A New Electrode Mode for Magnetic Detection Electrical Impedance Tomography: Computer Simulation Study

Li, Gang; Hao, Liling; Chen, Ruijuan

doi:10.1109/tmag.2012.2200692

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…The MDEIT system consists of two aspects: forward problem and inverse problem. The forward problem computes the magnetic flux density B surrounding the object from the known conductivity distribution σ according to the boundary value problem and Biot-Savart law [5,6] ( ) ( )…”

Section: Magnetic Detection Electrical Impedance Tomographymentioning

confidence: 99%

Magnetic detection electrical impedance tomography with total variation regularization

Hao

2014

Bio-Medical Materials and Engineering

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Magnetic detection electrical impedance tomography (MDEIT) is an imaging modality that aims to reconstruct the cross-sectional conductivity distribution of a volume from the magnetic flux density surrounding an object. The MDEIT inverse problem is inherently ill-posed, necessitating the use of regularization. The most commonly used L 2 norm regularizations generate the minimum energy solution, which blurs the sharp variations of the reconstructed image. Consequently, this paper presents the total variation (TV) regularization to preserve discontinuities and piecewise constancy of the MDEIT reconstructed image. The primal dual-interior point method (PD-IPM) is employed for minimizing the TV penalty in this paper. The proposed method is validated by MDEIT simulated data. In comparison with the performance of L 2 norm regularization, the results show that TV regularized algorithm produces sharper images and has better robustness to noise. The TV regularized algorithm preserves local smoothness and piecewise constancy, leading to improvements in the localization of the reconstructed conductivity images in MDEIT.

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Section: Magnetic Detection Electrical Impedance Tomographymentioning

confidence: 99%

Magnetic detection electrical impedance tomography with total variation regularization

Hao

2014

Bio-Medical Materials and Engineering

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“…Considering that the measurement of magnetic flux density does not require surface contact, the sensor fixing problems of tradition Electrical impedance tomography (EIT) [7] is eliminated in MDEIT and a great number of measurements can be recorded with precise detector positions [8]. Therefore, compared to the traditional EIT, MDEIT has the advantages of non-contact sensing, less electrodes, and extensive application domains [9], [10]. In the medical engineering, we can use MDEIT to monitor the physiological state and pathological information of the human body by reconstructing the electrical property information using pairs of excitation electrodes and a number of magnetic induction coils placed around the imaging target [11].…”

Section: Introductionmentioning

confidence: 99%

A Stacked Autoencoder Neural Network Algorithm for Breast Cancer Diagnosis With Magnetic Detection Electrical Impedance Tomography

Chen

et al. 2020

IEEE Access

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Magnetic detection electrical impedance tomography (MDEIT) is a novel imaging technique that aims to reconstruct the conductivity distribution with electrical current injection and the external magnetic flux density measurement by magnetic sensors. Aiming at improving the resolution and accuracy of MDEIT and providing an efficient imaging method for breast cancer diagnosis, a new algorithm based on stacked auto-encoder (SAE) neural network is proposed. Both numerical simulation and phantom experiments are done to verify its feasibility. In the numerical simulation, an amount of sample data with different conductivity distribution are calculated. Then a neural network model is established and trained by training these samples. Finally, the conductivity distribution of an imaging target with the anomaly location can be reconstructed by the network model. The reconstruction result of the SAE algorithm is compared with the reconstruction results of the traditional sensitivity matrix (SM) algorithm and the back propagation (BP) neural network algorithm. Under the noise of 30dB, the relative errors of BP algorithm, SM algorithm and SAE algorithm are 137.19%, 24.90% and 15.28% respectively. Result shows by the SAE algorithm, the location of anomalies is reconstructed more accurately, the conductivity value is more closely to the real one and the anti-noise performance is more robust. At last, a breast phantom experiment by self-made platforms is completed to verify the application feasibility of the new algorithm. The relative reconstruction error of conductivity by proposed SAE algorithm can be reduced to 14.56%. The results show that by SAE algorithm, MDEIT can be a promising approach in clinical diagnosis of breast cancer, and it also provide more potential application prospect for the extensive application of MDEIT. INDEX TERMS Breast cancer diagnosis, inverse problem, magnetic detection electrical impedance tomography, stacked auto-encoder.

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Development and evaluation of a slurry density measurement system based on Applied Current-Magnetic Induction Tomography (AC-MIT)

Taghizadeh-Tameh¹,

Mousazadeh²,

Rafiee³

et al. 2023

Flow Measurement and Instrumentation

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A New Electrode Mode for Magnetic Detection Electrical Impedance Tomography: Computer Simulation Study

Cited by 6 publications

References 32 publications

Magnetic detection electrical impedance tomography with total variation regularization

Magnetic detection electrical impedance tomography with total variation regularization

A Stacked Autoencoder Neural Network Algorithm for Breast Cancer Diagnosis With Magnetic Detection Electrical Impedance Tomography

Development and evaluation of a slurry density measurement system based on Applied Current-Magnetic Induction Tomography (AC-MIT)

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