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Accueil › Scientific publications › A Semi-Analytical Model Of High Permittivity Dielectric Ring Resonators for Magnetic Resonance Imaging

A Semi-Analytical Model Of High Permittivity Dielectric Ring Resonators for Magnetic Resonance Imaging

avril 14, 2020

Marine A.C. Moussu, Redha Abdeddaim, Marc Dubois, Elodie Georget, Andrew G. Webb, Elizaveta Nenasheva, Pavel Belov, Stanislav Glybovski, Luisa Ciobanu, and Stefan Enoch, “A Semi-Analytical Model Of High Permittivity Dielectric Ring Resonators for Magnetic Resonance Imaging,” in IEEE Transactions on Antennas and Propagation.

doi: 10.1109/TAP.2020.2980771

Abstract : Magnetic Resonance Imaging (MRI) is an imaging technique exploiting the magnetic resonance of specific nuclear spins, like protons. In this paper, MR probes based on dielectric ring resonators are investigated from a theoretical approach. We take advantage of the high-permittivity and low-losses properties of the ceramic material used for manufacturing these probes for microscopy applications. Magnetic Resonance Microscopy (MRM) aims at imaging tiny samples with a sufficient resolution to distinguish small details. In this framework, compact resonators, called volume probes, contain the investigated sample and are used for both signal transmission and reception. The new developed semi-analytical model enables estimation of the frequency of the first transverse electric mode of a cylindrical resonator. It also provides a method to compute the corresponding magnetic field distribution, the dielectric losses contributions from the probe and the sample, and Signal-to-Noise Ratio (SNR). The proposed approach aims at providing design guidelines for dielectric probes.

Keywords :  Dielectric Resonators; Magnetic Resonance Microscopy; Signal-to-Noise Ratio; Transverse Electric Mode

URL : https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9062525&isnumber=4907023

Partners : ITMO, AMU (Institut Fresnel & CRMBM), CEA Neurospin

avril 14, 2020

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This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 736937