Abstract #5008

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

Pascal Spincemaille¹, Zhe Liu^1,2, Shun Zhang^1,3, Matteo Ippoliti⁴, Marcus Makowski⁴, Richard Watts⁵, Ludovic de Rochefort⁶, Vijay Venkatraman⁷, Patricia Desmond⁷, Brian Kopell^8,9,10,11, Patrice Péran¹², and Yi Wang^1,2

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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