The Impact of Anisotropy on the Accuracy of Conductivity Imaging: A Quantitative Validation Study

Elsaid, Nahla M. H.; Nachman, Adrian; Ma, Weijing; DeMonte, Tim P.; Joy, Michael

doi:10.1109/tmi.2016.2617873

Cited by 5 publications

(7 citation statements)

References 33 publications

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“…As noted above, the Fréchet derivative terms in ( 14) have three components. If ICDI data is in the form of Jm , then the first two components are used in (14). If ICDI data is in the form of Jm , then only the first component of those terms is used in (14).…”

Section: The Fréchet Derivativementioning

confidence: 99%

See 1 more Smart Citation

Numerical conductivity reconstruction from partial interior current density information in three dimensions

Yazdanian¹,

Knudsen²

2021

Inverse Problems

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This paper focuses on conductivity imaging of a three dimensional object using interior current density information (ICDI). Applications include the emerging hybrid tomographic methods known as magnetic resonance electrical impedance tomography and current density impedance imaging, which potentially have both high contrast and high resolution. For all possible forms of ICDI data, the Fréchet derivative of the map between conductivity and ICDI is derived. Then, an iterative reconstruction method is formulated based on the Newton scheme. The method is implemented numerically and its properties are investigated on simulated data obtained from two different phantoms. The method is also benchmarked against the J-substitution method. We systematically study the possibilities, challenges, shortcomings, and artifacts due the different forms of full and partial ICDI data and one or several boundary conditions. The results establish that at least two components of two non-parallel interior current densities are required to obtain good reconstructions; this is an important outcome for conductivity imaging methods which use only one component of the magnetic field. The results hold promise for the near real-time and high resolution conductivity reconstruction in practical applications.

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Section: The Fréchet Derivativementioning

confidence: 99%

“…Joy et al [12] and Lee [13] independently discovered a simple explicit reconstruction formula which uses two transversal J measurements and determines the σ up to a multiplicative constant. In [6,14], this method was validated by 3D phantoms and experimental data.…”

Section: Introductionmentioning

confidence: 99%

Numerical conductivity reconstruction from partial interior current density information in three dimensions

Yazdanian¹,

Knudsen²

2021

Inverse Problems

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“…To avoid potential errors caused by segmentation faults, several methods have been proposed to directly estimate the tissue dielectric properties based on anatomical images (e.g. Serralles et al 2020, Hampe et al 2019, Ropella & Noll 2017, Elsaid et al 2017, Tuch et al 2001. The water content calculated from T1-weighted MR scans is modeled using a monotonic function to estimate the conductivity of major brain tissues (Michel et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Learning-based estimation of dielectric properties and tissue density in head models for personalized radio-frequency dosimetry

2020

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Radio-frequency dosimetry is an important process in assessments for human exposure safety and for compliance of related products. Recently, computational human models generated from medical images have often been used for such assessment, especially to consider the inter-subject variability. However, a common procedure to develop personalized models is time consuming because it involves excessive segmentation of several components that represent different biological tissues, which is a major obstacle in the inter-subject variability assessment of radiation safety. Deep learning methods have been shown to be a powerful approach for pattern recognition and signal analysis. Convolutional neural networks with deep architecture are proven robust for feature extraction and image mapping in several biomedical applications. In this study, we develop a learning-based approach for fast and accurate estimation of the dielectric properties and density of tissues directly from magnetic resonance images in a single shot. The smooth distribution of the dielectric properties in head models, which is realized using a process without tissue segmentation, improves the smoothness of the specific absorption rate (SAR) distribution compared with that in the commonly used procedure. The estimated SAR distributions, as well as that averaged over 10-g of tissue in a cubic shape, are found to be highly consistent with those computed using the conventional methods that employ segmentation.

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“…The anisotropic conductivity distribution of body tissues could have a significant influence on the pathways of current flows, which have been studied in the inverse modeling of Electroencephalography/Magnetoencephalography . To our knowledge, the existing research that considers anisotropy in EIT has been concentrating on the theoretical uniqueness of the inverse problem and numerical forward modeling based on a conductivity tensor . Until now, previous work on EIT has not sufficiently investigated the influence of current frequency on the tissue anisotropy.…”

Section: Introductionmentioning

confidence: 99%

“…12 To our knowledge, the existing research that considers anisotropy in EIT has been concentrating on the theoretical uniqueness of the inverse problem [13][14][15][16] and numerical forward modeling [17][18][19] based on a conductivity tensor. 20 Until now, previous work on EIT has not sufficiently investigated the influence of current frequency on the tissue anisotropy. The goal of this work is to gain deeper insight into the frequency-dependent characteristics of tissue anisotropy and preliminarily validate the feasibility of multifrequency electrical impedance tomography (mfEIT) in reconstructing a frequencydependent anisotropic object.…”

Section: Introductionmentioning

confidence: 99%

Frequency‐dependent anisotropic modeling and analysis using mfEIT: A computer simulation study

Zhang

Potter

et al. 2018

Numer Methods Biomed Eng

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Electrical properties of human tissues are usually linked with structure of thin insulating membranes and thereby reflect physiological function of the tissues or organs. It is clinically important to characterize electrical properties of tissues in vivo. Electrical impedance tomography is a recently developed medical imaging technique, which has been exploited to characterize electrical properties (conductivity and permittivity) of human tissues by injecting currents and measuring the resulting voltages at boundary electrodes. The electrical characteristic of a majority of human tissues, such as bones, muscles, and brain white matter, exhibits an anisotropic property. The anisotropic phenomenon of human tissues is frequency dependent that vanishes at high frequencies. Previous electrical impedance tomography studies that aimed at the reconstruction of anisotropic subject tissues have been focused on the theoretical analysis of uniqueness up to a diffeomorphism or the establishment of an accurate forward model by using an anisotropic conductivity tensor. However, effects of the current frequency on the accuracy of the reconstructions of anisotropic subjects remain poorly studied. The goal of this study is to examine the feasibility of multifrequency electrical impedance tomography by using it in a simulation study to recover the frequency-dependent anisotropic properties of a phantom subject composed of alternating insulating and conductive layers. The anisotropic properties of the subject were analyzed by an effective admittivity tensor, and the responses of the current flow pathways and voltages were investigated at various applied current frequencies in the forward model. The linear reconstruction was performed following the sensitivity matrix approach at multiple frequencies. Simulation results achieved at various frequencies revealed that the anisotropy of the model was effectively reconstructed at low frequencies and disappeared at high frequencies, from which we validated the feasibility of multifrequency electrical impedance tomography method in reconstructing the anisotropic directions of the considered object.

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The Impact of Anisotropy on the Accuracy of Conductivity Imaging: A Quantitative Validation Study

Cited by 5 publications

References 33 publications

Numerical conductivity reconstruction from partial interior current density information in three dimensions

Numerical conductivity reconstruction from partial interior current density information in three dimensions

Learning-based estimation of dielectric properties and tissue density in head models for personalized radio-frequency dosimetry

Frequency‐dependent anisotropic modeling and analysis using mfEIT: A computer simulation study

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