Multifrequency electrical impedance tomography using spectral constraints

Multi-Path Fusion in SFCF-Net for Enhanced Multi-Frequency Electrical Impedance Tomography

Multi-frequency electrical impedance tomography (mfEIT) offers a nondestructive imaging technology that reconstructs the distribution of electrical characteristics within a subject based on the impedance spectral differences among biological tissues. However, the technology faces challenges in imaging multi-class lesion targets when the conductivity of background tissues is frequency-dependent. To address these issues, we propose a spatial-frequency cross-fusion network (SFCF-Net) imaging algorithm, built on a multi-path fusion structure. This algorithm uses multi-path structures and hyper-dense connections to capture both spatial and frequency correlations between multi-frequency conductivity images, which achieves differential imaging for lesion targets of multiple categories through cross-fusion of information. According to both simulation and physical experiment results, the proposed SFCF-Net algorithm shows an excellent performance in terms of lesion imaging and category discrimination compared to the weighted frequency-difference, U-Net, and MMV-Net algorithms. The proposed algorithm enhances the ability of mfEIT to simultaneously obtain both structural and spectral information from the tissue being examined and improves the accuracy and reliability of mfEIT, opening new avenues for its application in clinical diagnostics and treatment monitoring.

Bi-directional hyperspectral reconstruction of cherry tomato: diagnosis of internal tissues maturation stage and composition

Introduction

Precision monitoring maturity in climacteric fruits like tomato is crucial for minimising losses within the food supply chain and enhancing pre- and post-harvest production and utilisation.

Objectives

This paper introduces an approach to analyse the precision maturation of tomato using hyperspectral tomography-like.

Methods

A novel bi-directional spectral reconstruction method is presented, leveraging visible to near-infrared (Vis-NIR) information gathered from tomato spectra and their internal tissues (skin, pulp, and seeds). The study, encompassing 118 tomatoes at various maturation stages, employs a multi-block hierarchical principal component analysis combined with partial least squares for bi-directional reconstruction. The approach involves predicting internal tissue spectra by decomposing the overall tomato spectral information, creating a superset with eight latent variables for each tissue. The reverse process also utilises eight latent variables for reconstructing skin, pulp, and seed spectral data.

Results

The reconstruction of the tomato spectra presents a mean absolute percentage error of 30.44 % and 5.37 %, 5.25 % and 6.42 % and Pearson's correlation coefficient of 0.85, 0.98, 0.99 and 0.99 for the skin, pulp and seed, respectively. Quality parameters, including soluble solid content (%), chlorophyll (a.u.), lycopene (a.u.), and puncture force (N), were assessed and modelled with PLS with the original and reconstructed datasets, presenting a range of R2 higher than 0.84 in the reconstructed dataset. An empirical demonstration of the tomato maturation in the internal tissues revealed the dynamic of the chlorophyll and lycopene in the different tissues during the maturation process.

Conclusion

The proposed approach for inner tomato tissue spectral inference is highly reliable, provides early indications and is easy to operate. This study highlights the potential of Vis-NIR devices in precision fruit maturation assessment, surpassing conventional labour-intensive techniques in cost-effectiveness and efficiency. The implications of this advancement extend to various agronomic and food chain applications, promising substantial improvements in monitoring and enhancing fruit quality.

Applied machine learning for stroke differentiation by electrical impedance tomography with realistic numerical models

Electrical impedance tomography (EIT) may have potential to overcome existing limitations in stroke differentiation, enabling low-cost, rapid, and mobile data collection. Combining bioimpedance measurement technologies such as EIT with machine learning classifiers to support decision-making can avoid commonly faced reconstruction challenges due to the nonlinear and ill-posed nature of EIT imaging. Therefore, in this work, we advance this field through a study integrating realistic head models with clinically relevant test scenarios, and a robust architecture consisting of nested cross-validation and principal component analysis. Specifically, realistic head models are designed which incorporate the highly conductive layers of cerebrospinal fluid in the subarachnoid space and ventricles. In total, 135 unique models are created to represent a large patient population, with normal, haemorrhagic, and ischemic brains. Simulated EIT voltage data generated from these models are used to assess the classification performance of support vector machines. Parameters explored include driving frequency, signal-to-noise ratio, kernel function, and composition of binary classes. Classifier accuracy at 60 dB signal-to-noise ratio, reported as mean and standard deviation, are (79.92% ± 10.82%) for lesion differentiation, (74.78% ± 3.79%) for lesion detection, (77.49% ± 15.90%) for bleed detection, and (60.31% ± 3.98%) for ischemia detection (after ruling out bleed). The results for each method were obtained with statistics from 3 independent runs with 17,280 observations, polynomial kernel functions, and feature reduction of 76% by PCA (from 208 to 50 features). While results of this study show promise for stroke differentiation using EIT data, our findings indicate that the achievable accuracy is highly dependent on the classification scenario and application-specific classifiers may be necessary to achieve acceptable accuracy.

Electronic Alert Signal for Early Detection of Tissue Injuries in Patients: An Innovative Pressure Sensor Mattress

Monitoring the early stage of developing tissue injuries requires intact skin for surface detection of cell damage. However, electronic alert signal for early detection is limited due to the lack of accurate pressure sensors for lightly pigmented skin injuries in patients. We developed an innovative pressure sensor mattress that produces an electronic alert signal for the early detection of tissue injuries. The electronic alert signal is developed using a web and mobile application for pressure sensor mattress reporting. The mattress is based on body distributions with reference points, temperature, and a humidity sensor to detect lightly pigmented skin injuries. Early detection of the pressure sensor is linked to an electronic alert signal at 32 mm Hg, a temperature of 37 °C, a relative humidity of 33.5%, a response time of 10 s, a loading time of 30 g, a density area of 1 mA, and a resistance of 7.05 MPa (54 N) at 0.87 m³/min. The development of the innovative pressure sensor mattress using an electronic alert signal is in line with its enhanced pressure detection, temperature, and humidity sensors.

Fast Iterative Shrinkage-Thresholding Algorithm with Continuation for Brain Injury Monitoring Imaging Based on Electrical Impedance Tomography

Electrical impedance tomography (EIT) is low-cost and noninvasive and has the potential for real-time imaging and bedside monitoring of brain injury. However, brain injury monitoring by EIT imaging suffers from image noise (IN) and resolution problems, causing blurred reconstructions. To address these problems, a least absolute shrinkage and selection operator model is built, and a fast iterative shrinkage-thresholding algorithm with continuation (FISTA-C) is proposed. Results of numerical simulations and head phantom experiments indicate that FISTA-C reduces IN by 63.2%, 47.2%, and 29.9% and 54.4%, 44.7%, and 22.7%, respectively, when compared with the damped least-squares algorithm, the split Bergman, and the FISTA algorithms. When the signal-to-noise ratio of the measurements is 80-50 dB, FISTA-C can reduce IN by 83.3%, 72.3%, and 68.7% on average when compared with the three algorithms, respectively. Both simulation and phantom experiments suggest that FISTA-C produces the best image resolution and can identify the two closest targets. Moreover, FISTA-C is more practical for clinical application because it does not require excessive parameter adjustments. This technology can provide better reconstruction performance and significantly outperforms the traditional algorithms in terms of IN and resolution and is expected to offer a general algorithm for brain injury monitoring imaging via EIT.

A Rapid, Low-Cost, and High-Precision Multifrequency Electrical Impedance Tomography Data Acquisition System for Plant Phenotyping

Plant phenotyping plays an important role for the thorough assessment of plant traits such as growth, development, and physiological processes with the target of achieving higher crop yields by the proper crop management. The assessment can be done by utilizing two- and three-dimensional image reconstructions of the inhomogeneities. The quality of the reconstructed image is required to maintain a high accuracy and a good resolution, and it is desirable to reconstruct the images with the lowest possible noise. In this work, an electrical impedance tomography (EIT) data acquisition system is developed for the reconstruction and evaluation of the inhomogeneities by utilizing a non-destructive method. A high-precision EIT system is developed by designing an electrode array sensor using a cylindrical domain for the measurements in different planes. Different edible plant slices along with multiple plant roots are taken in the EIT domain to assess and calibrate the system, and their reconstructed results are evaluated by utilizing an impedance imaging technique. A non-invasive imaging is carried out in multiple frequencies by utilizing a difference method of reconstruction. The performance and accuracy of the EIT system are evaluated by measuring impedances between 1 and 100 kHz using a low-cost and rapid electrical impedance spectroscopy (EIS) tool connected to the sensor. A finite element method (FEM) modeling is utilized for image reconstruction, which is carried out using electrical impedance and diffuse optical tomography reconstruction software (EIDORS). The reconstruction is made successfully with the optimized results obtained using Gauss–Newton (GN) algorithms.

An In Situ Electrical Impedance Tomography Sensor System for Biomass Estimation of Tap Roots

Root biomass is one of the most relevant root parameters for studies of plant response to environmental change. In this work, a dynamic and adjustable electrode array sensor system is designed for developing a cost-effective, high-speed data acquisition system based on electrical impedance tomography (EIT). The developed EIT system is found to be suitable for in situ measurements and capable of monitoring the changes in root growth and development with three-dimensional imaging by measuring impedances in multiple frequencies with the help of an EIT sensor. The designed EIT sensor system is assessed and calibrated by the inhomogeneities in both water and soil media. The impedances are measured for multiple tap roots using an electrical impedance spectroscopy (EIS) tool connected to the sensor at frequencies ranging from 1 kHz to 100 kHz. The changes in conductivity are calculated by obtaining the boundary voltages from the measured impedances for a given stimulation current. A non-invasive imaging method is utilized, and the spectral changes are observed accordingly to evaluate the growth of the roots. A further root analysis helps us estimate the root biomass non-destructively in real-time. The root size (such as, weight, length) is correlated with the measured impedances. A regression analysis is performed using the least square method, and more than 97% correlation is found for the biomass estimation of carrot roots with an RMSE of 4.516. The obtained models are later validated using a new and separate set of carrot root samples and the accuracy of the predicted models is found to be 93% or above. A complete electrode model is utilized, and the reconstruction analysis is performed and optimized by utilizing the impedance imaging technique in difference method. The tomography of the root is reconstructed with finite element method (FEM) modeling considering one-step Gauss–Newton (GN) algorithm which is carried out using an open source software known as electrical impedance and diffuse optical tomography reconstruction software (EIDORS).

Advances in electrical impedance tomography-based brain imaging

Novel advances in the field of brain imaging have enabled the unprecedented clinical application of various imaging modalities to facilitate disease diagnosis and treatment. Electrical impedance tomography (EIT) is a functional imaging technique that measures the transfer impedances between electrodes on the body surface to estimate the spatial distribution of electrical properties of tissues. EIT offers many advantages over other neuroimaging technologies, which has led to its potential clinical use. This qualitative review provides an overview of the basic principles, algorithms, and system composition of EIT. Recent advances in the field of EIT are discussed in the context of epilepsy, stroke, brain injuries and edema, and other brain diseases. Further, we summarize factors limiting the development of brain EIT and highlight prospects for the field. In epilepsy imaging, there have been advances in EIT imaging depth, from cortical to subcortical regions. In stroke research, a bedside EIT stroke monitoring system has been developed for clinical practice, and data support the role of EIT in multi-modal imaging for diagnosing stroke. Additionally, EIT has been applied to monitor the changes in brain water content associated with cerebral edema, enabling the early identification of brain edema and the evaluation of mannitol dehydration. However, anatomically realistic geometry, inhomogeneity, cranium completeness, anisotropy and skull type, etc., must be considered to improve the accuracy of EIT modeling. Thus, the further establishment of EIT as a mature and routine diagnostic technique will necessitate the accumulation of more supporting evidence.

(An overview of) Synergistic reconstruction for multimodality/multichannel imaging methods

Imaging is omnipresent in modern society with imaging devices based on a zoo of physical principles, probing a specimen across different wavelengths, energies and time. Recent years have seen a change in the imaging landscape with more and more imaging devices combining that which previously was used separately. Motivated by these hardware developments, an ever increasing set of mathematical ideas is appearing regarding how data from different imaging modalities or channels can be synergistically combined in the image reconstruction process, exploiting structural and/or functional correlations between the multiple images. Here we review these developments, give pointers to important challenges and provide an outlook as to how the field may develop in the forthcoming years. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.

Estimation of the Chlorophyll-A Concentration of Algae Species Using Electrical Impedance Spectroscopy

Algae are a significant component of a biological monitoring program in an aquatic ecosystem. They are ideally suited for water quality assessments because of their nutrient requirements, rapid reproduction rate, and very short life cycle. Algae composition and temporal variation in abundances are important in determining the trophic level of lakes, and those can be estimated by the Chlorophyll-a (Chl-a) concentration of the species. In this work, a non-destructive method was employed to estimate the Chlorophyll-a concentration of multiple algae species using electrical impedance spectroscopy (EIS). The proposed EIS method is rapid, cheaper, and suitable for in situ measurements compared with the other available non-destructive methods, such as spectrophotometry and hyperspectral or multispectral imaging. The electrical impedances in different frequencies ranging from 1 to 100 kHz were observed using an impedance converter system. Significant observations were identified within 3.5 kHz for multiple algae species and therefore reported in the results. A positive correlation was found between the Chlorophyll-a and the measured impedance of algae species at different frequencies. Later, EIS models were developed for the species in 1–3.5 kHz. A correlation of 90% was found by employing a least squares method and multiple linear regression. The corresponding coefficients of determination were obtained as 0.9, 0.885, and 0.915, respectively for 49 samples of Spirulina, 41 samples of Chlorella, and 26 samples of mixed algae species. The models were later validated using a new and separate set of samples of algae species.

Model Selection Based Algorithm in Neonatal Chest EIT

This paper presents a new method for selecting a patient specific forward model to compensate for anatomical variations in electrical impedance tomography (EIT) monitoring of neonates. The method uses a combination of shape sensors and absolute reconstruction. It takes advantage of a probabilistic approach which automatically selects the best estimated forward model fit from pre-stored library models. Absolute/static image reconstruction is performed as the core of the posterior probability calculations. The validity and reliability of the algorithm in detecting a suitable model in the presence of measurement noise is studied with simulated and measured data from 11 patients. The paper also demonstrates the potential improvements on the clinical parameters extracted from EIT images by considering a unique case study with a neonate patient undergoing computed tomography imaging as clinical indication prior to EIT monitoring. Two well-known image reconstruction techniques, namely GREIT and tSVD, are implemented to create the final tidal images. The impacts of appropriate model selection on the clinical extracted parameters such as center of ventilation and silent spaces are investigated. The results show significant improvements to the final reconstructed images and more importantly to the clinical EIT parameters extracted from the images that are crucial for decision-making and further interventions.

A Review on Electrical Impedance Tomography Spectroscopy

Electrical Impedance Tomography Spectroscopy (EITS) enables the reconstruction of material distributions inside an object based on the frequency-dependent characteristics of different substances. In this paper, we present a review of EITS focusing on physical principles of the technology, sensor geometries, existing measurement systems, reconstruction algorithms, and image representation methods. In addition, a novel imaging method is proposed which could fill some of the gaps found in the literature. As an example of an application, EITS of ice and water mixtures is used.

Multi-frequency symmetry difference electrical impedance tomography with machine learning for human stroke diagnosis

OBJECTIVE

Multi-frequency symmetry difference electrical impedance tomography (MFSD-EIT) can robustly detect and identify unilateral perturbations in symmetric scenes. Here, an investigation is performed to assess if the algorithm can be successfully applied to identify the aetiology of stroke with the aid of machine learning.

METHODS

Anatomically realistic four-layer finite element method models of the head based on stroke patient images are developed and used to generate EIT data over a 5 Hz-100 Hz frequency range with and without bleed and clot lesions present. Reconstruction generates conductivity maps of each head at each frequency. Application of a quantitative metric assessing changes in symmetry across the sagittal plane of the reconstructed image and over the frequency range allows lesion detection and identification. The algorithm is applied to both simulated and human (n = 34 subjects) data. A classification algorithm is applied to the metric value in order to differentiate between normal, haemorrhage and clot values.

MAIN RESULTS

An average accuracy of 85% is achieved when MFSD-EIT with support vector machines (SVM) classification is used to identify and differentiate bleed from clot in human data, with 77% accuracy when differentiating normal from stroke in human data.

CONCLUSION

Applying a classification algorithm to metrics derived from MFSD-EIT images is a novel and promising technique for detection and identification of perturbations in static scenes.

SIGNIFICANCE

The MFSD-EIT algorithm used with machine learning gives promising results of lesion detection and identification in challenging conditions like stroke. The results imply feasible translation to human patients.

Bi-Frequency Symmetry Difference EIT-Feasibility and Limitations of Application to Stroke Diagnosis

OBJECTIVE

Bi-Frequency Symmetry Difference (BFSD)-EIT can detect, localize and identify unilateral perturbations in symmetric scenes. Here, we test the viability and robustness of BFSD-EIT in stroke diagnosis.

METHODS

A realistic 4-layer Finite Element Method (FEM) head model with and without bleed and clot lesions is developed. Performance is assessed with test parameters including: measurement noise, electrode placement errors, contact impedance errors, deviations in assumed tissue conductivity, deviations in assumed anatomy, and a frequency-dependent background. A final test is performed using ischemic patient data. Results are assessed using images and quantitative metrics.

RESULTS

BFSD-EIT may be feasible for stroke diagnosis if a signal-to-noise ratio (SNR) of ≥60 dB is achievable. Sensitivity to errors in electrode positioning is seen with a tolerance of only ±5 mm, but a tolerance of up to ±30 mm is possible if symmetry is maintained between symmetrically opposite partner electrodes. The technique is robust to errors in contact impedance and assumed tissue conductivity up to at least ±50%. Asymmetric internal anatomy affects performance but may be tolerable for tissues with frequency-dependent conductivity. Errors in assumed external geometry marginally affect performance. A frequency-dependent background does not affect performance with carefully chosen frequency points or use of multiple frequency points across a band. The Global Left-Hand Side (LHS) & Right-Hand Side (RHS) Mean Intensity metric is particularly robust to errors.

CONCLUSION

BFSD-EIT is a promising technique for stroke diagnosis, provided parameters are within the tolerated ranges.

SIGNIFICANCE

BFSD-EIT may prove an important step forward in imaging of static scenes such as stroke.

Capacitively Coupled Electrical Impedance Tomography for Brain Imaging

Electrical impedance tomography (EIT) is considered as a potential candidate for brain stroke imaging due to its compactness and potential use in bedside and emergency settings. The electrode-skin contact impedance and low conductivity of skull pose some practical challenges to the EIT head imaging. This paper studies the application of capacitively coupled electrical impedance tomography (CCEIT) in brain imaging for the first time. CCEIT is a new contactless EIT technique which uses voltage excitation without direct contact with the skin, as oppose to directly injecting the current to the skin in EIT. Because the safety issue of a new technique should be strictly treated, simulation work based on a simplified head model was carried out to investigate the safety aspects of CCEIT. By comparing with the standard EIT excited by a typical safe current level used in brain imaging, the safe excitation reference of CCEIT is obtained. This is done by comparing the maximum level of internal electrical field (internal current density) of EIT and that of CCEIT. Simulation results provide useful knowledge of excitation signal level of CCEIT and also show a critical comparison with traditional EIT. Practical experiments were carried out with a 12-electrode CCEIT phantom, saline, and carrot samples. Experimental results show the feasibility and potential of CCEIT for stroke imaging. In this paper, the anomaly diameter resolution is 10 mm (1/18 of the phantom diameter), which indicates that small-volume stroke could be detected. This is achieved by a low excitation voltage of 1 V, showing the possibility of even better performance when higher but yet safe level of excitation voltages is used.

A novel time-difference electrical impedance tomography algorithm using multi-frequency information

BACKGROUND

Electrical impedance tomography (EIT) is a noninvasive, radiation-free, and low-cost imaging modality for monitoring the conductivity distribution inside a patient. Nowadays, time-difference EIT (tdEIT) is used extensively as it has fast imaging speed and can reflect the dynamic changes of diseases, which make it attractive for a number of medical applications. Moreover, modeling errors are compensated to some extent by subtraction of voltage measurements collected before and after the change. However, tissue conductivity varies with frequency and tdEIT does not efficiently exploit multi-frequency information as it only uses measurements associated with a single frequency.

METHODS

This paper proposes a tdEIT algorithm that imposes spectral constraints on the framework of the linear least squares problem. Simulation and phantom experiments are conducted to compare the proposed spectral constraints algorithm (SC) with the damped least squares algorithm (DLS), which is a stable tdEIT algorithm used in clinical practice. The condition number and rank of the matrices needing inverses are analyzed, and image quality is evaluated using four indexes. The possibility of multi-tissue imaging and the influence of spectral errors are also explored.

RESULTS

Significant performance improvement is achieved by combining multi-frequency and time-difference information. The simulation results show that, in one-step iteration, both algorithms have the same condition number and rank, but SC effectively reduces image noise by 20.25% compared to DLS. In addition, deformation error and position error are reduced by 8.37% and 7.86%, respectively. In two-step iteration, the rank of SC is greatly increased, which suggests that more information is employed in image reconstruction. Image noise is further reduced by an average of 32.58%, and deformation error and position error are also reduced by 20.20% and 31.36%, respectively. The phantom results also indicate that SC has stronger noise suppression and target identification abilities, and this advantage is more obvious with iteration. The results of multi-tissue imaging show that SC has the unique advantage of automatically extracting a single tissue to image.

CONCLUSIONS

SC enables tdEIT to utilize multi-frequency information in cases where the spectral constraints are known and then provides higher quality images for applications.

Bi-frequency symmetry difference electrical impedance tomography-a novel technique for perturbation detection in static scenes

OBJECTIVE

A novel method for the imaging of static scenes using electrical impedance tomography (EIT) is reported with implementation and validation using numerical and phantom models. The technique is applicable to regions featuring symmetry in the normal case, asymmetry in the presence of a perturbation, and where there is a known, frequency-dependent change in the electrical conductivity of the materials in the region.

APPROACH

The stroke diagnostic problem is used as a motivating sample application. The head is largely symmetrical across the sagittal plane. A haemorrhagic or ischaemic lesion located away from the sagittal plane will alter this natural symmetry, resulting in a symmetrical imbalance that can be detected using EIT. Specifically, application of EIT stimulation and measurement protocols at two distinct frequencies detects deviations in symmetry if an asymmetrically positioned lesion is present, with subsequent identification and localisation of the perturbation based on known frequency-dependent conductivity changes. Anatomically accurate computational models are used to demonstrate the feasibility of the proposed technique using different types, sizes, and locations of lesions with frequency-dependent (or independent) conductivity. Further, a realistic experimental head phantom is used to validate the technique using frequency-dependent perturbations emulating the key numerical simulations.

MAIN RESULTS

Lesion presence, type, and location are detectable using this novel technique. Results are presented in the form of images and corresponding robust quantitative metrics. Better detection is achieved for larger lesions, those further from the sagittal plane, and when measurements have a higher signal-to-noise ratio.

SIGNIFICANCE

Bi-frequency symmetry difference EIT is an exciting new modality of EIT with the ability to detect deviations in the symmetry of a region that occur due to the presence of a lesion. Notably, this modality does not require a time change in the region and thus may be used in static scenarios such as stroke detection.

Low-cost multifrequency electrical impedance-based system (MFEIBS) for clinical imaging: design and performance evaluation

Electrical impedance tomography (EIT) is an upcoming and capable imaging modality used for clinical imaging. It is non-invasive, non-ionising and an inexpensive technique. This paper explains the designing and the analysis of a low-cost multifrequency electrical impedance-based system (MFEIBS) having a flexible mechanism of interfacing up to 32 electrodes, suitable for 1 kHz-2 MHz. Various indicators to check the performance of the EIT system were evaluated and presented here. The performance of VCO and VCCS was measured up to 2 MHz. SNR was measured with saline phantom and its mean value is 74 dB for the complete bandwidth. Different combinations of resistors and capacitors were used to find the accuracy of the system, and relative error was less than 0.55% for the entire range. CMRR of the system was calculated and it was found to be maximum 85 dB at 1 kHz frequency. A 16-electrode circular plastic phantom having a diameter of 18 cm was established and connected with a simple MFEIBS. Obtained surface potential was applied to the computer used for image formation using NI USB-6259, 16-bit, 1.25 MS/s M Series High-speed DAQ. Images reconstructed using the system presented in this paper was generated from a 16-electrode plastic phantom filled with NaCl up to 1.2 cm height.

Multi-frequency electrical impedance tomography and neuroimaging data in stroke patients

Electrical Impedance Tomography (EIT) is a non-invasive imaging technique, which has the potential to expedite the differentiation of ischaemic or haemorrhagic stroke, decreasing the time to treatment. Whilst demonstrated in simulation, there are currently no suitable imaging or classification methods which can be successfully applied to human stroke data. Development of these complex methods is hindered by a lack of quality Multi-Frequency EIT (MFEIT) data. To address this, MFEIT data were collected from 23 stroke patients, and 10 healthy volunteers, as part of a clinical trial in collaboration with the Hyper Acute Stroke Unit (HASU) at University College London Hospital (UCLH). Data were collected at 17 frequencies between 5 Hz and 2 kHz, with 31 current injections, yielding 930 measurements at each frequency. This dataset is the most comprehensive of its kind and enables combined analysis of MFEIT, Electroencephalography (EEG) and Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) data in stroke patients, which can form the basis of future research into stroke classification.

Addition of internal electrodes is beneficial for focused bioimpedance measurements in the lung

OBJECTIVE

Bioimpedance measurements such as bioimpedance spectroscopy (BIS) or electrical impedance tomography (EIT) are used in many biomedical applications. While BIS measures and analyzes the impedance in a frequency range at constant electrode positions, EIT aims to reconstruct images of the conductivity distribution from multiple measurements at different electrode positions. Our aim is to add spatial information to tetrapolar BIS measurements by using electrode positions that focus measurements on desired regions of interest. In this paper, we aim to investigate, whether internal electrodes that can be integrated into breathing or gastroesophageal tubes, can improve the local sensitivity of bioimpedance spectroscopy measurements.

APPROACH

We present the results of a simulation study, in which we investigated more than 4 M different electrode configurations on their ability to monitor specific regions of interest (ROI) in the lung. Based on the sensitivity, which describes the impact of a conductivity change on the measured impedance, we define three main criteria which we use to evaluate our simulation results: the selectivity [Formula: see text], which describes the impact of a conductivity change inside the region of interest compared to a conductivity change outside the ROI; the homogeneity [Formula: see text], which describes the distribution of the sensitivity inside the ROI; and the absolute impedance contribution ratio [Formula: see text], which describes the contribution of the ROI to the measured impedance.

MAIN RESULTS

Depending on the region of interest, electrode configurations using internal electrodes are between 9.8 % and 90 % better with respect to these criteria than configurations using external electrodes only.

SIGNIFICANCE

The combination of internal and external electrodes improves the focusing ability of tetrapolar impedance measurements on specific lung regions, which may be especially beneficial for lung monitoring in intensive care.

A Multitasking Electrical Impedance Tomography System Using Titanium Alloy Electrode

This paper presents a multitasking electrical impedance tomography (EIT) system designed to improve the flexibility and durability of an existing EIT system. The ability of the present EIT system to detect, locate, and reshape objects was evaluated by four different experiments. The results of the study show that the system can detect and locate an object with a diameter as small as 1.5 mm in a testing tank with a diameter of 134 mm. Moreover, the results demonstrate the ability of the current system to reconstruct an image of several dielectric object shapes. Based on the results of the experiments, the programmable EIT system can adapt the EIT system for different applications without the need to implement a new EIT system, which may help to save time and cost. The setup for all the experiments consisted of a testing tank with an attached 16-electrode array made of titanium alloy grade 2. The titanium alloy electrode was used to enhance EIT system's durability and lifespan.

Design of current source for multi-frequency simultaneous electrical impedance tomography

Multi-frequency electrical impedance tomography has been evolving from the frequency-sweep approach to the multi-frequency simultaneous measurement technique which can reduce measuring time and will be increasingly attractive for time-varying biological applications. The accuracy and stability of the current source are the key factors determining the quality of the image reconstruction. This article presents a field programmable gate array-based current source for a multi-frequency simultaneous electrical impedance tomography system. A novel current source circuit was realized by combining the classic current mirror based on the feedback amplifier AD844 with a differential topology. The optimal phase offsets of harmonic sinusoids were obtained through the crest factor analysis. The output characteristics of this current source were evaluated by simulation and actual measurement. The results include the following: (1) the output impedance was compared with one of the Howland pump circuit in simulation, showing comparable performance at low frequencies. However, the proposed current source makes lower demands for resistor tolerance but performs even better at high frequencies. (2) The output impedance in actual measurement below 200 kHz is above 1.3 MΩ and can reach 250 KΩ up to 1 MHz. (3) An experiment based on a biological RC model has been implemented. The mean error for the demodulated impedance amplitude and phase are 0.192% and 0.139°, respectively. Therefore, the proposed current source is wideband, biocompatible, and high precision, which demonstrates great potential to work as a sub-system in the multi-frequency electrical impedance tomography system.

In Vivo Bioimpedance Spectroscopy Characterization of Healthy, Hemorrhagic and Ischemic Rabbit Brain within 10 Hz-1 MHz

Acute stroke is a serious cerebrovascular disease and has been the second leading cause of death worldwide. Conventional diagnostic modalities for stroke, such as CT and MRI, may not be available in emergency settings. Hence, it is imperative to develop a portable tool to diagnose stroke in a timely manner. Since there are differences in impedance spectra between normal, hemorrhagic and ischemic brain tissues, multi-frequency electrical impedance tomography (MFEIT) shows great promise in detecting stroke. Measuring the impedance spectra of healthy, hemorrhagic and ischemic brain in vivo is crucial to the success of MFEIT. To our knowledge, no research has established hemorrhagic and ischemic brain models in the same animal and comprehensively measured the in vivo impedance spectra of healthy, hemorrhagic and ischemic brain within 10 Hz–1 MHz. In this study, the intracerebral hemorrhage and ischemic models were established in rabbits, and then the impedance spectra of healthy, hemorrhagic and ischemic brain were measured in vivo and compared. The results demonstrated that the impedance spectra differed significantly between healthy and stroke-affected brain (i.e., hemorrhagic or ischemic brain). Moreover, the rate of change in brain impedance following hemorrhagic and ischemic stroke with regard to frequency was distinct. These findings further validate the feasibility of using MFEIT to detect stroke and differentiate stroke types, and provide data supporting for future research.

A Versatile and Reproducible Multi-Frequency Electrical Impedance Tomography System

A highly versatile Electrical Impedance Tomography (EIT) system, nicknamed the ScouseTom, has been developed. The system allows control over current amplitude, frequency, number of electrodes, injection protocol and data processing. Current is injected using a Keithley 6221 current source, and voltages are recorded with a 24-bit EEG system with minimum bandwidth of 3.2 kHz. Custom PCBs interface with a PC to control the measurement process, electrode addressing and triggering of external stimuli. The performance of the system was characterised using resistor phantoms to represent human scalp recordings, with an SNR of 77.5 dB, stable across a four hour recording and 20 Hz to 20 kHz. In studies of both haeomorrhage using scalp electrodes, and evoked activity using epicortical electrode mats in rats, it was possible to reconstruct images matching established literature at known areas of onset. Data collected using scalp electrode in humans matched known tissue impedance spectra and was stable over frequency. The experimental procedure is software controlled and is readily adaptable to new paradigms. Where possible, commercial or open-source components were used, to minimise the complexity in reproduction. The hardware designs and software for the system have been released under an open source licence, encouraging contributions and allowing for rapid replication.

The Frequency Spectral Properties of Electrode-Skin Contact Impedance on Human Head and Its Frequency-Dependent Effects on Frequency-Difference EIT in Stroke Detection from 10Hz to 1MHz

Frequency-difference electrical impedance tomography (fdEIT) reconstructs frequency-dependent changes of a complex impedance distribution. It has a potential application in acute stroke detection because there are significant differences in impedance spectra between stroke lesions and normal brain tissues. However, fdEIT suffers from the influences of electrode-skin contact impedance since contact impedance varies greatly with frequency. When using fdEIT to detect stroke, it is critical to know the degree of measurement errors or image artifacts caused by contact impedance. To our knowledge, no study has systematically investigated the frequency spectral properties of electrode-skin contact impedance on human head and its frequency-dependent effects on fdEIT used in stroke detection within a wide frequency band (10 Hz-1 MHz). In this study, we first measured and analyzed the frequency spectral properties of electrode-skin contact impedance on 47 human subjects’ heads within 10 Hz-1 MHz. Then, we quantified the frequency-dependent effects of contact impedance on fdEIT in stroke detection in terms of the current distribution beneath the electrodes and the contact impedance imbalance between two measuring electrodes. The results showed that the contact impedance at high frequencies (>100 kHz) significantly changed the current distribution beneath the electrode, leading to nonnegligible errors in boundary voltages and artifacts in reconstructed images. The contact impedance imbalance at low frequencies (<1 kHz) also caused significant measurement errors. We conclude that the contact impedance has critical frequency-dependent influences on fdEIT and further studies on reducing such influences are necessary to improve the application of fdEIT in stroke detection.

A novel multi-frequency electrical impedance tomography spectral imaging algorithm for early stroke detection

Multi-frequency electrical impedance tomography (MFEIT) reconstructs the image of conductivity inside the human body based on the dependence of tissue conductivity on frequency. As there exist differences in the conductivity over frequency between blood, ischemic cortical tissue and normal cortical tissue, MFEIT has potential application in the detection of acute stroke. However, because the conductivity distribution of the human head is highly inhomogeneous and the conductivities of normal head tissue and stroke lesion tissue both change with frequency, the anomaly and normal head tissues are often mixed together in the reconstructed image, which makes it difficult to discern the anomaly. Here we present a spectral decomposition frequency-difference (SD-FD) imaging algorithm in an attempt to address this issue: firstly, we reconstruct so-called EIT spectral images according to the conductivity spectra of tissues; secondly, we obtain the EIT image of the anomaly from the spectral images by using independent component analysis. The results show that the proposed algorithm is capable of detecting the anomaly in a numerical head phantom, as well as in a realistic human head tank with frequency-dependent and heterogeneous conductivities distribution. The proposed SD-FD algorithm may support MFEIT use for human stroke imaging in the future.

Ex-Vivo Characterization of Bioimpedance Spectroscopy of Normal, Ischemic and Hemorrhagic Rabbit Brain Tissue at Frequencies from 10 Hz to 1 MHz

Stroke is a severe cerebrovascular disease and is the second greatest cause of death worldwide. Because diagnostic tools (CT and MRI) to detect acute stroke cannot be used until the patient reaches the hospital setting, a portable diagnostic tool is urgently needed. Because biological tissues have different impedance spectra under normal physiological conditions and different pathological states, multi-frequency electrical impedance tomography (MFEIT) can potentially detect stroke. Accurate impedance spectra of normal brain tissue (gray and white matter) and stroke lesions (ischemic and hemorrhagic tissue) are important elements when studying stroke detection with MFEIT. To our knowledge, no study has comprehensively measured the impedance spectra of normal brain tissue and stroke lesions for the whole frequency range of 1 MHz within as short as possible an ex vivo time and using the same animal model. In this study, we established intracerebral hemorrhage and ischemic models in rabbits, then measured and analyzed the impedance spectra of normal brain tissue and stroke lesions ex vivo within 15 min after animal death at 10 Hz to 1 MHz. The results showed that the impedance spectra of stroke lesions significantly differed from those of normal brain tissue; the ratio of change in impedance of ischemic and hemorrhagic tissue with regard to frequency was distinct; and tissue type could be discriminated according to its impedance spectra. These findings further confirm the feasibility of detecting stroke with MFEIT and provide data supporting further study of MFEIT to detect stroke.

Improved electrode positions for local impedance measurements in the lung-a simulation study

Impedance spectroscopy can be used to analyze the dielectric properties of various materials. In the biomedical domain, it is used as bioimpedance spectroscopy (BIS) to analyze the composition of body tissue. Being a non-invasive, real-time capable technique, it is a promising modality, especially in the field of lung monitoring. Unfortunately, up to now, BIS does not provide any regional lung information as the electrodes are usually placed in hand-to-hand or transthoracic configurations. Even though transthoracic electrode configurations are in general capable of monitoring the lung, no focusing to specific regions is achieved. In order to resolve this issue, we use a finite element model (FEM) of the human body to study the effect of different electrode configurations on measured BIS data. We present evaluation results and show suitable electrode configurations for eight lung regions. We show that, using these optimized configurations, BIS measurements can be focused to desired regions allowing local lung analysis.

Correction of electrode modelling errors in multi-frequency EIT imaging

The differentiation of haemorrhagic from ischaemic stroke using electrical impedance tomography (EIT) requires measurements at multiple frequencies, since the general lack of healthy measurements on the same patient excludes time-difference imaging methods. It has previously been shown that the inaccurate modelling of electrodes constitutes one of the largest sources of image artefacts in non-linear multi-frequency EIT applications. To address this issue, we augmented the conductivity Jacobian matrix with a Jacobian matrix with respect to electrode movement. Using this new algorithm, simulated ischaemic and haemorrhagic strokes in a realistic head model were reconstructed for varying degrees of electrode position errors. The simultaneous recovery of conductivity spectra and electrode positions removed most artefacts caused by inaccurately modelled electrodes. Reconstructions were stable for electrode position errors of up to 1.5 mm standard deviation along both surface dimensions. We conclude that this method can be used for electrode model correction in multi-frequency EIT.

Correcting electrode modelling errors in EIT on realistic 3D head models

Electrical impedance tomography (EIT) is a promising medical imaging technique which could aid differentiation of haemorrhagic from ischaemic stroke in an ambulance. One challenge in EIT is the ill-posed nature of the image reconstruction, i.e., that small measurement or modelling errors can result in large image artefacts. It is therefore important that reconstruction algorithms are improved with regard to stability to modelling errors. We identify that wrongly modelled electrode positions constitute one of the biggest sources of image artefacts in head EIT. Therefore, the use of the Fréchet derivative on the electrode boundaries in a realistic three-dimensional head model is investigated, in order to reconstruct electrode movements simultaneously to conductivity changes. We show a fast implementation and analyse the performance of electrode position reconstructions in time-difference and absolute imaging for simulated and experimental voltages. Reconstructing the electrode positions and conductivities simultaneously increased the image quality significantly in the presence of electrode movement.

Multifrequency electrical impedance tomography with total variation regularization

Multifrequency electrical impedance tomography (MFEIT) reconstructs the distribution of conductivity by exploiting the dependence of tissue conductivity on frequency. MFEIT can be performed on a single instance of data, making it promising for applications such as stroke and cancer imaging, where it is not possible to obtain a 'baseline' measurement of healthy tissue. A nonlinear MFEIT algorithm able to reconstruct the volume fraction distribution of tissue rather than conductivities has been developed previously. For each volume, the fraction of a certain tissue should be either 1 or 0; this implies that the sharp changes of the fractions, representing the boundaries of tissue, contain all the relevant information. However, these boundaries are blurred by traditional regularization methods using [Formula: see text] norm. The total variation (TV) regularization can overcome this problem, but it is difficult to solve due to its non-differentiability. Because the fraction must be between 0 and 1, this imposes a constraint on the MFEIT method based on the fraction model. Therefore, a constrained optimization method capable of dealing with non-differentiable problems is required. Based on the primal and dual interior point method, we propose a new constrained TV regularized method to solve the fraction reconstruction problem. The noise performance of the new MFEIT method is analysed using simulations on a 2D cylindrical mesh. Convergence performance is also analysed through experiments using a cylindrical tank. Finally, simulations on an anatomically realistic head-shaped mesh are demonstrated. The proposed MFEIT method with TV regularization shows higher spatial resolution, particularly at the edges of the perturbation, and stronger noise robustness, and its image noise and shape error are 20% to 30% lower than the traditional fraction method.

A Reconstruction-Classification Method for Multifrequency Electrical Impedance Tomography

Multifrequency Electrical Impedance Tomography is an imaging technique which distinguishes biological tissues by their unique conductivity spectrum. Recent results suggest that the use of spectral constraints can significantly improve image quality. We present a combined reconstruction-classification method for estimating the spectra of individual tissues, whilst simultaneously reconstructing the conductivity. The advantage of this method is that a priori knowledge of the spectra is not required to be exact in that the constraints are updated at each step of the reconstruction. In this paper, we investigate the robustness of the proposed method to errors in the initial guess of the tissue spectra, and look at the effect of introducing spatial smoothing. We formalize and validate a frequency-difference variant of reconstruction-classification, and compare the use of absolute and frequency-difference data in the case of a phantom experiment.

Comparison of total variation algorithms for electrical impedance tomography

The applications of total variation (TV) algorithms for electrical impedance tomography (EIT) have been investigated. The use of the TV regularisation technique helps to preserve discontinuities in reconstruction, such as the boundaries of perturbations and sharp changes in conductivity, which are unintentionally smoothed by traditional l2 norm regularisation. However, the non-differentiability of TV regularisation has led to the use of different algorithms. Recent advances in TV algorithms such as the primal dual interior point method (PDIPM), the linearised alternating direction method of multipliers (LADMM) and the spilt Bregman (SB) method have all been demonstrated successful EIT applications, but no direct comparison of the techniques has been made. Their noise performance, spatial resolution and convergence rate applied to time difference EIT were studied in simulations on 2D cylindrical meshes with different noise levels, 2D cylindrical tank and 3D anatomically head-shaped phantoms containing vegetable material with complex conductivity. LADMM had the fastest calculation speed but worst resolution due to the exclusion of the second-derivative; PDIPM reconstructed the sharpest change in conductivity but with lower contrast than SB; SB had a faster convergence rate than PDIPM and the lowest image errors.

Detection of admittivity anomaly on high-contrast heterogeneous backgrounds using frequency difference EIT

This paper describes a multiple background subtraction method in frequency difference electrical impedance tomography (fdEIT) to detect an admittivity anomaly from a high-contrast background conductivity distribution. The proposed method expands the use of the conventional weighted frequency difference EIT method, which has been used limitedly to detect admittivity anomalies in a roughly homogeneous background. The proposed method can be viewed as multiple weighted difference imaging in fdEIT. Although the spatial resolutions of the output images by fdEIT are very low due to the inherent ill-posedness, numerical simulations and phantom experiments of the proposed method demonstrate its feasibility to detect anomalies. It has potential application in stroke detection in a head model, which is highly heterogeneous due to the skull.

An image reconstruction algorithm for 3-D electrical impedance mammography

The Sussex MK4 electrical impedance mammography system is especially designed for 3-D breast screening. It aims to diagnose breast cancer at an early stage when it is most treatable. Planar electrodes are employed in this system. The challenge with planar electrodes is the inaccuracy and poor sensitivity in the vertical direction for 3-D imaging. An enhanced image reconstruction algorithm using a duo-mesh method is proposed to improve the vertical accuracy and sensitivity. The novel part of the enhanced image reconstruction algorithm is the correction term. To evaluate the new algorithm, an image processing based error analysis method is presented, which not only can precisely assess the error of the reconstructed image but also locate the center and outline the center and outline the shape of the objects of interest. Although the enhanced image reconstruction algorithm and the image processing based error analysis method are designed for the Sussex MK4 system, they are applicable to all electrical impedance tomography systems, regardless of the hardware design. To validate the enhanced algorithm, performance results from simulations, phantoms and patients are presented.