Electrical properties tomography: Available contrast and reconstruction capabilities

Hancu, Ileana; Liu, Jiaen; Hua, Yaping; Lee, Seung Kyun

doi:10.1002/mrm.27453

Cited by 21 publications

(17 citation statements)

References 46 publications

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“…S1). 26 To reduce image blurring caused by the kernel, weighted polynomial fitting technique was applied, in which a weighting factor was determined from the magnitude of the T2WI. 24 The weighting factor, w(r), inside the fitting kernel, Ω, was determined at each pixel r 0 , by the following equation: w(r) = G δ (jS Ω (r) À S(r 0 )j), where G δ is a Gaussian distribution with standard deviation δ, and S Ω refers to the magnitude of the pixel inside the fitting kernel normalized by the maximum intensity of the image.…”

Section: Conductivity Mappingmentioning

confidence: 99%

Noncontrast‐Enhanced MR‐Based Conductivity Imaging for Breast Cancer Detection and Lesion Differentiation

Suh

Kim

et al. 2021

Magnetic Resonance Imaging

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Background: There is increasing interest in noncontrast-enhanced MRI due to safety concerns for gadolinium contrast agents. Purpose: To investigate the clinical feasibility of MR-based conductivity imaging for breast cancer detection and lesion differentiation. Study Type: Prospective. Subjects: One hundred and ten women, with 112 known cancers and 17 benign lesions (biopsy-proven), scheduled for preoperative MRI. Field Strength/Sequence: Non-fat-suppressed T2-weighted turbo spin-echo sequence (T2WI), dynamic contrast-enhanced MRI and diffusion-weighted imaging (DWI) at 3T. Assessment: Cancer detectability on each imaging modality was qualitatively evaluated on a per-breast basis: the conductivity maps derived from T2WI were independently reviewed by three radiologists (R1-R3). T2WI, DWI, and preoperative digital mammography were independently reviewed by three other radiologists (R4-R6). Conductivity and apparent diffusion coefficient (ADC) measurements (mean, minimum, and maximum) were performed for 112 cancers and 17 benign lesions independently by two radiologists (R1 and R2). Tumor size was measured from surgical specimens. Statistical Tests: Cancer detection rates were compared using generalized estimating equations. Multivariable logistic regression analysis was performed to identify factors associated with cancer detectability. Discriminating ability of conductivity and ADC was evaluated by using the areas under the receiver operating characteristic curve (AUC). Results: Conductivity imaging showed lower cancer detection rates (20%-32%) compared to T2WI (62%-71%), DWI (85%-90%), and mammography (79%-88%) (all P < 0.05). Fatty breast on MRI (odds ratio = 11.8, P < 0.05) and invasive tumor size (odds ratio = 1.7, P < 0.05) were associated with cancer detectability of conductivity imaging. The maximum conductivity showed comparable ability to the mean ADC in discriminating between cancers and benign lesions (AUC = 0.

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Section: Conductivity Mappingmentioning

confidence: 99%

Noncontrast‐Enhanced MR‐Based Conductivity Imaging for Breast Cancer Detection and Lesion Differentiation

Suh

Kim

et al. 2021

Magnetic Resonance Imaging

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“…Yet, as highlighted in three recently published works (Katscher and van den Berg 2017;Hancu et al 2018;McCann et al 2019), the number of studies showing in vivo RF conductivity reconstructions is limited, while permittivity reconstructions are not feasible. In particular, for brain tissues, the number of test subjects reported in these studies is very small (Voigt et al 2011;Zhang et al 2013a;Michel et al 2016;Tha et al 2018), and in vivo studies on groups of healthy subjects studies are currently missing.…”

Section: Supplementary Informationmentioning

confidence: 99%

Brain Tissue Conductivity Measurements with MR-Electrical Properties Tomography: An In Vivo Study

et al. 2020

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First in vivo brain conductivity reconstructions using Helmholtz MR-Electrical Properties Tomography (MR-EPT) have been published. However, a large variation in the reconstructed conductivity values is reported and these values differ from ex vivo conductivity measurements. Given this lack of agreement, we performed an in vivo study on eight healthy subjects to provide reference in vivo brain conductivity values. MR-EPT reconstructions were performed at 3 T for eight healthy subjects. Mean conductivity and standard deviation values in the white matter, gray matter and cerebrospinal fluid (σWM, σGM, and σCSF) were computed for each subject before and after erosion of regions at tissue boundaries, which are affected by typical MR-EPT reconstruction errors. The obtained values were compared to the reported ex vivo literature values. To benchmark the accuracy of in vivo conductivity reconstructions, the same pipeline was applied to simulated data, which allow knowledge of ground truth conductivity. Provided sufficient boundary erosion, the in vivo σWM and σGM values obtained in this study agree for the first time with literature values measured ex vivo. This could not be verified for the CSF due to its limited spatial extension. Conductivity reconstructions from simulated data verified conductivity reconstructions from in vivo data and demonstrated the importance of discarding voxels at tissue boundaries. The presented σWM and σGM values can therefore be used for comparison in future studies employing different MR-EPT techniques.

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“…Table 1 shows the estimated conductivity values in CSF, gray matter, and white matter regions (CSF: red spots, white matter: blue spots, gray matter: yellow spots in Fig 3(a) ). The known reference values of high-frequency conductivity are 1.65∼1.92 (CSF), 0.59∼0.63 (gray matter) and 0.30∼0.43 S/m (white matter) at 128 MHz [ 23 – 25 ]. The predicted conductivity values, , in CSF regions were slightly lower than σ H , while the conductivity values of in white matter regions were higher than σ H due to the adopted L 2 -regularization parameter to prevent overfitting in the deep learning process.…”

Section: Resultsmentioning

confidence: 99%

High-frequency conductivity at Larmor-frequency in human brain using moving local window multilayer perceptron neural network

et al. 2021

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Magnetic resonance electrical properties tomography (MREPT) aims to visualize the internal high-frequency conductivity distribution at Larmor frequency using the B1 transceive phase data. From the magnetic field perturbation by the electrical field associated with the radiofrequency (RF) magnetic field, the high-frequency conductivity and permittivity distributions inside the human brain have been reconstructed based on the Maxwell’s equation. Starting from the Maxwell’s equation, the complex permittivity can be described as a second order elliptic partial differential equation. The established reconstruction algorithms have focused on simplifying and/or regularizing the elliptic partial differential equation to reduce the noise artifact. Using the nonlinear relationship between the Maxwell’s equation, measured magnetic field, and conductivity distribution, we design a deep learning model to visualize the high-frequency conductivity in the brain, directly derived from measured magnetic flux density. The designed moving local window multi-layer perceptron (MLW-MLP) neural network by sliding local window consisting of neighboring voxels around each voxel predicts the high-frequency conductivity distribution in each local window. The designed MLW-MLP uses a family of multiple groups, consisting of the gradients and Laplacian of measured B1 phase data, as the input layer in a local window. The output layer of MLW-MLP returns the conductivity values in each local window. By taking a non-local mean filtering approach in the local window, we reconstruct a noise suppressed conductivity image while maintaining spatial resolution. To verify the proposed method, we used B1 phase datasets acquired from eight human subjects (five subjects for training procedure and three subjects for predicting the conductivity in the brain).

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Electrical properties tomography: Available contrast and reconstruction capabilities

Cited by 21 publications

References 46 publications

Noncontrast‐Enhanced MR‐Based Conductivity Imaging for Breast Cancer Detection and Lesion Differentiation

Noncontrast‐Enhanced MR‐Based Conductivity Imaging for Breast Cancer Detection and Lesion Differentiation

Brain Tissue Conductivity Measurements with MR-Electrical Properties Tomography: An In Vivo Study

High-frequency conductivity at Larmor-frequency in human brain using moving local window multilayer perceptron neural network

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