TY - JOUR
T1 - First-Order Induced Current Density Imaging and Electrical Properties Tomography in MRI
AU - Fuchs, Patrick S.
AU - Mandija, Stefano
AU - Stijnman, Peter R.S.
AU - Brink, Wyger M.
AU - van den Berg, Cornelis A.T.
AU - Remis, Rob F.
N1 - Accepted author manuscript
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Magnetic resonance imaging
KW - electrical properties tomography
KW - dielectric tissue properties
KW - $\hat{B}_1^+$field
U2 - 10.1109/TCI.2018.2873407
DO - 10.1109/TCI.2018.2873407
M3 - Article
SN - 2333-9403
VL - 4
SP - 624
EP - 631
JO - IEEE Transactions on Computational Imaging
JF - IEEE Transactions on Computational Imaging
IS - 4
ER -