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Abstract #1853

Improving multimodal imaging with an MR-compatible EEG net: the R-Net-MR-IT

Nina Fultz1,2,3, Hongbae Jeong1, Stephanie D. Williams1,3, Daniel E.P. Gomez1,3, Beverly Setzer1,3, Tracy Warbrick4, Manfred Jaschke4, Giorgio Bonmassar1, and Laura D. Lewis1,3
1Athinoula A. Martinos Center for Biomedical Engineering, Charlestown, MA, United States, 2Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark, 3Department of Engineering, Boston University, Boston, MA, United States, 4Brain Products GmbH, Gilching, Germany


EEG provides valuable clinical information, but EEG nets produce artifacts in MRI and CT images, preventing these modalities from being combined in typical clinical practice. We tested whether a new MR-compatible EEG net, called the R-Net-MR-IT, could produce high-quality clinical and research images. We assessed image quality in CT and MR images on a phantom with a conventional net, R-Net-MR-IT, and with no net. We then performed the same comparison in humans, as well as fMRI scans. Our results show that the R-Net-MR-IT enables acquisition of high quality CT and MR images, with minimal artifact from EEG hardware.

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