Imaging fast electrical activity in the brain with electrical impedance tomography

Image Reconstruction in Electrical Impedance Tomography Based on Structure-Aware Sparse Bayesian Learning

Electrical impedance tomography (EIT) is developed to investigate the internal conductivity changes of an object through a series of boundary electrodes, and has become increasingly attractive in a broad spectrum of applications. However, the design of optimal tomography image reconstruction algorithms has not achieved the adequate level of progress and matureness. In this paper, we propose an efficient and high-resolution EIT image reconstruction method in the framework of sparse Bayesian learning. Significant performance improvement is achieved by imposing structure-aware priors on the learning process to incorporate the prior knowledge that practical conductivity distribution maps exhibit clustered sparsity and intra-cluster continuity. The proposed method not only achieves high-resolution estimation and preserves the shape information even in low signal-to-noise ratio scenarios but also avoids the time-consuming parameter tuning process. The effectiveness of the proposed algorithm is validated through comparisons with state-of-the-art techniques using extensive numerical simulation and phantom experiment results.

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.

Model of Impedance Changes in Unmyelinated Nerve Fibers

OBJECTIVE

Currently, there is no imaging method that is able to distinguish the functional activity inside nerves. Such a method would be essential for understanding peripheral nerve physiology and would allow precise neuromodulation of organs these nerves supply. Electrical impedance tomography (EIT) is a method that produces images of electrical impedance change (dZ) of an object by injecting alternating current and recording surface voltages. It has been shown to be able to image fast activity in the brain and large peripheral nerves. To image inside small autonomic nerves, mostly containing unmyelinated fibers, it is necessary to maximize SNR and optimize the EIT parameters. An accurate model of the nerve is required to identify these optimal parameters as well as to validate data obtained in the experiments.

METHODS

In this study, we developed two three-dimensional models of unmyelinated fibers: Hodgkin-Huxley (HH) squid giant axon (single and multiple) and mammalian C-nociceptor. A coupling feedback system was incorporated into the models to simulate direct and alternating current application and simultaneously record external field during action potential propagation.

RESULTS

Parameters of the developed models were varied to study their influence on the recorded impedance changes; the optimal parameters were identified. The negative dZ was found to monotonically decrease with frequency for both HH and C fiber models, in accordance with the experimental data.

CONCLUSION AND SIGNIFICANCE

The accurate realistic model of unmyelinated nerve allows the optimization of EIT parameters and matches literature and experimental results.

Exploring the Potential of Electrical Impedance Tomography for Tissue Engineering Applications

In tissue engineering, cells are generally cultured in biomaterials to generate three-dimensional artificial tissues to repair or replace damaged parts and re-establish normal functions of the body. Characterizing cell growth and viability in these bioscaffolds is challenging, and is currently achieved by destructive end-point biological assays. In this study, we explore the potential to use electrical impedance tomography (EIT) as a label-free and non-destructive technology to assess cell growth and viability. The key challenge in the tissue engineering application is to detect the small change of conductivity associated with sparse cell distributions in regards to the size of the hosting scaffold, i.e., low volume fraction, until they assemble into a larger tissue-like structure. We show proof-of-principle data, measure cells within both a hydrogel and a microporous scaffold with an ad-hoc EIT equipment, and introduce the frequency difference technique to improve the reconstruction.

Low-Frequency Conductivity Tensor Imaging of the Human Head In Vivo Using DT-MREIT: First Study

We present the first in vivo images of anisotropic conductivity distribution in the human head, measured at a frequency of approximately 10 Hz. We used magnetic resonance electrical impedance tomography techniques to encode phase changes caused by current flow within the head via two independent electrode pairs. These results were then combined with diffusion tensor imaging data to reconstruct full anisotropic conductivity distributions in 5-mm-thick slices of the brains of two participants. Conductivity values recovered in this paper were broadly consistent with literature values. We anticipate that this technique will be of use in many areas of neuroscience, most importantly in functional imaging via inverse electroencephalogram. Future studies will involve pulse sequence acceleration to maximize brain coverage and resolution.

Phase division multiplexed EIT for enhanced temporal resolution

OBJECTIVE

The most commonly used EIT paradigm (time division multiplexing) limits the temporal resolution of impedance images due to the need to switch between injection electrodes. Advances have previously been made using frequency division multiplexing (FDM) to increase temporal resolution, but in cases where a fixed range of frequencies is available, such as imaging fast neural activity, an upper limit is placed on the total number of simultaneous injections. The use of phase division multiplexing (PDM) where multiple out of phase signals can be injected at each frequency is investigated to increase temporal resolution.

APPROACH

TDM, FDM and PDM were compared in head tank experiments, to compare transfer impedance measurements and spatial resolution between the three techniques. A resistor phantom paradigm was established to investigate the imaging of one-off impedance changes, of magnitude 1% and with durations as low as 500 µs (similar to those seen in nerve bundles), using both PDM and TDM approaches.

MAIN RESULTS

In head tank experiments, a strong correlation (r  >  0.85 and p  <  0.001) was present between the three sets of measured transfer impedances, and no statistically significant difference was found in reconstructed image quality. PDM was able to image impedance changes down to 500 µs in the phantom experiments, while the minimum duration imaged using TDM was 5 ms.

SIGNIFICANCE

PDM offers a possible solution to the imaging of fast moving impedance changes (such as in nerves), where the use of triggering or coherent averaging is not possible. The temporal resolution presents an order of magnitude improvement of the TDM approach, and the approach addresses the limited spatial resolution of FDM by increasing the number of simultaneous EIT injections.

A Parametric Level Set Method for Electrical Impedance Tomography

This paper presents an image reconstruction method based on parametric level set (PLS) method using electrical impedance tomography. The conductivity to be reconstructed was assumed to be piecewise constant and the geometry of the anomaly was represented by a shape-based PLS function, which we represent using Gaussian radial basis functions (GRBF). The representation of the PLS function significantly reduces the number of unknowns, and circumvents many difficulties that are associated with traditional level set (TLS) methods, such as regularization, re-initialization and use of signed distance function. PLS reconstruction results shown in this article are some of the first ones using experimental EIT data. The performance of the PLS method was tested with water tank data for two-phase visualization and with simulations which demonstrate the most popular biomedical application of EIT: lung imaging. In addition, robustness studies of the PLS method w.r.t width of the Gaussian function and GRBF centers were performed on simulated lung imaging data. The experimental and simulation results show that PLS method has significant improvement in image quality compared with the TLS reconstruction.

Conductive Hydrogel Electrodes for Delivery of Long-Term High Frequency Pulses

Nerve block waveforms require the passage of large amounts of electrical energy at the neural interface for extended periods of time. It is desirable that such waveforms be applied chronically, consistent with the treatment of protracted immune conditions, however current metal electrode technologies are limited in their capacity to safely deliver ongoing stable blocking waveforms. Conductive hydrogel (CH) electrode coatings have been shown to improve the performance of conventional bionic devices, which use considerably lower amounts of energy than conventional metal electrodes to replace or augment sensory neuron function. In this study the application of CH materials was explored, using both a commercially available platinum iridium (PtIr) cuff electrode array and a novel low-cost stainless steel (SS) electrode array. The CH was able to significantly increase the electrochemical performance of both array types. The SS electrode coated with the CH was shown to be stable under continuous delivery of 2 mA square pulse waveforms at 40,000 Hz for 42 days. CH coatings have been shown as a beneficial electrode material compatible with long-term delivery of high current, high energy waveforms.

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

We describe a sequence of experiments performed in vitro to verify the existence of a new magnetic resonance imaging contrast - Magnetic Resonance Electrical Impedance Tomography (MREIT) -sensitive to changes in active membrane conductivity. We compared standard deviations in MREIT phase data from spontaneously active Aplysia abdominal ganglia in an artificial seawater background solution (ASW) with those found after treatment with an excitotoxic solution (KCl). We found significant increases in MREIT treatment cases, compared to control ganglia subject to extra ASW. This distinction was not found in phase images from the same ganglia using no imaging current. Further, significance and effect size depended on the amplitude of MREIT imaging current used. We conclude that our observations were linked to changes in cell conductivity caused by activity. Functional MREIT may have promise as a more direct method of functional neuroimaging than existing methods that image correlates of blood flow such as BOLD fMRI.

Feasibility study of imaging fast neural activity in retinal tissue using Electrical Impedance Tomography

Electrical Impedance Tomography has been recently applied to image fast neural activity in the somatosensory cerebral cortex. This non-invasive imaging modality has the unique advantage of high spatial-temporal resolution in millimeters over milliseconds. This work was designed to test an existing 32-channel EIT system, a modified UCL ScouseTom, and to discuss the feasibility of imaging neural activity in retinal tissue through computer simulation. The finite element method was used to model a retinal slice with a realistic conductivity-depth profile of the macaque eye. The conductivity perturbation was simulated in five different layers of the model. 5μV_RMS white noise was added to boundary voltages. Simulation results showed that it is feasible to apply EIT in retinal tissue but the injection current is near threshold of unwanted phosphenes induction. Therefore the suggested future work are validating threshold current through animal experiments, developing electrodes with low contact impedance and mitigating noise through averaging.

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.

Electrical Impedance Spectroscopy for Quality Assessment of Meat and Fish: A Review on Basic Principles, Measurement Methods, and Recent Advances

Electrical impedance spectroscopy (EIS), as an effective analytical technique for electrochemical system, has shown a wide application for food quality and safety assessment recently. Individual differences of livestock cause high variation in quality of raw meat and fish and their commercialized products. Therefore, in order to obtain the definite quality information and ensure the quality of each product, a fast and on-line detection technology is demanded to be developed to monitor product processing. EIS has advantages of being fast, nondestructive, inexpensive, and easily implemented and shows potential to develop on-line detecting instrument to replace traditional methods to realize time, cost, skilled persons saving and further quality grading. This review outlines the fundamental theories and two common measurement methods of EIS applied to biological tissue, summarizes its application specifically for quality assessment of meat and fish, and discusses challenges and future trends of EIS technology applied for meat and fish quality assessment.

Detection and Classification of Measurement Errors in Bioimpedance Spectroscopy

Bioimpedance spectroscopy (BIS) measurement errors may be caused by parasitic stray capacitance, impedance mismatch, cross-talking or their very likely combination. An accurate detection and identification is of extreme importance for further analysis because in some cases and for some applications, certain measurement artifacts can be corrected, minimized or even avoided. In this paper we present a robust method to detect the presence of measurement artifacts and identify what kind of measurement error is present in BIS measurements. The method is based on supervised machine learning and uses a novel set of generalist features for measurement characterization in different immittance planes. Experimental validation has been carried out using a database of complex spectra BIS measurements obtained from different BIS applications and containing six different types of errors, as well as error-free measurements. The method obtained a low classification error (0.33%) and has shown good generalization. Since both the features and the classification schema are relatively simple, the implementation of this pre-processing task in the current hardware of bioimpedance spectrometers is possible.

Empirical validation of statistical parametric mapping for group imaging of fast neural activity using electrical impedance tomography

Electrical impedance tomography (EIT) allows for the reconstruction of internal conductivity from surface measurements. A change in conductivity occurs as ion channels open during neural activity, making EIT a potential tool for functional brain imaging. EIT images can have  >10 000 voxels, which means statistical analysis of such images presents a substantial multiple testing problem. One way to optimally correct for these issues and still maintain the flexibility of complicated experimental designs is to use random field theory. This parametric method estimates the distribution of peaks one would expect by chance in a smooth random field of a given size. Random field theory has been used in several other neuroimaging techniques but never validated for EIT images of fast neural activity, such validation can be achieved using non-parametric techniques. Both parametric and non-parametric techniques were used to analyze a set of 22 images collected from 8 rats. Significant group activations were detected using both techniques (corrected p  <  0.05). Both parametric and non-parametric analyses yielded similar results, although the latter was less conservative. These results demonstrate the first statistical analysis of such an image set and indicate that such an analysis is an approach for EIT images of neural activity.