Polymer thick film technology for improved simultaneous dEEG/MRI recording: Safety and MRI data quality.

Autor: Poulsen C; Electrical Geodesics, Inc, Eugene, Oregon, USA., Wakeman DG; A. A. Martinos Center, Harvard Medical School, Massachusetts General Hospital, Charlestown, Massachusetts, USA., Atefi SR; A. A. Martinos Center, Harvard Medical School, Massachusetts General Hospital, Charlestown, Massachusetts, USA., Luu P; Electrical Geodesics, Inc, Eugene, Oregon, USA., Konyn A; Electrical Geodesics, Inc, Eugene, Oregon, USA., Bonmassar G; A. A. Martinos Center, Harvard Medical School, Massachusetts General Hospital, Charlestown, Massachusetts, USA.
Jazyk: angličtina
Zdroj: Magnetic resonance in medicine [Magn Reson Med] 2017 Feb; Vol. 77 (2), pp. 895-903. Date of Electronic Publication: 2016 Feb 15.
DOI: 10.1002/mrm.26116
Abstrakt: Purpose: To develop a 256-channel dense-array electroencephalography (dEEG) sensor net (the Ink-Net) using high-resistance polymer thick film (PTF) technology to improve safety and data quality during simultaneous dEEG/MRI.
Methods: Heating safety was assessed with temperature measurements in an anthropomorphic head phantom during a 30-min, induced-heating scan at 7T. MRI quality assessment used B1 field mapping and functional MRI (fMRI) retinotopic scans in three humans at 3T. Performance of the 256-channel PTF Ink-Net was compared with a 256-channel MR-conditional copper-wired electroencephalography (EEG) net and to scans with no sensor net. A visual evoked potential paradigm assessed EEG quality within and outside the 3T scanner.
Results: Phantom temperature measurements revealed nonsignificant heating (ISO 10974) in the presence of either EEG net. In human B1 field and fMRI scans, the Ink-Net showed greatly reduced cross-modal artifact and less signal degradation than the copper-wired net, and comparable quality to MRI without sensor net. Cross-modal ballistocardiogram artifact in the EEG was comparable for both nets.
Conclusion: High-resistance PTF technology can be effectively implemented in a 256-channel dEEG sensor net for MR conditional use at 7T and with significantly improved structural and fMRI data quality as assessed at 3T. Magn Reson Med 77:895-903, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
(© 2016 International Society for Magnetic Resonance in Medicine.)
Databáze: MEDLINE
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