Electrical Tomography Research Articles

Geoelectric model of the exposure of basement rocks to the surface in the Uimon depression of Gorny Altay based on three-dimensional numerical modeling

In the western part of the Uimon depression of the Altai Mountains, during the 2023 field season, additional measurements were carried out using electrical tomography along three profiles to clarify the structure of the area with basement rocks exposed to the surface. During the study, the results of three-dimensional inversion were obtained taking into account new electrical tomography data, and a refined geoelectric model of the study area was constructed. To verify the resulting model, 2D and 3D numerical modeling was performed with the help of which the geometric dimensions and electrical resistivity of bedrock outcrops were justified. The influence of the daytime surface topography on geoelectric sections in mountainous regions with significant differences in elevation on the earth's surface has been shown. Determining the structure of the depressions is necessary to understand the stages of neotectonic formation of the Uimon depression and the entire region, as well as to assess the seismic hazard of the Altai Mountains. In the course of further research, it is possible to trace the object to a greater depth (~100 m) using a three-electrode electrical tomography installation or alternative equipment with an increased size of the measuring-generator cable.

Experiments and predictions for severe slugging of gas-liquid two-phase flows in a long-distance pipeline-riser system

Experiments concerning the flow regimes observed in gas-liquid two-phase flows in a long-distance pipeline-riser system were carried out and analyzed in this paper. The test loop with 0.075 m inner diameter consisted of a 314 m horizontal pipeline, followed by a 25 m downward-inclined pipe, and finally a 21.5 m riser pipe. An industrial-scale global flow regime map including 5 flow regimes was derived from differential pressure measurements in the riser and was used as a reference for improvements. Electrical capacitance volume tomography installed in the horizontal section was used to reconstruct the phase interface and calculate the average gas void fraction (GVF). An appropriate model for this system was selected by comparing the experimental GVF with different, widely used models. The occurrence of severe slugging flow (SS) can be predicted accurately using an improved model involving additional corrections that consider pipeline length, accurate GVF calculation, and gas density. This study supports the development of a suitable SS prediction model enabling safe design, operation, and control of practical systems.

Research on quantitative inversion characterization of high-definition electrical imaging logging in oil-based mud based on backpropagation neural network and multiple population genetic algorithm-Levenberg-Marquardt algorithm

Electrical imaging logging in oil-based mud (OBM) has been developed for some time and is gradually playing an important role in the description of deep carbonate and shale reservoirs. Quantitative characterization of reservoir rock parameters such as resistivity is one of the most innovative developments in this field. The development of this technology needs to address and resolve four core issues: a wide range of parameter variations, removal of mud-cake influence in low-resistivity formations, dielectric rollover in high-resistivity formations, and multifrequency dielectric dispersion effects. To address the aforementioned issues, the joint use of a backpropagation neural network (BPNN) and the multiple population genetic algorithm (MPGA)-Levenberg-Marquardt (LM) algorithm for high-resolution quantitative imaging is developed. First, the numerical simulation is used to calculate the well-logging response data under the influence of multiple parameters, thereby establishing a forward response database. Then, within the forward response database, the instrument response function is fitted using BPNN, to compress the data volume. Next, based on the fitted response function, an inversion method for three parameters, including reservoir rock resistivity, permittivity, and plate standoff, is established using the LM algorithm optimized with MPGA. The results indicate that the use of a three-layer BPNN enables rapid and accurate calculation of the electrical imaging logging response in OBM. The calculation of a single point only requires 0.1 ms with an accuracy of more than 99%. The MPGA-LM algorithm exhibits stronger stability and improved inversion accuracy, with a single point inversion time of only 2 ms, and contributes to the high-definition quantitative description of electrical imaging logging in OBM, which is important in characterizing formation structures, distinguishing formation fractures, etc.

All-electrical skyrmionic magnetic tunnel junction.

Topological whirls or 'textures' of spins such as magnetic skyrmions represent the smallest realizable emergent magnetic entities1-5. They hold considerable promise as robust, nanometre-scale, mobile bits for sustainable computing6-8. A longstanding roadblock to unleashing their potential is the absence of a device enabling deterministic electrical readout of individual spin textures9,10. Here we present the wafer-scale realization of a nanoscale chiral magnetic tunnel junction (MTJ) hosting a single, ambient skyrmion. Using a suite of electrical and multimodal imaging techniques, we show that the MTJ nucleates skyrmions of fixed polarity, whose large readout signal-20-70% relative to uniformly magnetized states-corresponds directly to skyrmion size. The MTJ exploits complementary nucleation mechanisms to stabilize distinctly sized skyrmions at zero field, thereby realizing three non-volatile electrical states. Crucially, it can electrically write and delete skyrmions to both uniform states with switching energies 1,000 times lower than the state of the art. Here, the applied voltage emulates a magnetic field and, in contrast to conventional MTJs, it reshapes both the energetics and kinetics of the switching transition, enabling deterministic bidirectional switching. Our stack platform enables large readout and efficient switching, and is compatible with lateral manipulation of skyrmionic bits, providing the much-anticipated backbone for all-electrical skyrmionic device architectures9,10. Its wafer-scale realizability provides a springboard to harness chiral spin textures for multibit memory and unconventional computing8,11.

Dynamic bubble tracking in fluidized beds via electrical capacitance volume tomography

This work sets forth the development of a novel, dynamic bubble detection algorithm for use with electrical capacitance volume tomography (ECVT) sensors for bubbling fluidized beds. Starting with an in-depth review of existing bubble detection methods, the novel phase detection method was developed to address the shortcomings of the other published methods by more fully utilizing the three-dimensional capability of the sensors. After the sensor parameters were optimized to verify capability of bubble detection, measurements were made with glass beads and quartz sand. A range of air velocities in a 10 cm diameter bubbling fluidized bed were used. The results largely agree with the fundamentals of bubbling fluidization and results from publications with similar experimental setups. Within each measurement the bubble dimensions, velocities and frequencies over the axial and radial position could be evaluated. Due to the three-dimensional nature of the novel bubble detection technique, insight into the directional tendencies of detected bubbles was gained. For example, bubble migration toward the radial center of the bed, radial and axial bubble coalescence, and splitting are more evident from the trends that are producible with this method.

Joint focusing method based on optimal image-distance interval for multi-image-distance imaging system

The multi-image-distance imaging system captures multiple images simultaneously with different focused depths, and uses image fusion to obtain an all-in-focus image for extending the depth of field (DoF). However, an image-distance adjustment strategy that takes into account the optimal DoF extension capability and imaging quality remains to be explored. In this paper, we propose a joint focusing method based on the optimal image-distance interval between the three sensors. First, we propose multi-sensor image-distance adjustment control strategies for different scenes. Then, theoretical analyses and calibration experiments of the optimal image-distance interval are conducted for continuous depth scenes. In order to quantitatively evaluate the DoF extension performance of the imaging system, a DoF estimation method based on point spread function measurement is proposed. Additionally, an evaluation metrics for extended DoF imaging based on the range and continuity of DoF is presented. We have developed a multi-image-distance electric focusing imaging system. Imaging experiments are conducted in various scenes to verify the effectiveness of the joint focusing method. The joint focusing method, based on the optimal image-distance interval, provides an image-distance adjustment strategy to achieve high-quality extended DoF imaging for multi-image-distance imaging systems.

Increased extra-neurite conductivity of brain in patients with Alzheimer's disease: A pilot study

The objectives of this study were to investigate how the extra-neurite conductivity (EC) and intra-neurite conductivity (IC) were reflected in Alzheimer's disease (AD) patients compared with old cognitively normal (CN) people and patients with amnestic mild cognitive impairment (MCI) and to evaluate the association between those conductivity values and cognitive decline. To do this, high-frequency conductivity (HFC) at the Larmor frequency was obtained using MRI-based electrical property tomography (MREPT) and was decomposed into EC and IC using information of multi-shell multi-gradient direction diffusion tensor images. This prospective single-center study included 20 patients with mild or moderate AD, 25 patients with amnestic MCI, and 21 old CN participants. After decomposing EC and IC from HFC for all participants, we performed voxel-based and regions-of-interest analyses to compare conductivity between the three participant groups and to evaluate the association with either age or the Mini-Mental State Examination (MMSE) scores. We found increased EC in AD compared to CN and MCI. EC was significantly negatively associated with MMSE scores in the insula, and middle temporal gyrus. EC might be used as an imaging biomarker for helping to monitor cognitive function.

MR-based electrical property tomography using a physics-informed network at 3 and 7 T.

Magnetic resonance electrical propert tomography promises to retrieve electrical properties (EPs) quantitatively and non-invasively in vivo, providing valuable information for tissue characterization and pathology diagnosis. However, its clinical implementation has been hindered by, for example, B1 measurement accuracy, reconstruction artifacts resulting from inaccuracies in underlying models, and stringent hardware/software requirements. To address these challenges, we present a novel approach aimed at accurate and high-resolution EPs reconstruction based on water content maps by using a physics-informed network (PIN-wEPT). The proposed method utilizes standard clinical protocols and conventional multi-channel receive arrays that have been routinely equipped in clinical settings, thus eliminating the need for specialized RF sequence/coil configurations. Compared with the original wEPT method, the network generates accurate water content maps that effectively eliminate the influence of and by incorporating data mismatch with electrodynamic constraints derived from the Helmholtz equation. Subsequent regression analysis develops a broad relationship between water content and EPs across various types of brain tissue. A series of numerical simulations was conducted at 7 T to assess the feasibility and performance of the method, which encompassed four normal head models and models with tumorous tissues incorporated, and the results showed normalized mean square error below 1.0% in water content, below 11.7% in conductivity, and below 1.1% in permittivity reconstructions for normal brain tissues. Moreover, in vivo validations conducted over five healthy subjects at both 3 and 7 T showed reasonably good consistency with empirical EPs values across the white matter, gray matter, and cerebrospinal fluid. The PIN-wEPT method, with its demonstrated efficacy, flexibility, and compatibility with current MRI scanners, holds promising potential for future clinical application.

High-density electric source imaging patterns and outcomes following temporal lobectomy in patients with hippocampal sclerosis.

The objective of this study was to ascertain specific patterns of electrical source imaging (ESI) that are associated with a good surgical outcome (no seizure recurrence) using 256-channel high-density (HD) electroencephalography (EEG) in patients with temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) who underwent temporal lobectomy. Adult patients (age ≥ 18 years) were prospectively recruited from September 2016 to May 2020 at the authors' center. All patients underwent phase I presurgical evaluation and were subsequently advised to proceed with surgery based on consensus from a multidisciplinary epilepsy conference, without knowing HD-ESI results. All recruited patients were followed for at least 12 months after surgery. The outcome of interest was a status of no seizure recurrence, which was assessed at the end of the study. The association between ESI patterns and outcome was assessed using the chi-square or Fisher exact test. Associated p values as well as odds ratios and 95% CIs were reported. The diagnostic performance of the significant pattern associated with the outcome was also evaluated. Fifty-eight patients with known predictors for either good or worse surgical outcomes were recruited. The mean postoperative follow-up period was 33.34 (SD 13.88) months. Forty-seven patients had sufficient interictal epileptiform discharges for HD-ESI analysis. Thirteen of these 47 patients experienced seizure recurrence. The most common source localizations were at Brodmann area (BA) 20 (inferior temporal area) and BA 21 (middle temporal area). A specific ESI pattern of BA 21 without extratemporal sources was significantly associated with no seizure recurrence (p = 0.047). This pattern had a high positive predictive value of 100% and false-positive rate of 0% associated with no seizure recurrence following the surgery. A specific ESI pattern that was highly associated with no seizure recurrence following surgery was demonstrated by a 256-channel HD-EEG. If this pattern can be reproducibly proven in further studies, some TLE-HS patients may be able to proceed with surgery without further investigations.

CBR Mapping Prediction from 2D Resistivity Imaging Using Regression Following Archie’s Formula

Indirect geophysical methods are increasingly associated with direct underground methods in investigating the subsurface to address environmental and geotechnical problems and reduce the cost of underground studies. These methods make it possible to explore large areas with acceptable precision, time, and cost. The present study combines the electrical resistivity method with the California Bearing Ratio for exploring an area located west of the Nouakchott port in Mauritania. The study aims firstly to verify that the thickness of the backfill layer must be less than 2 m throughout the entire study area and secondly to map areas with low California Bearing Ratio values (<80%) indicating substandard backfill compaction using 2D Electrical Resistivity Tomography imaging survey and regression. The measured Electrical Resistivity values exhibit a good nonlinear regression with California Bearing Ratio, following Archie's equation. A map of California Bearing Ratio variation was derived from the Electrical Resistivity values, indicating the distribution and variation of soil strength in the study area. The results revealed that the backfill layer did not meet the standards, with approximately 35% of the total area having a California Bearing Ratio value below 80%. The areas with poor compaction requiring treatment were primarily located in the southern sector, followed by the middle and eastern sectors.

Task Scheduling Optimization for UAV Electrical Image Fault Detection with Wireless Power Transfer

The smooth operation of the power system is of significant importance for ensuring societal stability. Therefore, regular inspections of the power system are necessary. The most efficient method for power inspection is to utilize Unmanned Aerial Vehicles (UAVs) to assist inspection. However, the UAV inspection still needs human resources to operate and manage the UAVs effectively. This paper proposes a UAV-airport power inspection system, where the airport can operate and manage UAVs instead of humans. The key contribution of the system is the computation offloading technology and the Wireless Power Transfer (WPT) capability. In other words, the airport can help UAVs execute electrical image fault detection and the UAVs can be charged by the airport. We propose a joint optimization strategy for UAV flight speed, local execution frequency for fault detection, and WPT time to maximize the number of images in the electrical dataset captured by the UAV that have undergone power fault detection within the inspection time. Comprehensive experiments are conducted to verify the performance of the UAV-airport power inspection system and our optimization strategy.

Application of electrical impedance tomography imaging technology combined with generative adversarial network in pulmonary ventilation monitoring

Electrical impedance tomography (EIT) plays a crucial role in the monitoring of pulmonary ventilation and regional pulmonary function test. However, the inherent ill-posed nature of EIT algorithms results in significant deviations in the reconstructed conductivity obtained from voltage data contaminated with noise, making it challenging to obtain accurate distribution images of conductivity change as well as clear boundary contours. In order to enhance the image quality of EIT in lung ventilation monitoring, a novel approach integrating the EIT with deep learning algorithm was proposed. Firstly, an optimized operator was introduced to enhance the Kalman filter algorithm, and Tikhonov regularization was incorporated into the state-space expression of the algorithm to obtain the initial lung image reconstructed. Following that, the imaging outcomes were fed into a generative adversarial network model in order to reconstruct accurate lung contours. The simulation experiment results indicate that the proposed method produces pulmonary images with clear boundaries, demonstrating increased robustness against noise interference. This methodology effectively achieves a satisfactory level of visualization and holds potential significance as a reference for the diagnostic purposes of imaging modalities such as computed tomography.

CGAN-ECT: Reconstruction of Electrical Capacitance Tomography images from capacitance measurements using Conditional Generative Adversarial Networks

Due to the rapid growth of applications of Electrical Capacitance Tomography (ECT) across diverse industrial sectors, there is a pressing demand for swift and high-fidelity methodologies for image reconstruction from raw capacitance measurements. Deep learning, as an effective non-linear mapping tool for complicated functions, has been going viral in many fields including electrical tomography. This paper introduces a pioneering contribution by proposing a Conditional Generative Adversarial Network (CGAN) model explicitly tailored for the purpose of reconstructing ECT images from capacitance measurements. We propose a novel modulator to transform the raw input capacitance measurements into a 2D image. To the best of our knowledge, this is the first time to represent the capacitance measurements in an image form. We have created a new massive ECT dataset of synthetic image-measurements pairs for training and testing purposes of the proposed model. This extensive ECT dataset comprises 320,000 synthetic image-measurement pairs. It forms the basis for evaluating the feasibility and generalizability of the CGAN-ECT model, with assessments conducted on testing data, contaminated data, and flow patterns which are intentionally excluded from the model’s training phase. The evaluation results prove the efficacy of the proposed CGAN model in generating ECT images with heightened accuracy compared to traditional methods and alternative deep learning-based image reconstruction algorithms. The CGAN-ECT model has succeeded in a achieving an average image correlation coefficient exceeding 99.3%, coupled with an average relative image error of approximately 0.07.

Detection Method and Application of Nuclear-Shaped Anomaly Areas in Spatial Electric Field Power Spectrum Images

It is found that there are some anomalous high-energy nuclear-shaped regions in the VLF frequency band of the space electric field. To detect and statistically analyze these nuclear-shaped anomaly areas, this paper proposes a nuclear-shaped anomaly area detection method based on the electric field power spectrum image data of the China Seismo Electromagnetic Satellite (CSES-01). First, the logarithm of VLF frequency band data was calculated and rotated counterclockwise to create power spectrum images and label them to form a sample image dataset; then, images were enhanced (which involved resizing, scaling, rotation, gaussian denoising, etc.) to solve the problems of the model overfitting and sample imbalance. Finally, the U-net network model based on the ResNet50 encoder was trained to obtain the optimal kernel anomaly detection model ResNet50_Unet. Comparative experiments with various semantic segmentation algorithms show that the ResNet50_Unet model has the best performance. Applying this model to detect the electric field power spectrum images from November 2021 to February 2022, a total of 101 nuclear-shaped anomaly areas were found, distributed between 45° and 70° of the north–south latitude. This model can quickly detect nuclear-shaped anomaly regions from massive data, providing reference significance for the detection of other types of ionospheric spatial disturbances. At the same time, it has important scientific significance and practical value for understanding the ionosphere and space communication.

Synchronous recording of magnetocardiographic and electrocardiographic signals

We present a system for simultaneous recording of the electrocardiogram and the magnetocardiogram. The measurement system contained of printed carbon electrodes and SERF magnetometer. The use of this system confirms that the position of the end of the magnetic T wave extends further than the electric T wave, which is an important indicator for the diagnosis of cardiological patients and for drug arrhythmogenicity. We analyze this phenomenon in depth, and demonstrate, that it originates from the fundamental difference between electric and magnetic measurements. The measured value is always bipolar since the electric measurements require two electrodes. We demonstrate how the dual electric and magnetic measuring system adds a new information to the commonly used electrocardiographic diagnosis. The ECG should be interpreted as the spatial asymmetry of the electric cardiac potential, and not as the potential itself. The results seem to prove, that the relation between the magnetic and the electric imaging of neural activities may be broadly applied for the benefit of medical diagnosis in cardiology and many other fields, where the neural activity is measured. This is a pilot study which requires further confirmation at the clinical level.

Improving the sensitivity of cochlear implant integrity testing by recording electrode voltages with surface electrodes

IntroductionIdentification of faults with the internal, implanted, part of a cochlear implant presents a challenge for the cochlear implant community. Advanced Bionics Ultra V1 devices are vulnerable to moisture ingress, a hard failure, resulting in reduced volume and clarity for the recipient. The manufacturer uses a trans-impedance test “Electrical Field Imaging” to identify faulty Ultra V1 devices but reports the sensitivity of the test to be only 70–90%.MethodsIn our clinic we performed Electrode Voltage measurements with surface electrodes and have compared the two tests. Electrical Field imaging and Electrode Voltage (EV) measurements were available for 65 devices. Surface electrodes were attached to the earlobes and forehead and potentials measured in three montages: ipsilateral earlobe and forehead, contralateral earlobe and forehead, and both earlobes; voltages were extracted and relative voltages across the array were calculated.ResultsRelative EV were compared for the two earlobes montage and fitted to a third order polynomial function. A new criterion for identifying faulty devices was derived, with a deviation of < 6% for individual electrodes for normally functioning devices or ≥6% for faulty devices. All devices which were normal according to the new criterion (N = 15) had a normal electrical field imaging test, whilst 17/50 devices which were abnormal had normal electrical field imaging and 33/50 which were abnormal had abnormal electrical field imaging.DiscussionThe REVs test was well-tolerated and carried out in a routine cochlear implant clinic. Together with test sensitivity and reliability this may make it a new routine assessment tool to aid in distinguishing hard and soft failures.

On Using CFD and Experimental Data to Train an Artificial Neural Network to Reconstruct ECVT Images: Application for Fluidized Bed Reactors

Electrical capacitance volume tomography (ECVT) is an experimental technique capable of reconstructing 3D solid volume fraction distribution inside a sensing region. This technique has been used in fluidized beds as it allows for accessing data that are very difficult to obtain using other experimental devices. Recently, artificial neural networks have been proposed as a new type of reconstruction algorithm for ECVT devices. One of the main drawbacks of neural networks is that they need a database containing previously reconstructed images to learn from. Previous works have used databases with very simple or limited configurations that might not be well adapted to the complex dynamics of fluidized bed configurations. In this work, we study two different approaches: a supervised learning approach that uses simulated data as a training database and a reinforcement learning approach that relies only on experimental data. Our results show that both techniques can perform as well as the classical algorithms. However, once the neural networks are trained, the reconstruction process is much faster than the classical algorithms.

Exposure Time Control Method for Higher Intermediate Frequency in Optical Heterodyne Imaging and Its Application to Electric-Field Imaging Based on Electro-Optic Effect.

We propose and demonstrate a method for equivalent time sampling using image sensors to selectively detect only the target frequency. Shortening the exposure time of the image sensor and using equivalent time sampling allows for the detection of frequency components that are higher than the frame rate. However, the imaging system in our previous work was also sensitive to the frequency component at 1/4 of the frame rate. In this study, we control the phase relationship between the exposure time and observed signal by inserting an additional interval once every four frames to detect the target frequency selectively. With this technique, we conducted electric field imaging based on the electro-optic effect under high noise conditions in the low-frequency band to which the conventional method is sensitive. The results demonstrated that the proposed method improved the signal-to-noise ratio.

Investigation of flow characteristics in a rectangular spouted fluidized bed using electrical capacitance volume tomography

The characterization of gas-solid flow behavior in rectangular spouted fluidized bed (RSFB) poses significant challenges due to the presence of complex gas-phase vortex flow within the bed. The flow characteristics of gas-solid two-phase flow in RSFB were investigated by using a self-developed 32-electrode electrical capacitance volume tomography (ECVT) system with an imaging speed of 105 frames per second when the spouting gas velocity Uf increased from 0.97 m/s to 1.78 m/s. Experimental results demonstrate that with increasing Uf, the flow transitions from weak bubbling/bubble fluidization to turbulent fluidization in the RSFB. The amplitude peak difference of particles in different planes of the fluidized bed gradually decreases, and the rising velocity of bubbles initially decreases and then increases. The gas-particle backmixing effect strengthens, while the entrainment effect during bubble rupture weakens. The research findings can provide valuable insights for understanding the flow behavior in RSFB and offer guidance for optimizing the process operation of fluidized bed reactors.

Transimpedance Matrix Can Be Used to Estimate Electrode Positions Intraoperatively and to Monitor Their Positional Changes Postoperatively in Cochlear Implant Patients.

Accurate positioning of the electrode array during cochlear implant (CI) surgery is crucial for achieving optimal hearing outcomes. Traditionally, postoperative radiological imaging has been used to assess electrode position. Transimpedance matrix (TIM) measurements have also emerged as a promising method for assessing electrode position. This involves utilizing electric field imaging to create an electric distance matrix by analyzing voltage variations among adjacent electrodes. This study aimed to investigate the feasibility of using intraoperative TIM measurements to estimate electrode position and monitor postoperative changes. Retrospective cohort study. University Medical center, tertiary academic referral center. Patients undergoing CI (CI622) surgery between January 2019 and June 2022. CI electrode positions and maximal angular insertion depths (maxAID) were determined using X-ray imaging according to Stenvers' projection. The mean gradient phase (MGP) was extracted from the TIM, and a correlation between the MGP and maxAID was examined. A model was then built to estimate the maxAID using the MGP, and changes in electrode location over time were assessed using this model. Twenty-four patients were included in this study. A positive correlation between the maxAID and the MGP ( R = 0.7, p = 0.0001) was found. The established model was able to predict the maxAID with an accuracy of 27.7 ± 4.4°. Comparing intraoperative and postoperative TIM measurements, a decrease of 24.1° ± 10.7° in maxAID over time was observed. TIM measurements are useful for estimating the insertion depth of the electrode and monitoring changes in the electrode's position over time.