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The Feasibility of Fast Neural Magnetic Detection Electrical Impedance Tomography: A Modelling Study

Mason, K; Aristovich, K; Holder, D; (2023) The Feasibility of Fast Neural Magnetic Detection Electrical Impedance Tomography: A Modelling Study. In: Proceedings of the 11th International IEEE/EMBS Conference on Neural Engineering (NER) 2023. (pp. pp. 1-4). Institute of Electrical and Electronics Engineers (IEEE) Green open access

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Abstract

Magnetic Detection Electrical Impedance Tomography (MDEIT) is a possible method to non-invasively image fast neural activity in the human brain by injecting current with scalp electrodes and measuring the change in the magnetic field due to neural activity. A modelling study was performed on an anatomically realistic head model, assessing the SNR and reconstructed image quality for MDEIT and EIT with 3 different realistic noise cases. EIT produced a larger SNR than MDEIT for 2 out of the 3 noise cases. However, MDEIT was found to reconstruct images with a significantly lower error for all the reconstruction cases considered (P< 0.001).

Type: Proceedings paper
Title: The Feasibility of Fast Neural Magnetic Detection Electrical Impedance Tomography: A Modelling Study
Event: 11th International IEEE/EMBS Conference on Neural Engineering (NER) 2023
ISBN-13: 978-1-6654-6292-1
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/NER52421.2023.10123778
Publisher version: https://doi.org/10.1109/NER52421.2023.10123778
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: Neural imaging, Magnetometry
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Engineering Science Faculty Office
URI: https://discovery.ucl.ac.uk/id/eprint/10172361
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