A comparison of methods for measurement of spatial resolution in two-dimensional circular EIT images

Seeing Through Muddy Water: Laser-Induced Graphene for Portable Tomography Imaging

Due to its outstanding physical and chemical properties, graphene synthesized by laser scribing on polyimide (PI) offers excellent opportunities for photothermal applications, antiviral and antibacterial surfaces, and electrochemical storage and sensing. However, the utilization of such graphene for imaging is yet to be explored. Herein, using chemically durable and electrically conductive laser-induced graphene (LIG) for tomography imaging in aqueous suspensions is proposed. These graphene electrodes are designed as impedance imaging units for four-terminal electrical measurements. Using the real-time portable imaging prototypes, the conductive and dielectric objects can be seen in clear and muddy water with equivalent impedance modeling. This low-cost graphene tomography measurement system offers significant advantages over traditional visual cameras, in which the suspended muddy particles hinder the imaging resolution. This research shows the potential of applying graphene nanomaterials in emerging marine technologies, such as underwater robotics and automatic fisheries.

Technical Principles and Clinical Applications of Electrical Impedance Tomography in Pulmonary Monitoring

Pulmonary monitoring is crucial for the diagnosis and management of respiratory conditions, especially after the epidemic of coronavirus disease. Electrical impedance tomography (EIT) is an alternative non-radioactive tomographic imaging tool for monitoring pulmonary conditions. This review proffers the current EIT technical principles and applications on pulmonary monitoring, which gives a comprehensive summary of EIT applied on the chest and encourages its extensive usage to clinical physicians. The technical principles involving EIT instrumentations and image reconstruction algorithms are explained in detail, and the conditional selection is recommended based on clinical application scenarios. For applications, specifically, the monitoring of ventilation/perfusion (V/Q) is one of the most developed EIT applications. The matching correlation of V/Q could indicate many pulmonary diseases, e.g., the acute respiratory distress syndrome, pneumothorax, pulmonary embolism, and pulmonary edema. Several recently emerging applications like lung transplantation are also briefly introduced as supplementary applications that have potential and are about to be developed in the future. In addition, the limitations, disadvantages, and developing trends of EIT are discussed, indicating that EIT will still be in a long-term development stage before large-scale clinical applications.

Signal-to-noise ratio variance impact on the image reconstruction of electrical resistance tomography in solutions with high background conductivity

Electrical Resistance Tomography (ERT) has the potentialities of non-intrusive techniques and high temporal resolution which are essential characteristics for multiphase flow measurements. However, high background conductivities, such as saline water in oil extraction, impose a limitation in ERT image reconstruction. Focusing on the operational limits of an ERT tomography system operating in different conductivity backgrounds from 0.010 to 4.584 S/m, the impact on the image reconstruction was assessed via signal-to-noise variance. The signal-to-noise ratio (SNR) variance had a strong correlation (p-value = 5.40 × 10^-15) with the image reconstruction quality at the threshold of 30 dB, reaching a correlation value of r = -0.92 in the range of 0.010-0.246 S/m. Regarding the position error of the phantom, p-value = 1.30 × 10^-5 and r = -0.66 were attained. The global results revealed that the correlation of the mean of the SNR (p-value = 5 × 10^-4 and r = 0.55) was kept unaltered through the whole conductivity range, showing that such a statistical index can induce bias in establishing the operational limits of the hardware.

Spatially resolved electrical impedance methods for cell and particle characterization

Electrical impedance is an established technique used for cell and particle characterization. The temporal and spectral resolution of electrical impedance have been used to resolve basic cell characteristics like size and type, as well as to determine cell viability and activity. Such electrical impedance measurements are typically performed across the entire sample volume and can only provide an overall indication concerning the properties and state of that sample. For the study of heterogeneous structures such as cell layers, biological tissue, or polydisperse particle mixtures, an overall measured impedance value can only provide limited information and can lead to data misinterpretation. For the investigation of localized sample properties in complex heterogeneous structures/mixtures, the addition of spatial resolution to impedance measurements is necessary. Several spatially resolved impedance measurement techniques have been developed and applied to cell and particle research, including electrical impedance tomography, scanning electrochemical microscopy, and microelectrode arrays. This review provides an overview of spatially resolved impedance measurement methods and assesses their applicability for cell and particle characterization.

Noncontact Strain Monitoring of Osseointegrated Prostheses

The objective of this study was to develop a noncontact, noninvasive, imaging system for monitoring the strain and deformation states of osseointegrated prostheses. The proposed sensing methodology comprised of two parts. First, a passive thin film was designed such that its electrical permittivity increases in tandem with applied tensile loading and decreases while unloading. It was found that patterning the thin films could enhance their dielectric property's sensitivity to strain. The film can be deposited onto prosthesis surfaces as an external coating prior to implant. Second, an electrical capacitance tomography (ECT) measurement technique and reconstruction algorithm were implemented to capture strain-induced changes in the dielectric property of nanocomposite-coated prosthesis phantoms when subjected to different loading scenarios. The preliminary results showed that ECT, when coupled with strain-sensitive nanocomposites, could quantify the strain-induced changes in the dielectric property of thin film-coated prosthesis phantoms. The results suggested that ECT coupled with embedded thin films could serve as a new noncontact strain sensing method for scenarios when tethered strain sensors cannot be used or instrumented, especially in the case of osseointegrated prostheses.

A Versatile Noise Performance Metric for Electrical Impedance Tomography Algorithms

Electrical impedance tomography (EIT) is an emerging technology for real-time monitoring of patients under mechanical ventilation. EIT has the potential to offer continuous medical monitoring while being noninvasive, radiation free, and low cost. Due to their ill-posedness, image reconstruction typically uses regularization, which implies a hyperparameter controlling the tradeoff between noise rejection and resolution or other accuracies. In order to compare reconstruction algorithms, it is common to choose hyperparameter values such that the reconstructed images have equal noise performance (NP), i.e., the amount of measurement noise reflected in the images. For EIT many methods have been suggested, but none work well when the data originate from different measurement setups, such as for different electrode positions or measurement patterns. To address this issue, we propose a new NP metric based on the average signal-to-noise ratio in the image domain. The approach is validated for EIT using simulation experiments on a human thorax model and measurements on a resistor phantom. Results show that the approach is robust to the measurement configuration (i.e., number and position of electrodes, skip pattern) and the reconstruction algorithm used. We propose this novel approach as a way to select optimized measurement configurations and algorithms.

Trackability evaluation of reconstruction algorithms to the change of measured objects in electrical tomography

The continuous monitoring of the changing process is an important application field of electrical tomography (ET). In this changing process, the size, position and shape of measured objects are always alterative. The trackability of algorithms to the change of measured objects is important to the application of ET. The single object model group and two-object-model group were established to simulate the change of measured objects. The single object model group includes the circle model group and square model group. The suitable evaluation parameters were designed to evaluate the trackability of the different algorithms quantitatively, which includes the single image parameter and group parameter. Evaluation software was developed, which can generate measured boundary data of different models, complete reconstructed image greying, calculate evaluation parameters and plot parameter curves, etc. Furthermore, the trackability of ten selected algorithms was evaluated by this evaluation software. The results show that the trackability of the different algorithms is different in the evaluation of the different model group. Therefore, the different model group should be established according to the application requirement. Then the suitable algorithm for a particular application could be chosen through the evaluation process.

Evaluation of EIT system performance

An electrical impedance tomography (EIT) system images internal conductivity from surface electrical stimulation and measurement. Such systems necessarily comprise multiple design choices from cables and hardware design to calibration and image reconstruction. In order to compare EIT systems and study the consequences of changes in system performance, this paper describes a systematic approach to evaluate the performance of the EIT systems. The system to be tested is connected to a saline phantom in which calibrated contrasting test objects are systematically positioned using a position controller. A set of evaluation parameters are proposed which characterize (i) data and image noise, (ii) data accuracy, (iii) detectability of single contrasts and distinguishability of multiple contrasts, and (iv) accuracy of reconstructed image (amplitude, resolution, position and ringing). Using this approach, we evaluate three different EIT systems and illustrate the use of these tools to evaluate and compare performance. In order to facilitate the use of this approach, all details of the phantom, test objects and position controller design are made publicly available including the source code of the evaluation and reporting software.

Objective selection of hyperparameter for EIT

An algorithm for objectively calculating the hyperparameter for linearized one-step electrical impedance tomography (EIT) image reconstruction algorithms is proposed and compared to existing strategies. EIT is an ill-conditioned problem in which regularization is used to calculate a stable and accurate solution by incorporating some form of prior knowledge into the solution. A hyperparameter is used to control the trade-off between conformance to data and conformance to the prior. A remaining challenge is to develop and validate methods of objectively selecting the hyperparameter. In this paper, we evaluate and compare five different strategies for hyperparameter selection. We propose a calibration-based method of objective hyperparameter selection, called BestRes, that leads to repeatable and stable image reconstructions that are indistinguishable from heuristic selections. Results indicate: (1) heuristic selections of hyperparameter are inconsistent among experts, (2) generalized cross-validation approaches produce under-regularized solutions, (3) L-curve approaches are unreliable for EIT and (4) BestRes produces good solutions comparable to expert selections. Additionally, we show that it is possible to reliably detect an inverse crime based on analysis of these parameters.

Induced current electrical impedance tomography system: experimental results and numerical simulations

In electrical impedance tomography (EIT), measurements of developed surface potentials due to applied currents are used for the reconstruction of the conductivity distribution. Practical implementation of EIT systems is known to be problematic due to the high sensitivity to noise of such systems, leading to a poor imaging quality. In the present study, the performance of an induced current EIT (ICEIT) system, where eddy current is applied using magnetic induction, was studied by comparing the voltage measurements to simulated data, and examining the imaging quality with respect to simulated reconstructions for several phantom configurations. A 3-coil, 32-electrode ICEIT system was built, and an iterative modified Newton-Raphson algorithm was developed for the solution of the inverse problem. The RMS norm between the simulated and the experimental voltages was found to be 0.08 +/- 0.05 mV (<3%). Two regularization methods were implemented and compared: the Marquardt regularization and the Laplacian regularization (a bounded second-derivative regularization). While the Laplacian regularization method was found to be preferred for simulated data, it resulted in distinctive spatial artifacts for measured data. The experimental reconstructed images were found to be indicative of the angular positioning of the conductivity perturbations, though the radial sensitivity was low, especially when using the Marquardt regularization method.

Accounting for erroneous electrode data in electrical impedance tomography

An unfortunate occurrence in experimental measurements with electrical impedance tomography is electrodes which become detached or poorly connected, such that the measured data cannot be used. This paper develops an image reconstruction methodology which allows use of the remaining valid data. A finite element model of the EIT difference imaging forward problem is linearized as z = Hx, where z represents the change in measurements and x the element log conductivity changes. Image reconstruction is represented in terms of a maximum a posteriori (MAP) estimate as x = inv(Htinv(Rn) + inv(Rx))Htinv(Rn)z, where Rx and Rn represent the a priori estimates of image and measurement noise crosscorrelations, respectively. Using this formulation, missing electrode data can be naturally modelled as infinite noise on all measurements using the affected electrodes. Simulations indicate position error and resolution are close (+/- 10%) to the values calculated without missing electrode data as long as the target was further than 10% of the medium diameter from the affected electrode. Applications of this technique to experimental data show good results in terms of removing artefacts from images.

Effects of incompatible boundary information in EIT on the convergence behavior of an iterative algorithm

In electrical impedance tomography, currents are applied to the body through electrodes that are attached to the surface and the corresponding surface voltages are measured. Based on these boundary measurements, the internal admittivity distribution of the body can be reconstructed. In order to improve the image quality it is necessary and useful to apply physiologically meaningful prior information into the image reconstruction. Such prior information usually can be obtained from other sources. For example, information on the object's boundary shape and internal structure can be obtained from computed tomography and magnetic resonance imaging scan. However, this type of prior information may change from time to time and from person to person. As these changes are limited anatomically and physiologically, the prior information including the possible changes can be presented in a number of variational forms. The aim of this paper is to find which form of prior information is more compatible for a specific imaged object at the time of imaging. This paper proposes a new method for selecting the most appropriate form of prior information, through the procedure of iterative image reconstruction by using the information obtained from boundary measurements. The method is based on the principle that incompatible prior information causes errors which are able to affect the image reconstruction's convergence behavior. In this method, according to the various forms of prior information available, several image reconstruction configurations are designed. Then, through monitoring the convergence behavior in an iterative image reconstruction, the configuration with compatible prior information can be found among those different configurations. As an example, the prior information regarding the imaged object's boundary shape and internal structure was studied by computer simulation. Results were shown and discussed.