Caracterization and modeling of magnetic field switched gradient-induced voltages on electrophysiological signals in MRI

New developments in MRI techniques create sources of induced voltages that “pollute” the simultaneously acquired electrophysiological signals (EPS), used to monitor patients and/or synchronize images. We developed a device to allow a deep study of the contamination mechanism, which would assist in elaborating new tools to obtain higher quality EPS. The system consists of three main modules: (i) a signal transmission system composed of an EPS generator and a transmission box, which transmits the EPS to a MR-compatible receiver inside the tunnel, (ii) an electro-conductive tissue-mimicking phantom in which the EPS is injected, (iii) a signal collection module composed of a MR compatible amplifier-transmitter that emits, via an optical cable, the collected signal to a receiver box placed outside the MRI room. The receiver box comprises 20 channels distributed into four frequency bands (40, 80, 160, and 350 Hz). Measurements of the induced voltages were performed in 1.5 T and 3 T MRI environments. An algorithm to extract and analyze and model the induced voltages was developed. The modeling algorithm is based on a sinusoidal decomposition of the induced voltages. This work aimed to assess the disturbance level of the EPS, when using larger bandwidth amplifiers. The characterization and modeling of the induced voltages, which represent the dominant noise, reveal relevant information which can be used to develop robust and efficient noise reduction algorithms.

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Source https://theses.hal.science/tel-00869185
Author El Tatar, Aziz
Maintainer CCSD
Last Updated May 9, 2026, 12:18 (UTC)
Created May 9, 2026, 12:18 (UTC)
Identifier NNT: 2013COMP2069
Language fr
Rights https://about.hal.science/hal-authorisation-v1/
contributor Biomécanique et Bioingénierie (BMBI) ; Université de Technologie de Compiègne (UTC)-Centre National de la Recherche Scientifique (CNRS)
creator El Tatar, Aziz
date 2013-03-29T00:00:00
harvest_object_id 758ffc9c-1281-4c88-9c2d-8a792ba5a069
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2026-04-27T00:00:00
set_spec type:THESE