CSI-EPT: A novel contrast source approach to MRI based electric properties tomography and patient-specific SAR

Balidemaj, Edmond; Trinks, Johan; Berg, Cornelis A.T. van den; Nederveen, A. J.; Lier, A. L. van; Stalpers, Lukas J.A.; Crezee, J.; Remis, Rob

doi:10.1109/iceaa.2013.6632328

Cited by 5 publications

(3 citation statements)

References 8 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Boundary errors regarding the Laplacian may be reduced by considering backward and forward differentiation or by adapting the kernel-size near boundaries. A recent study (39) has introduced a new approach based on Contrast Source Inversion method. This approach is free of assumptions regarding the piece-wise regions and is based on integral representations of the EM field, avoiding derivatives of B þ 1 fields.…”

Section: Discussionmentioning

confidence: 99%

Feasibility of Electric Property Tomography of pelvic tumors at 3T

Balidemaj

Lier

Crezee

et al. 2014

Magnetic Resonance in Med

View full text Add to dashboard Cite

This study demonstrates the feasibility of electric property tomography to measure quantitatively the conductivity of centrally located tumors in a pelvic-sized phantom and human pelvis with a standard magnetic resonance (MR) system and MR sequences. A good quantitative agreement was found between the reconstructed σ-values and probe measurements for a wide range of σ-values and for off-axis located spherical compartment. As most pelvic tumors are located in the central region of the pelvis, these results can be exploited in hyperthermia treatment planning systems.

show abstract

Section: Discussionmentioning

confidence: 99%

Feasibility of Electric Property Tomography of pelvic tumors at 3T

Balidemaj

Lier

Crezee

et al. 2014

Magnetic Resonance in Med

View full text Add to dashboard Cite

show abstract

“…MR-Electrical Properties Tomography (EPT) falls in this sense in a completely new category (1). With EPT it is possible to reconstruct electrical properties contrast images at Larmor frequency (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12), useful for clinical diagnosis (13,14) and radiofrequency (RF) patient safety (15)(16)(17)(18). Instead, at low frequencies (LF, 100 Hz -1 kHz), where other methods like transcranial magnetic stimulations (TMS) and electrocorticography operate (19)(20)(21)(22)(23)(24)(25), knowledge on the relation between electrical conduction and tissue composition is limited.…”

Section: Introductionmentioning

confidence: 99%

A geometrical shift results in erroneous appearance of low frequency tissue eddy current induced phase maps

Mandija

Lier

Katscher

et al. 2015

Magnetic Resonance in Med

View full text Add to dashboard Cite

show abstract

“…Magnetic resonance electrical properties tomography (MR-EPT) is a non-invasive measurement technique that derives electrical properties (EPs: conductivity σ and permittivity ε) of human tissues from the distribution of transmit and receive radiofrequency (RF) fields of an MR system (Haacke et al, 1991;Katscher et al, 2009). The tissue EPs distribution is not only important for estimation of the specific absorption rate (Balidemaj et al, 2017;Katscher et al, 2009;Voigt et al, 2012;Zhang et al, 2013), but clinical studies have shown its potential as a biomarker in oncology (Balidemaj et al, 2015a(Balidemaj et al, , 2016Kim et al, 2016;Lazebnik et al, 2007;Lee et al, 2022;Mori et al, 2019;Shin et al, 2015;Suh et al, 2021;Tha et al, 2014Tha et al, , 2018.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven electrical conductivity brain imaging using 3 T MRI

Jung

Mandija

Cui

et al. 2023

Human Brain Mapping

View full text Add to dashboard Cite

Magnetic resonance electrical properties tomography (MR‐EPT) is a non‐invasive measurement technique that derives the electrical properties (EPs, e.g., conductivity or permittivity) of tissues in the radiofrequency range (64 MHz for 1.5 T and 128 MHz for 3 T MR systems). Clinical studies have shown the potential of tissue conductivity as a biomarker. To date, model‐based conductivity reconstructions rely on numerical assumptions and approximations, leading to inaccuracies in the reconstructed maps. To address such limitations, we propose an artificial neural network (ANN)‐based non‐linear conductivity estimator trained on simulated data for conductivity brain imaging. Network training was performed on 201 synthesized T2‐weighted spin‐echo (SE) data obtained from the finite‐difference time‐domain (FDTD) electromagnetic (EM) simulation. The dataset was composed of an approximated T2‐w SE magnitude and transceive phase information. The proposed method was tested three in‐silico and in‐vivo on two volunteers and three patients' data. For comparison purposes, various conventional phase‐based EPT reconstruction methods were used that ignore magnitude information, such as Savitzky–Golay kernel combined with Gaussian filter (S‐G Kernel), phase‐based convection‐reaction EPT (cr‐EPT), magnitude‐weighted polynomial‐fitting phase‐based EPT (Poly‐Fit), and integral‐based phase‐based EPT (Integral‐based). From the in‐silico experiments, quantitative analysis showed that the proposed method provides more accurate and improved quality (e.g., high structural preservation) conductivity maps compared to conventional reconstruction methods. Representatively, in the healthy brain in‐silico phantom experiment, the proposed method yielded mean conductivity values of 1.97 ± 0.20 S/m for CSF, 0.33 ± 0.04 S/m for WM, and 0.52 ± 0.08 S/m for GM, which were closer to the ground‐truth conductivity (2.00, 0.30, 0.50 S/m) than the integral‐based method (2.56 ± 2.31, 0.39 ± 0.12, 0.68 ± 0.33 S/m). In‐vivo ANN‐based conductivity reconstructions were also of improved quality compared to conventional reconstructions and demonstrated network generalizability and robustness to in‐vivo data and pathologies. The reported in‐vivo brain conductivity values were in agreement with literatures. In addition, the proposed method was observed for various SNR levels (SNR levels = 10, 20, 40, and 58) and repeatability conditions (the eight acquisitions with the number of signal averages = 1). The preliminary investigations on brain tumor patient datasets suggest that the network trained on simulated dataset can generalize to unforeseen in‐vivo pathologies, thus demonstrating its potential for clinical applications.

show abstract

CSI-EPT: A novel contrast source approach to MRI based electric properties tomography and patient-specific SAR

Abstract: -In this paper, we present a novel method (Contrast Source Inversion -Electric Properties Tomography or CSI-EPT) to dielectric imaging of biological tissue using so-called B

Cited by 5 publications

References 8 publications

Feasibility of Electric Property Tomography of pelvic tumors at 3T

Feasibility of Electric Property Tomography of pelvic tumors at 3T

A geometrical shift results in erroneous appearance of low frequency tissue eddy current induced phase maps

Data‐driven electrical conductivity brain imaging using 3 T MRI

Contact Info

Product

Resources

About