Magnetic-Resonance-Based Electrical Property Mapping Using Global Maxwell Tomography With an 8-Channel Head Coil at 7 Tesla: A Simulation Study

Giannakopoulos, Ilias I.; Serralles, Jose E. C.; Daniel, Luca; Sodickson, Daniel K.; Polimeridis, Athanasios G.; White, Jacob K.; Lattanzi, Riccardo

doi:10.1109/tbme.2020.2991399

Cited by 15 publications

(12 citation statements)

References 44 publications

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“…Their development is similar to that of the well-known microwave imaging techniques [ 19 , 20 , 21 ]. The similarity appears clear, especially with the EPT methods based on optimization theory [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. Nonetheless, knowledge of the value of

at a large number of points within the inspected body suggests that EPT can achieve a larger effective resolution than traditional microwave imaging [ 19 , 25 ].…”

Section: Introductionmentioning

confidence: 89%

Repeatability and Reproducibility Uncertainty in Magnetic Resonance-Based Electric Properties Tomography of a Homogeneous Phantom

et al. 2023

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Uncertainty assessment is a fundamental step in quantitative magnetic resonance imaging because it makes comparable, in a strict metrological sense, the results of different scans, for example during a longitudinal study. Magnetic resonance-based electric properties tomography (EPT) is a quantitative imaging technique that retrieves, non-invasively, a map of the electric properties inside a human body. Although EPT has been used in some early clinical studies, a rigorous experimental assessment of the associated uncertainty has not yet been performed. This paper aims at evaluating the repeatability and reproducibility uncertainties in phase-based Helmholtz-EPT applied on homogeneous phantom data acquired with a clinical 3 T scanner. The law of propagation of uncertainty is used to evaluate the uncertainty in the estimated conductivity values starting from the uncertainty in the acquired scans, which is quantified through a robust James–Stein shrinkage estimator to deal with the dimensionality of the problem. Repeatable errors are detected in the estimated conductivity maps and are quantified for various values of the tunable parameters of the EPT implementation. The spatial dispersion of the estimated electric conductivity maps is found to be a good approximation of the reproducibility uncertainty, evaluated by changing the position of the phantom after each scan. The results underpin the use of the average conductivity (calculated by weighting the local conductivity values by their uncertainty and taking into account the spatial correlation) as an estimate of the conductivity of the homogeneous phantom.

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at a large number of points within the inspected body suggests that EPT can achieve a larger effective resolution than traditional microwave imaging [ 19 , 25 ].…”

Section: Introductionmentioning

confidence: 89%

Repeatability and Reproducibility Uncertainty in Magnetic Resonance-Based Electric Properties Tomography of a Homogeneous Phantom

et al. 2023

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“…Global Maxwell tomography (GMT) is a volumetric integral method that iteratively updates the tissue parameters based on improved estimations of the transmit field by solving a forward problem and minimizing an objective function [63][64][65]. GMT makes use of the identity [66]…”

Section: Global Maxwell Tomographymentioning

confidence: 99%

“…Even though a computational expensive inverse problem is removed, the method remains computationally expensive since the gradient updates typically require a large amount of iterations. The presented formulation still requires knowledge of the absolute transmit phase; however, this has been addressed by reformulating the objective function to only consider the magnitude of the transmit field or, in the case of a multi-element transmit system, by reformulating it in terms of magnitude and relative phases [64,65].…”

Section: Global Maxwell Tomographymentioning

confidence: 99%

Electrical Properties Tomography: A Methodological Review

et al. 2021

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Electrical properties tomography (EPT) is an imaging method that uses a magnetic resonance (MR) system to non-invasively determine the spatial distribution of the conductivity and permittivity of the imaged object. This manuscript starts by providing clear definitions about the data required for, and acquired in, EPT, followed by comprehensively formulating the physical equations underlying a large number of analytical EPT techniques. This thorough mathematical overview of EPT harmonizes several EPT techniques in a single type of formulation and gives insight into how they act on the data and what their data requirements are. Furthermore, the review describes machine learning-based algorithms. Matlab code of several differential and iterative integral methods is available upon request.

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“…We first demonstrated the proposed compression method for an 8-ports close-fitting head coil, previously designed for GMT [16], which we loaded with the "Billie" realistic head model from the virtual family population [45] (Fig. 7).…”

Section: ) Head Coil Experimentsmentioning

confidence: 99%

“…In the first experimental demonstration of GMT with a uniform phantom, constant incident fields were used for all iterations [15]. More recently, it was shown in simulation that GMT could accurately reconstruct brain electrical properties at 7 T using a tailored RF coil array [16]. However, in order to confirm this with in-vivo experiments, the currents on the coil conductors cannot be simply considered constant as in the initial experiment with a uniform phantom.…”

mentioning

confidence: 99%

Compression of volume-surface integral equation matrices via Tucker decomposition for magnetic resonance applications

Giannakopoulos,

Guryev,

Serralles

et al. 2021

Preprint

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In this work, we propose a method for the compression of the coupling matrix in volume-surface integral equation (VSIE) formulations. VSIE methods are used for electromagnetic analysis in magnetic resonance imaging (MRI) applications, for which the coupling matrix models the interactions between the coil and the body. We showed that these effects can be represented as independent interactions between remote elements in 3D tensor formats, and subsequently decomposed with the Tucker model. Our method can work in tandem with the adaptive cross approximation technique to provide fast solutions of VSIE problems. We demonstrated that our compression approaches can enable the use of VSIE matrices of prohibitive memory requirements, by allowing the effective use of modern graphical processing units (GPUs) to accelerate the arising matrix-vector products. This is critical to enable numerical MRI simulations at clinical voxel resolutions in a feasible computation time. In this paper, we demonstrate that the VSIE matrix-vector products needed to calculate the electromagnetic field produced by an MRI coil inside a numerical body model with 1 mm 3 voxel resolution, could be performed in ∼ 33 seconds in a GPU, after compressing the associated coupling matrix from ∼ 80 TB to ∼ 43 MB.

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Magnetic-Resonance-Based Electrical Property Mapping Using Global Maxwell Tomography With an 8-Channel Head Coil at 7 Tesla: A Simulation Study

Cited by 15 publications

References 44 publications

Repeatability and Reproducibility Uncertainty in Magnetic Resonance-Based Electric Properties Tomography of a Homogeneous Phantom

Repeatability and Reproducibility Uncertainty in Magnetic Resonance-Based Electric Properties Tomography of a Homogeneous Phantom

Electrical Properties Tomography: A Methodological Review

Compression of volume-surface integral equation matrices via Tucker decomposition for magnetic resonance applications

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