First-Order Induced Current Density Imaging and Electrical Properties Tomography in MRI

Journal Article (2018)
Author(s)

P.S. Fuchs (TU Delft - Signal Processing Systems)

Stefano Mandija (University Medical Center Utrecht)

P.R.S. Stijnman (University Medical Center Utrecht)

Wyger M. Brink (Leiden University Medical Center)

C.A.T. van den Berg (University Medical Center Utrecht)

RF Remis (TU Delft - Signal Processing Systems)

Research Group
Signal Processing Systems
Copyright
© 2018 P.S. Fuchs, Stefano Mandija, Peter R.S. Stijnman, Wyger M. Brink, Cornelis A.T. van den Berg, R.F. Remis
DOI related publication
https://doi.org/10.1109/TCI.2018.2873407
More Info
expand_more
Publication Year
2018
Language
English
Copyright
© 2018 P.S. Fuchs, Stefano Mandija, Peter R.S. Stijnman, Wyger M. Brink, Cornelis A.T. van den Berg, R.F. Remis
Related content
Research Group
Signal Processing Systems
Bibliographical Note
Accepted author manuscript@en
Issue number
4
Volume number
4
Pages (from-to)
624-631
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

In this paper, we present an efficient dedicated electrical properties tomography (EPT) algorithm (called first-order current density EPT ) that exploits the particular radio frequency field structure, which is present in the midplane of a birdcage coil, to reconstruct conductivity and permittivity maps in this plane from B ^ + 1 data. The algorithm consists of a current density and an electrical properties step. In the current density reconstruction step, the induced currents in the midplane are determined by acting with a specific first-order differentiation operator on the B ^ + 1 data. In the electrical properties step, we first determine the electric field strength by solving a particular integral equation, and subsequently determine conductivity and permittivity maps from the constitutive relations. The performance of the algorithm is illustrated by presenting reconstructions of a human brain model based on simulated (noise corrupted) data and of a known phantom model based on experimental data. The method manages to reconstruct conductivity profiles without model related boundary artifacts and is also more robust to noise because only first-order differencing of the data is required as opposed to second-order data differencing in Helmholtz-based approaches. Moreover, reconstructions can be performed in less than a second, allowing for essentially real-time electrical properties mapping.

Files

FoCI_EPT.pdf
(pdf | 1.94 Mb)
License info not available