English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Magnetic resonance electrical property tomography is a recent medical imagingmodality for visualizing the electrical tissue properties of the human bodyusing radio-frequency magnetic fields. It uses the fact that in magneticresonance imaging systems the eddy currents induced by the radio-frequencymagnetic fields reflect the conductivity ($\sigma$) and permittivity($\epsilon$) distributions inside the tissues through Maxwell's equations. Thecorresponding inverse problem consists of reconstructing the admittivitydistribution ($\gamma=\sigma+i\omega\epsilon$) at the Larmor frequency($\omega/2\pi=$128 MHz for a 3 tesla MRI machine) from the positive circularlypolarized component of the magnetic field ${\bf H}=(H_x,H_y,H_z)$. Previousmethods are usually based on an assumption of local homogeneity($\nabla\gamma\approx 0$) which simplifies the governing equation. However,previous methods that include the assumption of homogeneity are prone toartifacts in the region where $\gamma$ varies. Hence, recent work has sought areconstruction method that does not assume local-homogeneity. This paperpresents a new magnetic resonance electrical property tomography reconstructionmethod which does not require any local homogeneity assumption on $\gamma$. Wefind that $\gamma$ is a solution of a semi-elliptic partial differentialequation with its coefficients depending only on the measured data $H^+$, whichenable us to compute a blurred version of $\gamma$. To improve the resolutionof the reconstructed image, we developed a new optimization algorithm thatminimizes the mismatch between the data and the model data as a highlynonlinear function of $\gamma$. Numerical simulations are presented toillustrate the potential of the proposed reconstruction method.

Magnetic resonance-based reconstruction method of conductivity and permittivity distributions at the Larmor frequency