran12, and Yi Wang1,2

1Radiology, Weill Cornell Medical College, New York, NY, United States, 2Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, United States, 3Radiology, Tongji Hospital, Wuhan, China, 4Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany, 5Radiology, Larner College of Medicine, The University of Vermont, Burlington, VT, United States, 6Center for Magnetic Resonance in Biology and Medicine, Aix Marseille Université, Marseille, France, 7Radiology, University of Melbourne, Melbourne, Australia, 8Division of Movement Disorders, Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, 9Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, United States, 10Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States, 11Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States, 12Toulouse NeuroImaging Center, Université de Toulouse, Toulouse, France

In recent years, quantitative susceptibility mapping (QSM) has undergone a series of technical improvements and found applications in an expanding array of diseases. To support this ongoing process of development and validation, it is important to automate the computationally intensive susceptibility reconstruction on the scanner after the acquisition of gradient echo data. In this work, an online QSM reconstruction system for a variety of scanner platforms with low cross-site ROI standard deviation is demonstrated.

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