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https://yscholarhub.yonsei.ac.kr/handle/2021.sw.yonsei/4206
2024-02-22T21:33:40ZMagnetic resonance-based reconstruction method of conductivity and permittivity distributions at the Larmor frequency
https://yscholarhub.yonsei.ac.kr/handle/2021.sw.yonsei/16601
Title: Magnetic resonance-based reconstruction method of conductivity and permittivity distributions at the Larmor frequency
Authors: Ammari, Habib; Kwon, Hyeuknam; Lee, Yoonseop; Kang, Kyungkeun; Seo, Jin Keun
Abstract: Magnetic resonance electric properties tomography (MREPT) is a recent medical imaging modality for visualizing the electrical tissue properties of the human body using radio-frequency magnetic fields. It uses the fact that in magnetic resonance imaging (MRI) systems the eddy currents induced by the radio-frequency magnetic fields reflect the conductivity (sigma) and permittivity (epsilon) distributions inside the tissues through Maxwell's equations. The corresponding inverse problem consists of reconstructing the admittivity distribution (gamma = sigma + i omega epsilon) at the Larmor frequency (omega/2 pi = 128 MHz for a 3 Tesla MRI machine) from the positive circularly polarized component of the magnetic field H = (H-x, H-y, H-z). Previous methods are usually based on an assumption of local homogeneity (del gamma approximate to 0) which simplifies the governing equation. However, previous methods that include the assumption of homogeneity are prone to artifacts in the region where gamma varies. Hence, recent work has sought a reconstruction method that does not assume local-homogeneity. This paper presents a new MREPT reconstruction method which does not require any local homogeneity assumption on gamma. We find that gamma is a solution of a semi-elliptic partial differential equation with its coefficients depending only on the measured data H+ := (H-x + iH(y))/2, which enable us to compute a blurred version of gamma. To improve the resolution of the reconstructed image, we developed a new optimization algorithm that minimizes the mismatch between the data and the model data as a highly nonlinear function of gamma. Numerical simulations are presented to illustrate the potential of the proposed reconstruction method.2015-10-01T00:00:00Z