Electrical impedance tomography of human brain function using reconstruction algorithms based on the finite element method

Transcranial volumetric imaging using a conformal ultrasound patch

Accurate and continuous monitoring of cerebral blood flow is valuable for clinical neurocritical care and fundamental neurovascular research. Transcranial Doppler (TCD) ultrasonography is a widely used non-invasive method for evaluating cerebral blood flow1, but the conventional rigid design severely limits the measurement accuracy of the complex three-dimensional (3D) vascular networks and the practicality for prolonged recording2. Here we report a conformal ultrasound patch for hands-free volumetric imaging and continuous monitoring of cerebral blood flow. The 2 MHz ultrasound waves reduce the attenuation and phase aberration caused by the skull, and the copper mesh shielding layer provides conformal contact to the skin while improving the signal-to-noise ratio by 5 dB. Ultrafast ultrasound imaging based on diverging waves can accurately render the circle of Willis in 3D and minimize human errors during examinations. Focused ultrasound waves allow the recording of blood flow spectra at selected locations continuously. The high accuracy of the conformal ultrasound patch was confirmed in comparison with a conventional TCD probe on 36 participants, showing a mean difference and standard deviation of difference as -1.51 ± 4.34 cm s-1, -0.84 ± 3.06 cm s-1 and -0.50 ± 2.55 cm s-1 for peak systolic velocity, mean flow velocity, and end diastolic velocity, respectively. The measurement success rate was 70.6%, compared with 75.3% for a conventional TCD probe. Furthermore, we demonstrate continuous blood flow spectra during different interventions and identify cascades of intracranial B waves during drowsiness within 4 h of recording.

Research Trends and Hotspots of Medical Electrical Impedance Tomography Algorithms: A Bibliometric Analysis From 1987 to 2021

Electrical impedance tomography (EIT) is a gradually maturing medical imaging technique that relies on computational algorithms for reconstructing and visualizing internal conductivity distributions within the human body. To provide a comprehensive and objective understanding of the current state and trends in the EIT algorithm research, we conducted bibliometric analysis on a 25-year EIT algorithm research dataset sourced from Web of Science Core Collections. We visualized publication characteristics, collaboration patterns, keywords, and co-cited references. The results indicate a steady increase in annual publications over recent decades. The United States, United Kingdom, China, and South Korea contributed 60% of the articles collaboratively. Keyword analysis unveiled three distinct stages in the evolution of EIT algorithm research: the establishment of fundamental algorithm frameworks, optimization for improved imaging performance, and the development of algorithms for clinical applications. Additionally, there has been a shift in research focus from traditional theories to the incorporation of new methods, such as artificial intelligence. Co-cited references suggest that integrating EIT with other established imaging techniques may emerge as a new trend in EIT algorithm research. In summary, EIT algorithms have been a consistent research focus, with current efforts centered on optimizing algorithms to enhance imaging performance. The emerging research trend involves utilizing more diverse and intersecting algorithms.

Fast absolute 3D CGO-based electrical impedance tomography on experimental tank data

Objective.To present the first 3D CGO-based absolute EIT reconstructions from experimental tank data.Approach.CGO-based methods for absolute EIT imaging are compared to traditional TV regularized non-linear least squares reconstruction methods. Additional robustness testing is performed by considering incorrect modeling of domain shape.Main Results.The CGO-based methods are fast, and show strong robustness to incorrect domain modeling comparable to classic difference EIT imaging and fewer boundary artefacts than the TV regularized non-linear least squares reference reconstructions.Significance.This work is the first to demonstrate fully 3D CGO-based absolute EIT reconstruction on experimental data and also compares to TV-regularized absolute reconstruction. The speed (1-5 s) and quality of the reconstructions is encouraging for future work in absolute EIT.

Novel Electrode Placement in Electrical Bioimpedance-Based Stroke Detection: Effects on Current Penetration and Injury Characterization in a Finite Element Model

OBJECTIVE

Reducing time-to-treatment and providing acute management in stroke are essential for patient recovery. Electrical bioimpedance (EBI) is an inexpensive and non-invasive tissue measurement approach that has the potential to provide novel continuous intracranial monitoring-something not possible in current standard-of-care. While extensive previous work has evaluated the feasibility of EBI in diagnosing stroke, high-impedance anatomical features in the head have limited clinical translation.

METHODS

The present study introduces novel electrode placements near highly-conductive cerebral spinal fluid (CSF) pathways to enhance electrical current penetration through the skull and increase detection accuracy of neurologic damage. Simulations were conducted on a realistic finite element model (FEM). Novel electrode placements at the tear ducts, soft palate and base of neck were evaluated. Classification accuracy was assessed in the presence of signal noise, patient variability, and electrode positioning.

RESULTS

Algorithms were developed to successfully determine stroke etiology, location, and size relative to impedance measurements from a baseline scan. Novel electrode placements significantly increased stroke classification accuracy at various levels of signal noise (e.g., p < 0.001 at 40 dB). Novel electrodes also amplified current penetration, with up to 30% increase in current density and 57% increased sensitivity in central intracranial regions (p < 0.001).

CONCLUSION

These findings support the use of novel electrode placements in EBI to overcome prior limitations, indicating a potential approach to increasing the technology's clinical utility in stroke identification.

SIGNIFICANCE

A non-invasive EBI monitor for stroke could provide essential timely intervention and care of stroke patients.

Mini Review: Impedance Measurement in Neuroscience and Its Prospective Application in the Field of Surgical Neurooncology

Impedance measurement of human tissue can be performed either in vivo or ex vivo. The majority of the in-vivo approaches are non-invasive, and few are invasive. To date, there is no gold standard for impedance measurement of intracranial tissue. In addition, most of the techniques addressing this topic are still experimental and have not found their way into clinical practice. This review covers available impedance measurement approaches in the neuroscience in general and specifically addresses recent advances made in the application of impedance measurement in the field of surgical neurooncology. It will provide an understandable picture on impedance measurement and give an overview of limitations that currently hinders clinical application and require future technical and conceptual solutions.

The Research Progress of Electrical Impedance Tomography for Lung Monitoring

Medical imaging can intuitively show people the internal structure, morphological information, and organ functions of the organism, which is one of the most important inspection methods in clinical medical diagnosis. Currently used medical imaging methods can only be applied to some diagnostic occasions after qualitative lesions have been generated, and the general imaging technology is usually accompanied by radiation and other conditions. However, electrical impedance tomography has the advantages of being noninvasive and non-radiative. EIT (Electrical Impedance Tomography) is also widely used in the early diagnosis and treatment of some diseases because of these advantages. At present, EIT is relatively mature and more and more image reconstruction algorithms are used to improve imaging resolution. Hardware technology is also developing rapidly, and the accuracy of data collection and processing is continuously improving. In terms of clinical application, EIT has also been used for pathological treatment of lungs, the brain, and the bladder. In the future, EIT has a good application prospect in the medical field, which can meet the needs of real-time, long-term monitoring and early diagnosis. Aiming at the application of EIT in the treatment of lung pathology, this article reviews the research progress of EIT, image reconstruction algorithms, hardware system design, and clinical applications used in the treatment of lung diseases. Through the research and introduction of several core components of EIT technology, it clarifies the characteristics of EIT system complexity and its solutions, provides research ideas for subsequent research, and once again verifies the broad development prospects of EIT technology in the future.

Supervised Descent Learning for Thoracic Electrical Impedance Tomography

OBJECTIVE

The absolute image reconstruction problem of electrical impedance tomography (EIT) is ill-posed. Traditional methods usually solve a nonlinear least squares problem with some kind of regularization. These methods suffer from low accuracy, poor anti-noise performance, and long computation time. Besides, the integration of a priori information is not very flexible. This work tries to solve EIT inverse problem using a machine learning algorithm for the application of thorax imaging.

METHODS

We developed the supervised descent learning EIT (SDL-EIT) inversion algorithm based on the idea of supervised descent method (SDM). The algorithm approximates the mapping from measured data to the conductivity image by a series of descent directions learned from training samples. We designed a training data set in which the thorax contour, and some general structure of lungs, and heart are embedded. The algorithm is implemented in both two-, and three-dimensional cases, and is evaluated using synthetic, and measured thoracic data. Results, and conclusion: For synthetic data, SDL-EIT shows better accuracy, and anti-noise performance compared with traditional Gauss-Newton inversion (GNI) method. For measured data, the result of SDL-EIT is reasonable compared with computed tomography (CT) scan image.

SIGNIFICANCE

Using SDL-EIT, prior information can be easily integrated through the specifically designed training data set, and the image reconstruction process can be accelerated. The algorithm is effective in inverting measured thoracic data. It is a potential algorithm for human thorax imaging.

Towards a thoracic conductive phantom for EIT

Phantom experiments are a crucial step for testing new hardware or imaging algorithms for electrical impedance tomography (EIT) studies. However, constructing an accurate phantom for EIT research remains critical; some studies have attempted to model the skull and breasts, and even fewer, as yet, have considered the thorax. In this study, a critical comparison between the electrical properties (impedance) of three materials is undertaken: a polyurethane foam, a silicone mixture and a thermoplastic polyurethane filament. The latter was identified as the most promising material and adopted for the development of a flexible neonatal torso. The validation is performed by the EIT image reconstruction of the air filled cavities, which mimic the lung regions. The methodology is reproducible for the creation of any phantom that requires a slight flexibility.

Comparison of electrical impedance tomography and intracranial pressure during dehydration treatment of cerebral edema

Cerebral edema after brain injury can lead to brain damage and death if diagnosis and treatment are delayed. This study investigates the feasibility of employing electrical impedance tomography (EIT) as a non-invasive imaging tool for monitoring the development of cerebral edema, in which impedance imaging of the brain related to brain water content is compared with intracranial pressure (ICP). We enrolled forty patients with cerebral hemorrhage who underwent lateral external ventricular drain with intraventricular ICP and EIT monitoring for 3 h after initiation of dehydration treatment. The average reconstructed impedance value (ARV) calculated from EIT images was compared with ICP. Dehydration effects induced changes in ARV and ICP showed a close negative correlation in all patients, and the mean correlation reached R2 = 0.78 ± 0.16 (p < .001). A regression equation (R2 = 0.62, p < .001) was formulated from the total of measurement data. The 95% limits of agreement were - 6.13 to 6.13 mmHg. Adaptive clustering and variance analysis of normalized changes in ARV and ICP showed 92.5% similarity and no statistically significant differences (p > .05). Moreover, the sensitivity, specificity and area under the curve of changes in ICP >10 mmHg were 0.65, 0.73 and 0.70 respectively. The findings show that EIT can monitor changes in brain water content associated with cerebral edema, which could provide a real-time and non-invasive imaging tool for early identification of cerebral edema and the evaluation of mannitol dehydration.

Three-Dimensional Electrical Impedance Tomography With Multiplicative Regularization

OBJECTIVE

The multiplicative regularization scheme is applied to three-dimensional electrical impedance tomography (EIT) image reconstruction problem to alleviate its ill-posedness.

METHODS

A cost functional is constructed by multiplying the data misfit functional with the regularization functional. The regularization functional is based on a weighted L²-norm with the edge-preserving characteristic. Gauss-Newton method is used to minimize the cost functional. A method based on the discrete exterior calculus (DEC) theory is introduced to formulate the discrete gradient and divergence operators related to the regularization on unstructured meshes.

RESULTS

Both numerical and experimental results show good reconstruction accuracy and anti-noise performance of the algorithm. The reconstruction results using human thoracic data show promising applications in thorax imaging.

CONCLUSION

The multiplicative regularization can be applied to EIT image reconstruction with promising applications in thorax imaging.

SIGNIFICANCE

In the multiplicative regularization scheme, there is no need to set an artificial regularization parameter in the cost functional. This helps to reduce the workload related to choosing a regularization parameter which may require expertise and many numerical experiments. The DEC-based method provides a systematic and rigorous way to formulate operators on unstructured meshes. This may help EIT image reconstructions using regularizations imposing structural or spatial constraints.

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.

Unveiling the development of intracranial injury using dynamic brain EIT: an evaluation of current reconstruction algorithms

OBJECTIVE

Dynamic brain electrical impedance tomography (EIT) is a promising technique for continuously monitoring the development of cerebral injury. While there are many reconstruction algorithms available for brain EIT, there is still a lack of study to compare their performance in the context of dynamic brain monitoring.

APPROACH

To address this problem, we develop a framework for evaluating different current algorithms with their ability to correctly identify small intracranial conductivity changes. Firstly, a simulation 3D head phantom with realistic layered structure and impedance distribution is developed. Next several reconstructing algorithms, such as back projection (BP), damped least-square (DLS), Bayesian, split Bregman (SB) and GREIT are introduced. We investigate their temporal response, noise performance, location and shape error with respect to different noise levels on the simulation phantom. The results show that the SB algorithm demonstrates superior performance in reducing image error. To further improve the location accuracy, we optimize SB by incorporating the brain structure-based conductivity distribution priors, in which differences of the conductivities between different brain tissues and the inhomogeneous conductivity distribution of the skull are considered. We compare this novel algorithm (called SB-IBCD) with SB and DLS using anatomically correct head shaped phantoms with spatial varying skull conductivity. Main results and Significance: The results showed that SB-IBCD is the most effective in unveiling small intracranial conductivity changes, where it can reduce the image error by an average of 30.0% compared to DLS.

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.

A fast time-difference inverse solver for 3D EIT with application to lung imaging

A class of sparse optimization techniques that require solely matrix-vector products, rather than an explicit access to the forward matrix and its transpose, has been paid much attention in the recent decade for dealing with large-scale inverse problems. This study tailors application of the so-called Gradient Projection for Sparse Reconstruction (GPSR) to large-scale time-difference three-dimensional electrical impedance tomography (3D EIT). 3D EIT typically suffers from the need for a large number of voxels to cover the whole domain, so its application to real-time imaging, for example monitoring of lung function, remains scarce since the large number of degrees of freedom of the problem extremely increases storage space and reconstruction time. This study shows the great potential of the GPSR for large-size time-difference 3D EIT. Further studies are needed to improve its accuracy for imaging small-size anomalies.

Contrast enhancement in EIT imaging of the brain

We consider electrical impedance tomography (EIT) imaging of the brain. The brain is surrounded by the poorly conducting skull which has low conductivity compared to the brain. The skull layer causes a partial shielding effect which leads to weak sensitivity for the imaging of the brain tissue. In this paper we propose an approach based on the Bayesian approximation error approach, to enhance the contrast in brain imaging. With this approach, both the (uninteresting) geometry and the conductivity of the skull are embedded in the approximation error statistics, which leads to a computationally efficient algorithm that is able to detect features such as internal haemorrhage with significantly increased sensitivity and specificity. We evaluate the approach with simulations and phantom data.

Impedance based automatic electrode positioning

The position of electrodes in electrical imaging and stimulation of the human brain is an important variable with vast influences on the precision in modeling approaches. Nevertheless, the exact position is obscured by many factors. 3-D Digitization devices can measure the distribution over the scalp surface but remain uncomfortable in application and often imprecise. We demonstrate a new approach that uses solely the impedance information between the electrodes to determine the geometric position. The algorithm involves multidimensional scaling to create a 3 dimensional space based on these impedances. The success is demonstrated in a simulation study. An average electrode position error of 1.67cm over all 6 subjects could be achieved.

Linearly constrained minimum variance spatial filtering for localization of conductivity changes in electrical impedance tomography

We localize dynamic electrical conductivity changes and reconstruct their time evolution introducing the spatial filtering technique to electrical impedance tomography (EIT). More precisely, we use the unit-noise-gain constrained variation of the distortionless-response linearly constrained minimum variance spatial filter. We address the effects of interference and the use of zero gain constraints. The approach is successfully tested in simulated and real tank phantoms. We compute the position error and resolution to compare the localization performance of the proposed method with the one-step Gauss-Newton reconstruction with Laplacian prior. We also study the effects of sensor position errors. Our results show that EIT spatial filtering is useful for localizing conductivity changes of relatively small size and for estimating their time-courses. Some potential dynamic EIT applications such as acute ischemic stroke detection and neuronal activity localization may benefit from the higher resolution of spatial filters as compared to conventional tomographic reconstruction algorithms.

A new head phantom with realistic shape and spatially varying skull resistivity distribution

Brain electrical impedance tomography (EIT) is an emerging method for monitoring brain injuries. To effectively evaluate brain EIT systems and reconstruction algorithms, we have developed a novel head phantom that features realistic anatomy and spatially varying skull resistivity. The head phantom was created with three layers, representing scalp, skull, and brain tissues. The fabrication process entailed 3-D printing of the anatomical geometry for mold creation followed by casting to ensure high geometrical precision and accuracy of the resistivity distribution. We evaluated the accuracy and stability of the phantom. Results showed that the head phantom achieved high geometric accuracy, accurate skull resistivity values, and good stability over time and in the frequency domain. Experimental impedance reconstructions performed using the head phantom and computer simulations were found to be consistent for the same perturbation object. In conclusion, this new phantom could provide a more accurate test platform for brain EIT research.

Transrectal electrical impedance tomography of the prostate: spatially coregistered pathological findings for prostate cancer detection

PURPOSE

Prostate cancer ranks as one of the most common malignancies and currently represents the second leading cancer-specific cause of death in men. The current use of single modality transrectal ultrasound (TRUS) for biopsy guidance has a limited sensitivity and specificity for accurately identifying cancerous lesions within the prostate. This study introduces a novel prostate cancer imaging method that combines TRUS with electrical impedance tomography (EIT) and reports on initial clinical findings based on in vivo measurements.

METHODS

The ultrasound system provides anatomic information, which guides EIT image reconstruction. EIT reconstructions are correlated with semiquantitative pathological findings. Thin plate spline warping transformations are employed to overlay electrical impedance images and pathological maps describing the spatial distribution of prostate cancer, with the latter used as reference for data analysis. Clinical data were recorded from a total of 50 men prior to them undergoing radical prostatectomy for prostate cancer treatment. Student's t-tests were employed to statistically examine the electrical property difference between cancerous tissue and benign tissue as defined through histological assessment of the excised gland.

RESULTS

Example EIT reconstructions are presented along with a statistical analysis comparing EIT and pathology. An average transformation error of 1.67% is found when 381 spatially coregistered pathological images are compared with their target EIT reconstructed counterparts. At EIT signal frequencies of 0.4, 3.2, and 25.6 kHz, paired-testing demonstrated that the conductivity of cancerous regions is significantly greater than that of benign regions ( p < 0.0304).

CONCLUSIONS

These preliminary clinical findings suggest the potential benefits electrical impedance measurements might have for prostate cancer detection.

In vivo imaging of twist drill drainage for subdural hematoma: a clinical feasibility study on electrical impedance tomography for measuring intracranial bleeding in humans

Intracranial bleeding is one of the most severe medical emergencies in neurosurgery. Early detection or diagnosis would largely reduce the rate of disability and mortality, and improve the prognosis of the patients. Electrical Impedance Tomography (EIT) can non-invasively image the internal resistivity distribution within a human body using a ring of external electrodes, and is thus a promising technique to promptly detect the occurrence of intracranial bleedings because blood differs from other brain tissues in resistivity. However, so far there is no experimental study that has determined whether the intracranial resistivity changes in humans could be repeatedly detected and imaged by EIT. Hence, we for the first time attempt to clinically validate this by in vivo imaging the influx and efflux of irrigating fluid (5% dextrose in water, D5W) during the twist-drill drainage operation for the patients with subdural hematoma (SDH). In this study, six patients (four male, two female) with subacute or chronic SDH received the surgical operation in order to evacuate the hematoma around subdural region, and EIT measurements were performed simultaneously on each patient's head. The results showed that the resistivity significantly increased on the corresponding position of EIT images during the influx of D5W and gradually decreased back to baseline during the efflux. In the quantitative analysis, the average resistivity values demonstrated the similar results and had highly linear correlation (R(2) = 0.93 ± 0.06) with the injected D5W volumes, as well as the area of the resistivity gain(R(2) = 0.94 ± 0.05). In conclusion, it was clinically validated that intracranial resistivity changes in humans were detectable and quantifiable by the EIT method. After further technical improvements, EIT has the great potential of being a routine neuroimaging tool for early detection of intracranial bleedings.

Comparison of frequency difference reconstruction algorithms for the detection of acute stroke using EIT in a realistic head-shaped tank

Imaging of acute stroke might be possible using multi-frequency electrical impedance tomography (MFEIT) but requires absolute or frequency difference imaging. Simple linear frequency difference reconstruction has been shown to be ineffective in imaging with a frequency-dependant background conductivity; this has been overcome with a weighted frequency difference approach with correction for the background but this has only been validated for a cylindrical and hemispherical tank. The feasibility of MFEIT for imaging of acute stroke in a realistic head geometry was examined by imaging a potato perturbation against a saline background and a carrot-saline frequency-dependant background conductivity, in a head-shaped tank with the UCLH Mk2.5 MFEIT system. Reconstruction was performed with time difference (TD), frequency difference (FD), FD adjacent (FDA), weighted FD (WFD) and weighted FDA (WFDA) linear algorithms. The perturbation in reconstructed images corresponded to the true position to <9.5% of image diameter with an image SNR of >5.4 for all algorithms in saline but only for TD, WFDA and WFD in the carrot-saline background. No reliable imaging was possible with FD and FDA. This indicates that the WFD approach is also effective for a realistic head geometry and supports its use for human imaging in the future.

Quantification of intraventricular hemorrhage with electrical impedance tomography using a spherical model

We have developed a robust EEG-based current pattern which shows promise for the detection of intraventricular hemorrhage (IVH) in neonates. Our reconstructions to date are based on a layered spherical head model. In this study, the current pattern was used to gather data from three realistic-shaped neonatal head models and a physical phantom based on one of these models. We found that a sensitivity matrix calculated from a spherical model gave us satisfactory reconstructions in terms of both image quality and quantification. Incorporating correct geometry information into the forward model improved image quality. However, it did not improve quantification accuracy. The results indicate that using a spherical matrix may be a more practical choice for monitoring IVH volumes in neonates for whom patient-specific models are not available.

Can high-field MREIT be used to directly detect neural activity? Theoretical considerations

We sought to determine the feasibility of directly studying neural tissue activity by analysis of differential phase shifts in MRI signals that occurred when trickle currents were applied to a bath containing active or resting neural tissue. We developed a finite element bidomain model of an aplysia abdominal ganglion in order to estimate the sensitivity of this contrast mechanism to changes in cell membrane conductance occurring during a gill-withdrawal reflex. We used our model to determine both current density and magnetic potential distributions within a sample chamber containing an isolated ganglion when it was illuminated with current injected synchronously with the MR imaging sequence and predicted the resulting changes in MRI phase images. This study provides the groundwork for attempts to image neural function using Magnetic Resonance Electrical Impedance Tomography (MREIT). We found that phase noise in a candidate 17.6 T MRI system should be sufficiently low to detect phase signal differences between active and resting membrane states at resolutions around 1 mm(3). We further delineate the broad dependencies of signal-to-noise ratio on activity frequency, current application time and active tissue fractions and outline strategies that can be used to lower phase noise below that presently observed in conventional MREIT techniques. We also propose the idea of using MREIT as an alternative means of studying neuromodulation.

A robust current pattern for the detection of intraventricular hemorrhage in neonates using electrical impedance tomography

We compared two 16-electrode electrical impedance tomography (EIT) current patterns on their ability to reconstruct and quantify small amounts of bleeding inside a neonatal human head using both simulated and phantom data. The current patterns used were an adjacent injection RING pattern (with electrodes located equidistantly on the equator of a sphere) and an EEG current pattern based on the 10-20 EEG electrode layout. Structures mimicking electrically important structures in the infant skull were included in a spherical numerical forward model and their effects on reconstructions were determined. The EEG pattern was found to be a better topology to localize and quantify anomalies within lateral ventricular regions. The RING electrode pattern could not reconstruct anomaly location well, as it could not distinguish different axial positions. The quantification accuracy of the RING pattern was as good as the EEG pattern in noise-free environments. However, the EEG pattern showed better quantification ability than the RING pattern when noise was added. The performance of the EEG pattern improved further with respect to the RING pattern when a fontanel was included in forward models. Significantly better resolution and contrast of reconstructed anomalies was achieved when generated from a model containing such an opening and 50 dB added noise. The EEG method was further applied to reconstruct data from a realistic neonatal head model. Overall, acceptable reconstructions and quantification results were obtained using this model and the homogeneous spherical forward model.

An electrode addressing protocol for imaging brain function with electrical impedance tomography using a 16-channel semi-parallel system

Electrical impedance tomography of brain function poses special problems because applied current is diverted by the resistive skull. In the past, image resolution was maximized with the use of an electrode addressing protocol with widely spaced drive electrode pairs and use of a multiplexer so that many electrode pairs could be flexibly addressed. The purpose of this study was to develop and test an electrode protocol for a 16-channel semi-parallel system which uses parallel recording channels with fixed wiring, the Kyung Hee University (KHU) Mk1. Ten protocols were tested, all addressing pairs of electrodes for recording or current drive, based on recording with a spiral, spiral with suboccipital electrodes (spiral s-o) and zig-zag configurations, and combinations of current injection from electrode pairs at 180 degrees , 120 degrees and 60 degrees . These were compared by assessing the image reconstruction quality of five simulated perturbations in a homogenous model of the human head and of four epileptic foci in an anatomically realistic model in the presence of realistic noise, in terms of localization error, resolution, image distortion and sensitivity in the region of interest. The spiral s-o with current injection at 180 degrees + 120 degrees + 60 degrees gave the best image quality and permitted reconstruction with a localization error of less than 10% of the head diameter. This encourages the view that it might be possible to obtain satisfactory images of focal abnormalities in the human brain with 16 scalp electrodes and improved instrumentation avoiding multiplexers on recording circuits.

A method for recording resistance changes non-invasively during neuronal depolarization with a view to imaging brain activity with electrical impedance tomography

Electrical impedance tomography (EIT) is a recently developed medical imaging method which has the potential to produce images of fast neuronal depolarization in the brain. The principle is that current remains in the extracellular space at rest but passes into the intracellular space during depolarization through open ion channels. As current passes into the intracellular space across the capacitance of cell membranes at higher frequencies, applied current needs to be below 100 Hz. A method is presented for its measurement with subtraction of the contemporaneous evoked potentials which occur in the same frequency band. Neuronal activity is evoked by stimulation and resistance is recorded from the potentials resulting from injection of a constant current square wave at 1 Hz with amplitude less than 25% of the threshold for stimulating neuronal activity. Potentials due to the evoked activity and the injected square wave are removed by subtraction. The method was validated with compound action potentials in crab walking leg nerve. Resistance changes of -0.85+/-0.4% (mean+/-SD) occurred which decreased from -0.97+/-0.43% to -0.46+/-0.16% with spacing of impedance current application electrodes from 2 to 8 mm but did not vary significantly with applied currents of 1-10 microA. These tallied with biophysical modelling, and so were consistent with a genuine physiological origin. This method appears to provide a reproducible and artefact free means for recording resistance changes during neuronal activity which could lead to the long-term goal of imaging of fast neural activity in the brain.

Impedance changes recorded with scalp electrodes during visual evoked responses: implications for Electrical Impedance Tomography of fast neural activity

Electrical Impedance Tomography (EIT) is a recently developed medical imaging method which could enable fast neural imaging in the brain by recording the resistance changes which occur as ion channels open during neuronal depolarization. In published studies in animal models with intracranial electrodes, changes of 0.005 to 3% have been reported but the amplitude of changes in the human is not known. The purpose of this work was to determine if resistance changes could be recorded non-invasively in humans during evoked activity which could form the basis for EIT of fast neural activity. Resistance was recorded with scalp electrodes during 2 Hz pattern visual evoked responses over 10 min using an insensible 1 Hz square wave constant current of 0.1-1 mA. Significant resistance decreases of 0.0010+/-0.0005% (0.30+/-0.15 microV, signal-to-noise ratio (SNR) of 2:1, n=16 recordings over 6 subjects) (mean+/-SE) were recorded. These are in broad agreement with modelling which estimated changes of 0.0039+/-0.0034% (1.03+/-0.75 microV) using an anatomically realistic finite element model. This is the first demonstration of such changes in humans and so encourages the belief that EIT could be used for neural imaging. Unfortunately, the signal-to-noise ratio was not sufficient to permit imaging at present because recording over multiple injection sites needed for imaging would require impractically long recording times. However, in the future, invasive imaging with intracranial electrodes in animal models or humans and improved signal processing or recording may still enable imaging; this would constitute a significant advance in neuroscience technology.

Comparison of methods for optimal choice of the regularization parameter for linear electrical impedance tomography of brain function

Electrical impedance tomography has the potential to provide a portable non-invasive method for imaging brain function. Clinical data collection has largely been undertaken with time difference data and linear image reconstruction methods. The purpose of this work was to determine the best method for selecting the regularization parameter of the inverse procedure, using the specific application of evoked brain activity in neonatal babies as an exemplar. The solution error norm and image SNR for the L-curve (LC), discrepancy principle (DP), generalized cross validation (GCV) and unbiased predictive risk estimator (UPRE) selection methods were evaluated in simulated data using an anatomically accurate finite element method (FEM) of the neonatal head and impedance changes due to blood flow in the visual cortex recorded in vivo. For simulated data, LC, GCV and UPRE were equally best. In human data in four neonatal infants, no significant differences were found among selection methods. We recommend that GCV or LC be employed for reconstruction of human neonatal images, as UPRE requires an empirical estimate of the noise variance.

Image reconstruction incorporated with the skull inhomogeneity for electrical impedance tomography

The structural similarity of the head model affects the accuracy of forward solution to electrical impedance tomography (EIT). Generally, the four-concentric circle model (FCCM) is used as the head model, which ignores the inhomogeneous distribution of the conductivity of real skull. In order to decrease the errors caused by using FCCM, a more accurate head model named inhomogeneous skull model (ISM) has been proposed and a reconstruction algorithm incorporated with ISM has been developed for brain EIT. Simulation results have shown improvement in image quality and localization accuracy when using ISM. It is also suggested that the reconstructed image could be more sensitive to the location of bony sutures than to the variation of skull thickness. In conclusion, incorporating skull inhomogeneity into image reconstruction is an effective way to improve image quality and localization accuracy for brain EIT.