Direct detection of neural activity in vitro using magnetic resonance electrical impedance tomography (MREIT)

Sadleir, Rosalind; Fu, Fanrui; Falgas, Corey; Holland, Stephen A.; Boggess, May; Grant, Samuel C.; Woo, Eung Je

doi:10.1016/j.neuroimage.2017.08.004

Cited by 14 publications

(22 citation statements)

References 40 publications

(63 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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]. In the energy and electricity field, the topology and breakpoints of the grounding grid can be detected by MDEIT [12].…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Magnetic resonance electrical impedance tomography (MREIT) was then proposed to image electrical conductivity with high and position-independent resolution [9]. MREIT is today on the way to becoming a clinically applicable technique [10][11][12]. In this technique, injected or induced currents are applied to the region of interest as in EIT, but in this case, magnetic flux densities generated by these currents are measured within the body using magnetic resonance imaging (MRI) techniques with equal sensitivity.…”

Section: Introductionmentioning

confidence: 99%

Iterative sensitivity matrix-based magnetic resonance conductivity tensor imaging

Değirmenci¹

2019

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Magnetic resonance conductivity tensor imaging (MRCTI) reconstructs high-resolution anisotropic conductivity images, which are proved to have critical importance in radio-oncological imaging as well as source localization fields. In the MRCTI technique, linearly independent current injections are applied to the region to be imaged and resulting magnetic flux densities are measured using magnetic resonance imaging techniques. In this study, a novel iterative reconstruction algorithm based on a sensitivity matrix approach is proposed and tested using both simulated and experimental measurements. Obtained results show that the proposed technique can reconstruct anisotropic conductivity images with high and position-independent spatial resolution in addition to decreased number of current injection strategies.

show abstract

“…All these experiments have been aimed at clinical translation and were primarily performed at main magnetic field strengths of 3T. Functional MREIT has also been demonstrated at ultra‐high fields (11.75T and 18.8T) in neural preparations of Aplysia Californica …”

Section: Introductionmentioning

confidence: 99%

Evaluation of magnetohydrodynamic effects in magnetic resonance electrical impedance tomography at ultra‐high magnetic fields

Minhas

Chauhan

et al. 2018

Magnetic Resonance in Med

Self Cite

View full text Add to dashboard Cite

Purpose Artifacts observed in experimental magnetic resonance electrical impedance tomography images were hypothesized to be because of magnetohydrodynamic (MHD) effects. Theory and Methods Simulations of MREIT acquisition in the presence of MHD and electrical current flow were performed to confirm findings. Laminar flow and (electrostatic) electrical conduction equations were bidirectionally coupled via Lorentz force equations, and finite element simulations were performed to predict flow velocity as a function of time. Gradient sequences used in spin‐echo and gradient echo acquisitions were used to calculate overall effects on MR phase images for different electrical current application or phase‐encoding directions. Results Calculated and experimental phase images agreed relatively well, both qualitatively and quantitatively, with some exceptions. Refocusing pulses in spin echo sequences did not appear to affect experimental phase images. Conclusion MHD effects were confirmed as the cause of observed experimental phase changes in MREIT images obtained at high fields. These findings may have implications for quantitative measurement of viscosity using MRI techniques. Methods developed here may be also important in studies of safety and in vivo artifacts observed in high field MRI systems.

show abstract

Direct detection of neural activity in vitro using magnetic resonance electrical impedance tomography (MREIT)

Cited by 14 publications

References 40 publications

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

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

Iterative sensitivity matrix-based magnetic resonance conductivity tensor imaging

Evaluation of magnetohydrodynamic effects in magnetic resonance electrical impedance tomography at ultra‐high magnetic fields

Contact Info

Product

Resources

About