Magnetic detection electrical impedance tomography with total variation regularization

Hao, Liling; Li, Gang; Xu, Lisheng

doi:10.3233/bme-141104

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…The reconstruction algorithms considered for the tank study were chosen based on their applicability to fast neural imaging. Only one-step regularisation methods were considered because they are faster to implement than methods such as total variation regularisation (Hao et al 2014). The effect of NBC was studied because it has never been applied to MDEIT before.…”

Section: Magnetometer Sensitivitymentioning

confidence: 99%

Non-invasive imaging of neural activity with magnetic detection electrical impedance tomography (MDEIT): a modelling study

Mason,

Aristovich,

Holder

2023

Physiol. Meas.

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Objectives. (1) Develop a computational pipeline for three-dimensionalfast neural magnetic detection electrical impedance tomography (MDEIT), (2)determine whether constant current or constant voltage is preferable for MDEIT,(3) perform reconstructions of simulated neural activity in a human headmodel with realistic noise and compare MDEIT to EIT and (4) perform atwo-dimensional study in a saline tank for MDEIT with optically pumpedmagnetometers (OPMs) and compare reconstruction algorithms. Approach.Forward modelling and image reconstruction were performed with a realisticmodel of a human head in three dimensions and at three noise levels forfour perturbations representing neural activity. Images were compared usingthe error in the position and size of the reconstructed perturbations. Two-dimensional MDEIT was performed in a saline tank with a resistive perturbationand one OPM. Six reconstruction algorithms were compared using the errorin the position and size of the reconstructed perturbations. Main Results.A computational pipeline was developed in COMSOL Multiphysics, reducingthe Jacobian calculation time from months to days. MDEIT reconstructedimages with a lower reconstruction error than EIT with a mean difference of7.0%, 5.5% and 11% for three noise cases representing current noise, reducedcurrent source noise and reduced current source and magnetometer noise. Arank analysis concluded that the MDEIT Jacobian was less rank-deficient thanthe EIT Jacobian. Reconstructions of a phantom in a saline tank had a bestreconstruction error of 13%, achieved using 0th order Tikhonov regularisationwith simulated noise-based correction. Significance. This study demonstratedthat three-dimensional MDEIT for neural imaging is feasible and that MDEITreconstructed superior images to EIT, which can be explained by the lesser rankdeficiency of the MDEIT Jacobian. Reconstructions of a perturbation in a salinetank demonstrated a proof of principle for two-dimensional MDEIT with OPMsand identified the best reconstruction algorithm.

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Section: Magnetometer Sensitivitymentioning

confidence: 99%

Non-invasive imaging of neural activity with magnetic detection electrical impedance tomography (MDEIT): a modelling study

Mason,

Aristovich,

Holder

2023

Physiol. Meas.

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“…where L is a loss function. The Gradient Descent method is used to update the weight matrices and the bias vectors according to (11)- (13).…”

Section: Sae Algorithm For Inverse Problemmentioning

confidence: 99%

“…In the energy and electricity field, the topology and breakpoints of the grounding grid can be detected by MDEIT [12]. However, although the reconstruction can be improved a lot compared with traditional EIT, there is still problems pending further development in MDEIT such as the spatial and temporal image resolution [13].…”

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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A convolutional neural network algorithm for breast tumor detection with magnetic detection electrical impedance tomography

Chen

Zhao

et al. 2021

Review of Scientific Instruments

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Breast cancer is a malignant tumor disease for which early detection, diagnosis, and treatment are of paramount significance in prolonging the life of patients. Magnetic Detection Electrical Impedance Tomography (MDEIT) based on the Convolutional Neural Network (CNN), which aims to realize non-invasive, high resolution detection of breast tumors, is proposed. First, the MDEIT forward problem of the coronal and horizontal planes of the breast was simulated and solved using the Finite Element Method to obtain sample datasets of different lesions. Then, the CNN was built and trained to predict the conductivity distribution in different orientations of the breast model. Finally, noise and phantom experiments were performed in order to assess the anti-noise performance of the CNN algorithm and its feasibility of detecting breast tumors in practical applications. The simulation results showed that the reconstruction relative error with the CNN algorithm can be reduced to 10%, in comparison with the truncated singular value decomposition algorithm and back propagation algorithm. The CNN algorithm had better stability in the anti-noise performance test. When the noise of 60 dB was added, the shape of the breast tumor could still be restored by the CNN algorithm. The phantom experimental results showed that through the CNN based reconstruction algorithm, the reconstruction conductivity distribution image was legible and the position of the breast tumor could be determined. It is reasonable to conclude that the MDEIT reconstruction method proposed in this study has practical importance for the early and non-invasive detection of breast tumors.

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Magnetic detection electrical impedance tomography with total variation regularization

Cited by 3 publications

References 11 publications

Non-invasive imaging of neural activity with magnetic detection electrical impedance tomography (MDEIT): a modelling study

Non-invasive imaging of neural activity with magnetic detection electrical impedance tomography (MDEIT): a modelling study

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

A convolutional neural network algorithm for breast tumor detection with magnetic detection electrical impedance tomography

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