An optimized strategy for real-time hemorrhage monitoring with electrical impedance tomography

A preliminary study on the application of electrical impedance tomography based on cerebral perfusion monitoring to intracranial pressure changes

Background

In intracranial pathologic conditions of intracranial pressure (ICP) disturbance or hemodynamic instability, maintaining appropriate ICP may reduce the risk of ischemic brain injury. The change of ICP is often accompanied by the change of intracranial blood status. As a non-invasive functional imaging technique, the sensitivity of electrical impedance tomography (EIT) to cerebral hemodynamic changes has been preliminarily confirmed. However, no team has conducted a feasibility study on the dynamic detection of ICP by EIT technology from the perspective of non-invasive whole-brain blood perfusion monitoring. In this study, human brain EIT image sequence was obtained by in vivo measurement, from which a variety of indicators that can reflect the tidal changes of the whole brain impedance were extracted, in order to establish a new method for non-invasive monitoring of ICP changes from the level of cerebral blood perfusion monitoring.

Methods

Valsalva maneuver (VM) was used to temporarily change the cerebral blood perfusion status of volunteers. The electrical impedance information of the brain during this process was continuously monitored by EIT device and real-time imaging was performed, and the hemodynamic indexes of bilateral middle cerebral arteries were monitored by transcranial Doppler (TCD). The changes in monitoring information obtained by the two techniques were compared and observed.

Results

The EIT imaging results indicated that the image sequence showed obvious tidal changes with the heart beating. Perfusion indicators of vascular pulsation obtained from EIT images decreased significantly during the stabilization phase of the intervention (PAC: 242.94 ± 100.83, p < 0.01); perfusion index which reflects vascular resistance increased significantly in the stable stage of intervention (PDT: 79.72 ± 18.23, p < 0.001). After the intervention, the parameters gradually returned to the baseline level before compression. The changes of EIT indexes in the whole process are consistent with the changes of middle cerebral artery velocity related indexes shown in TCD results.

Conclusion

The EIT image combined with the blood perfusion index proposed in this paper can reflect the decrease of cerebral blood flow under the condition of increased ICP in real time and intuitively. With the advantages of high time resolution and high sensitivity, EIT provides a new idea for non-invasive bedside measurement of ICP.

Exploratory study of a multifrequency EIT-based method for detecting intracranial abnormalities

Objective

The purpose of this paper is to compare the differences in the features of multifrequency electrical impedance tomography (MFEIT) images of human heads between healthy subjects and patients with brain diseases and to explore the possibility of applying MFEIT to intracranial abnormality detection.

Methods

Sixteen healthy volunteers and 8 patients with brain diseases were recruited as subjects, and the cerebral MFEIT data of 9 frequencies in the range of 21 kHz - 100 kHz of all subjects were acquired with an MFEIT system. MFEIT image sequences were obtained according to certain imaging algorithms, and the area ratio of the ROI (AR_ROI) and the mean value of the reconstructed resistivity change of the ROI (MVRRC_ROI) on both the left and right sides of these images were extracted. The geometric asymmetry index (GAI) and intensity asymmetry index (IAI) were further proposed to characterize the symmetry of MFEIT images based on the extracted indices and to statistically compare and analyze the differences between the two groups of subjects on MFEIT images.

Results

There were no significant differences in either the AR_ROI or the MVRRC_ROI between the two sides of the brains of healthy volunteers (p > 0.05); some of the MFEIT images mainly in the range of 30 kHz - 60 kHz of patients with brain diseases showed stronger resistivity distributions (larger area or stronger signal) that were approximately symmetric with the location of the lesions. However, statistical analysis showed that the AR_ROI and the MVRRC_ROI on the healthy sides of MFEIT images of patients with unilateral brain disease were not significantly different from those on the affected side (p > 0.05). The GAI and IAI were higher in all patients with brain diseases than in healthy volunteers except for 80 kHz (p < 0.05).

Conclusion

There were significant differences in the geometric symmetry and the signal intensity symmetry of the reconstructed targets in the MFEIT images between healthy volunteers and patients with brain diseases, and the above findings provide a reference for the rapid detection of intracranial abnormalities using MFEIT images and may provide a basis for further exploration of MFEIT for the detection of brain diseases.

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.

Advances of deep learning in electrical impedance tomography image reconstruction

Electrical impedance tomography (EIT) has been widely used in biomedical research because of its advantages of real-time imaging and nature of being non-invasive and radiation-free. Additionally, it can reconstruct the distribution or changes in electrical properties in the sensing area. Recently, with the significant advancements in the use of deep learning in intelligent medical imaging, EIT image reconstruction based on deep learning has received considerable attention. This study introduces the basic principles of EIT and summarizes the application progress of deep learning in EIT image reconstruction with regards to three aspects: a single network reconstruction, deep learning combined with traditional algorithm reconstruction, and multiple network hybrid reconstruction. In future, optimizing the datasets may be the main challenge in applying deep learning for EIT image reconstruction. Adopting a better network structure, focusing on the joint reconstruction of EIT and traditional algorithms, and using multimodal deep learning-based EIT may be the solution to existing problems. In general, deep learning offers a fresh approach for improving the performance of EIT image reconstruction and could be the foundation for building an intelligent integrated EIT diagnostic system in the future.

A pilot study of contrast-enhanced electrical impedance tomography for real-time imaging of cerebral perfusion

Background

Real-time detection of cerebral blood perfusion can prevent adverse reactions, such as cerebral infarction and neuronal apoptosis. Our previous clinical trial have shown that the infusion of therapeutic fluid can significantly change the impedance distribution in the brain. However, whether this alteration implicates the cerebral blood perfusion remains unclear. To explore the feasibility of monitoring cerebral blood perfusion, the present pilot study established a novel cerebral contrast-enhanced electrical impedance tomography (C-EIT) technique.

Materials and methods

Rabbits were randomly divided into two groups: the internal carotid artery non-occlusion (ICAN) and internal carotid artery occlusion (ICAO) groups. Both of groups were injected with glucose, an electrical impedance-enhanced contrast agent, through the right internal carotid artery under EIT monitoring. The C-EIT reconstruction images of the rabbits brain were analyzed according to the collected raw data. The paired and independent t-tests were used to analyze the remodeled impedance values of the left and right cerebral hemispheres within and between studied groups, respectively. Moreover, pathological examinations of brain were performed immediately after C-EIT monitoring.

Results

According to the reconstructed images, the impedance value of the left cerebral hemisphere in the ICAN group did not change significantly, whereas the impedance value of the right cerebral hemisphere gradually increased, reaching a peak at approximately 10 s followed by gradually decreased. In the ICAO group, the impedance values of both cerebral hemispheres increased gradually and then began to decrease after reaching the peak value. According to the paired t-test, there was a significant difference (P < 0.001) in the remodeling impedance values between the left and right hemispheres in the ICAN group, and there was also a significant difference (P < 0.001) in the ICAO group. According to the independent t-test, there was a significant difference (P < 0.001) of the left hemispheres between the ICAN and ICAO groups.

Conclusion

The cerebral C-EIT proposed in this pilot study can reflect cerebral blood perfusion. This method has potential in various applications in the brain in the future, including disease progression monitoring, collateral circulation judgment, tumor-specific detection, and brain function research.

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.

Investigation of a Ballistocardiogram-Based Technique for Unobtrusive Monitoring of Fluid Accumulation in the Body. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020;2020:5146-5149. [PMID: 33019144 DOI: 10.1109/embc44109.2020.9176094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

We introduce a novel monitoring solution for fluid accumulation in the human body (e.g. internal bleeding), based on observation of a selected energy-describing feature of the Ballistocardiogram (BCG) signal. It is hypothesized that, because of additional damping generated by the fluid, BCG signal energy decreases as compared to its baseline value. Data were collected from 15 human volunteers via accelerometers attached to the participants' body, and an electromechanical-film (EMFi) sensor-equipped bed. Fluid accumulation along the gastrointestinal (GI) tract was induced by means of water intake by the participants, and the BCG signal was recorded before and after intake. Based on performance evaluation, we selected a suitable energy feature and sensing channel amongst the ones investigated. The chosen feature showed a significant decrease in signal energy from baseline to after-intake condition (p-value<0.001), and identified the presence of fluid accumulation with high sensitivity (90% in bed-based, and 100% in standing-position monitoring).

Real-Time Detection of Hemothorax and Monitoring its Progression in a Piglet Model by Electrical Impedance Tomography: A Feasibility Study

Hemothorax is a serious medical condition that can be life-threatening if left untreated. Early diagnosis and timely treatment are of great importance to produce favorable outcome. Although currently available diagnostic techniques, e.g., chest radiography, ultrasonography, and CT, can accurately detect hemothorax, delayed hemothorax cannot be identified early because these examinations are often performed on patients until noticeable symptoms manifest. Therefore, for early detection of delayed hemothorax, real-time monitoring by means of a portable and noninvasive imaging technique is needed. In this study, we employed electrical impedance tomography (EIT) to detect the onset of hemothorax in real time on eight piglet hemothorax models. The models were established by injection of 60 ml fresh autologous blood into the pleural cavity, and the subsequent development of hemothorax was monitored continuously. The results showed that EIT was able to sensitively detect hemothorax as small as 10 ml in volume, as well as its location. Also, the development of hemothorax over a range of 10 ml up to 60 ml was well monitored in real time, with a favorable linear relationship between the impedance change in EIT images and the volume of blood injected. These findings demonstrated that EIT has a unique potential for early diagnosis and continuous monitoring of hemothorax in clinical practice, providing medical staff valuable information for prompt identification and treatment of delayed hemothorax.

Real-time imaging of infarction deterioration after ischemic stroke in rats using electrical impedance tomography

OBJECTIVE

This study investigated the feasibility of electrical impedance tomography (EIT) for monitoring the deterioration of ischemic lesion after the onset of stroke.

APPROACH

Fifteen rats were randomly distributed into two groups: rats operated to establish a right middle cerebral artery occlusion (MCAO) (n  =  10), and sham-operated rats (n  =  5). Then, the operated rats were kept 2 h under anesthesia for EIT monitoring. Subsequently, descriptive statistical analysis was performed on whole-brain resistivity changes, and repeated-measures analysis of variance (ANOVA) on the average resistivity variation index. Additionally, pathological examinations were performed after 6 h of infarction.

MAIN RESULTS

The results obtained showed that ischemic damage developed in the right corpus striatum of the rats with MCAO, whereas the brains of the sham group showed no anomalies. The descriptive statistical analysis revealed that the whole-brain resistivity changes after 30, 60, 90, and 120 min of infarction were 0.063  ±  0.038, 0.097  ±  0.046, 0.141  ±  0.062, and 0.204  ±  0.092 for the rats with MCAO and 0.029  ±  0.021, 0.002  ±  0.002, 0.017  ±  0.011, and  -0.001  ±  0.011 for the sham-operated rats, respectively. The repeated-measures ANOVA revealed that the right MCAO model resulted in a significant impedance increase in the right hemisphere, which continued to increase over time after infarction.

SIGNIFICANCE

The overall study results indicate that EIT facilitates monitoring of local impedance variations caused by MCAO and may be a solution for real-time monitoring of intracranial pathological changes in ischemic stroke patients.

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.

An iterative damped least-squares algorithm for simultaneously monitoring the development of hemorrhagic and secondary ischemic lesions in brain injuries

Electrical impedance tomography (EIT) is a non-invasive and real-time imaging method that has the potential to be used for monitoring intracerebral hemorrhage (ICH). Recent studies have proposed that ischemia secondary to ICH occurs simultaneously in the brain. Real-time monitoring of the development of hemorrhage and risk of secondary ischemia is crucial for clinical intervention. However, few studies have explored the performance of EIT monitoring in cases where hemorrhage and secondary ischemia exist. When these lesions get close to each other, or their conductivity and volume changes differ greatly, it becomes challenging for dynamic EIT algorithms to simultaneously reconstruct subtle injuries. To address this, an iterative damped least-squares (IDLS) algorithm is proposed in this study. The quality of the IDLS algorithm was assessed using blur radius and temporal response during computer simulation and a phantom 3D head-shaped model where bidirectional disturbance targets were simulated. The results showed that the IDLS algorithm enhanced contrast and concurrently reconstructed bidirectional disturbance targets in images. Moreover, it showed superior performance in decreasing the blur radius and was time cost-effective. With further improvement, the IDLS algorithm has the potential to be used for monitoring the development of hemorrhage and risk of ischemia secondary to ICH. Graphical abstract (a) and (b) are simulation images of bidirectional disturbance targets with different change ratios of volume (Vr) and conductivity (σ_r) based on the damped least-squares (DLS) algorithm and iterative damped least-squared (IDLS) algorithm, respectively. (c) shows the performance metrics of blur radius and temporal response with different volume ratio (corresponding to Vr). (d) shows the performance metrics of blur radius and temporal response with different conductivity change percentage (corresponding to σ_r).

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.

An on-line processing strategy for head movement interferences removal of dynamic brain electrical impedance tomography based on wavelet decomposition

BACKGROUND

Head movement interferences are a common problem during prolonged dynamic brain electrical impedance tomography (EIT) clinical monitoring. Head movement interferences mainly originate from body movements of patients and nursing procedures performed by medical staff, etc. These body movements will lead to variation in boundary voltage signals, which affects image reconstruction.

METHODS

This study employed a data preprocessing method based on wavelet decomposition to inhibit head movement interferences in brain EIT data. Mixed Gaussian models were applied to describe the distribution characteristics of brain EIT data. We identified head movement signal through the differences in distribution characteristics of corresponding wavelet decomposition coefficients between head movement artifacts and normal signals, and then managed the contaminated data with improved on-line wavelet processing methods.

RESULTS

To validate the efficacy of the method, simulated signal experiments and human data experiments were performed. In the simulation experiment, the simulated movement artifact was significantly reduced and data quality was improved with indicators' increase in PRD and correlation coefficient. Human data experiments demonstrated that this method effectively suppressed head movement in signals and reduce artifacts resulting from head movement artifacts in images.

CONCLUSION

In this paper, we proposed an on-line strategy to manage the head movement interferences from the brain EIT data based on the distribution characteristics of wavelet coefficients. Our strategy is capable of reducing the movement interference in the data and improving the reconstructed images. This work would improve the clinical practicability of brain EIT and contribute to its further promotion.

Experimental study on the detection of cerebral hemorrhage in rabbits based on broadband antenna technology

Hematoma enlargement often occurs in patients with spontaneous intracerebral hemorrhage (ICH), so it is necessary to monitor the amount of intracranial hemorrhage in patients after admission. At present, the commonly used intracranial pressure (ICP) method has the disadvantages of trauma and infection, and the Computer Tomography (CT) method cannot achieve continuous monitoring. So it is urgent to develop a non-contact and non-invasive method for continuous monitoring of cerebral hemorrhage. The dielectric properties of blood are different from those of brain tissue, so the hematoma will affect the amplitude and phase of the electromagnetic waves passing through the head. A microstrip antenna was designed to construct the detection system for cerebral hemorrhage. Based on the animal model of acute cerebral hemorrhage, the detecting experiment was carried out on thirteen rabbits. Each rabbit had three bleeding states: 1, 2, and 3 ml, which represented the severity of cerebral hemorrhage. According to the measured data of high dimension and small sample, the support vector machine (SVM) algorithm was used to assess the severity of cerebral hemorrhage. According to simulation results, the antenna's forward radiation was 5 dB larger than the backward radiation, which ensured the antenna being not affected by external signals during the measurement. According to test results, the -10 dB workband of the antenna was 1.55-2.05 GHz and the frequency range of the transmission parameters S₂₁ above -30 dB is 1.2 - 3 GHz. In the animal experiment, the phase difference of Transmission coefficient S₂₁ was gradually increased with the increase of bleeding volume. Through the classification of 39 bleeding states of the 13 rabbits, the total accuracy was about 77%. Through animal experiments, the feasibility of detection method has been proved. But the classification accuracy need to be further improved. The detection system is based on broadband antenna has the potential to realize non-contact, non-invasive and continuous monitoring for cerebral hemorrhage.

EIT Imaging of Intracranial Hemorrhage in Rabbit Models Is Influenced by the Intactness of Cranium

Electrical impedance tomography (EIT) has been shown to be a promising, bedside imaging method to monitor the progression of intracranial hemorrhage (ICH). However, the observed impedance changes within brain related to ICH differed among groups, and we hypothesized that the cranium intactness (open or closed) may be the one of potential reasons leading to the difference. Therefore, the aim of this study was to investigate this effect of open or closed cranium on impedance changes within brain in the rabbit ICH model. In this study, we first established the ICH model in 12 rabbits with the open cranium and in 12 rabbits with the closed cranium. Simultaneously, EIT measurements on the rabbits' heads were performed to record the impedance changes caused by injecting the autologous nonheparinized blood into cerebral parenchyma. Finally, the regional impedance changes on EIT images and the whole impedance changes were analyzed. It was surprisingly found that when the cranium was open, the impedance of the area where the blood was injected, as well as the whole brain impedance, decreased with the amount of blood being injected; when the cranium was closed, while the impedance of the area where blood was not injected continued to increase, the impedance of the area where blood was injected decreased within 20s of the blood being injected and then remained almost unchanged, and the whole brain impedance had a small fall and then notably increased. The results have validated that the cranium completeness (open or closed) has influences on impedance changes within brain when using EIT to monitor ICH. In future study on application of EIT to monitor ICH, the cranium completeness should be taken into account for establishing an ICH model and analyzing the corresponding EIT results.

Combing signal processing methods with algorithm priori information to produce synergetic improvements on continuous imaging of brain electrical impedance tomography

Dynamic electrical impedance tomography (EIT) promises to be a valuable technique for monitoring the development of brain injury. But in practical long-term monitoring, noise and interferences may cause insufficient image quality. To help unveil intracranial conductivity changes, signal processing methods were introduced to improve EIT data quality and algorithms were optimized to be more robust. However, gains for EIT image reconstruction can be significantly increased if we combine the two techniques properly. The basic idea is to apply the priori information in algorithm to help de-noise EIT data and use signal processing to optimize algorithm. First, we process EIT data with principal component analysis (PCA) and reconstruct an initial CT-EIT image. Then, as the priori that changes in scalp and skull domains are unwanted, we eliminate their corresponding boundary voltages from data sets. After the two-step denoising process, we finally re-select a local optimal regularization parameter and accomplish the reconstruction. To evaluate performances of the signal processing-priori information based reconstruction (SPR) method, we conducted simulation and in-vivo experiments. The results showed SPR could improve brain EIT image quality and recover the intracranial perturbations from certain bad measurements, while for some measurement data the generic reconstruction method failed.

Real-time imaging of epileptic seizures in rats using electrical impedance tomography

The presence of multiple or diffuse lesions on imaging is a contraindication to surgery for patients with intractable epilepsy. Theoretically, as a functional imaging technique, electrical impedance tomography (EIT) can accurately image epileptic foci. However, most current studies are limited to examining epileptic spikes and few studies use EIT for real-time imaging of seizure activity. Moreover, little is known about changes in electrical impedance during seizures. In this study, we used EIT to monitor seizure progression in real time and analyzed changes in electrical impedance during seizures. EIT and electroencephalography data were recorded simultaneously in rats. Sixty-three seizures were recorded from the cortices of eight rats. During 54 seizures, the average impedance decreased by between 4.86 and 9.17% compared with the baseline. Compared with the control group, the average impedance of the experimental group decreased significantly (P=0.004). Our results indicate that EIT can be used to detect and image electrical impedance reduction within lesions during epileptic seizures.

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.

Fast detection and data compensation for electrodes disconnection in long-term monitoring of dynamic brain electrical impedance tomography

Background

Electrode disconnection is a common occurrence during long-term monitoring of brain electrical impedance tomography (EIT) in clinical settings. The data acquisition system suffers remarkable data loss which results in image reconstruction failure. The aim of this study was to: (1) detect disconnected electrodes and (2) account for invalid data.

Methods

Weighted correlation coefficient for each electrode was calculated based on the measurement differences between well-connected and disconnected electrodes. Disconnected electrodes were identified by filtering out abnormal coefficients with discrete wavelet transforms. Further, previously valid measurements were utilized to establish grey model. The invalid frames after electrode disconnection were substituted with the data estimated by grey model. The proposed approach was evaluated on resistor phantom and with eight patients in clinical settings.

Results

The proposed method was able to detect 1 or 2 disconnected electrodes with an accuracy of 100%; to detect 3 and 4 disconnected electrodes with accuracy of 92 and 84% respectively. The time cost of electrode detection was within 0.018 s. Further, the proposed method was capable to compensate at least 60 subsequent frames of data and restore the normal image reconstruction within 0.4 s and with a mean relative error smaller than 0.01%.

Conclusions

In this paper, we proposed a two-step approach to detect multiple disconnected electrodes and to compensate the invalid frames of data after disconnection. Our method is capable of detecting more disconnected electrodes with higher accuracy compared to methods proposed in previous studies. Further, our method provides estimations during the faulty measurement period until the medical staff reconnects the electrodes. This work would improve the clinical practicability of dynamic brain EIT and contribute to its further promotion.

Use of electrical impedance tomography to monitor regional cerebral edema during clinical dehydration treatment

OBJECTIVE

Variations of conductive fluid content in brain tissue (e.g. cerebral edema) change tissue impedance and can potentially be measured by Electrical Impedance Tomography (EIT), an emerging medical imaging technique. The objective of this work is to establish the feasibility of using EIT as an imaging tool for monitoring brain fluid content.

DESIGN

a prospective study.

SETTING

In this study EIT was used, for the first time, to monitor variations in cerebral fluid content in a clinical model with patients undergoing clinical dehydration treatment. The EIT system was developed in house and its imaging sensitivity and spatial resolution were evaluated on a saline-filled tank.

PATIENTS

23 patients with brain edema.

INTERVENTIONS

The patients were continuously imaged by EIT for two hours after initiation of dehydration treatment using 0.5 g/kg intravenous infusion of mannitol for 20 minutes.

MEASUREMENT AND MAIN RESULTS

Overall impedance across the brain increased significantly before and after mannitol dehydration treatment (p = 0.0027). Of the all 23 patients, 14 showed high-level impedance increase and maintained this around 4 hours after the dehydration treatment whereas the other 9 also showed great impedance gain during the treatment but it gradually decreased after the treatment. Further analysis of the regions of interest in the EIT images revealed that diseased regions, identified on corresponding CT images, showed significantly less impedance changes than normal regions during the monitoring period, indicating variations in different patients' responses to such treatment.

CONCLUSIONS

EIT shows potential promise as an imaging tool for real-time and non-invasive monitoring of brain edema patients.

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.

Exploratory study on the methodology of fast imaging of unilateral stroke lesions by electrical impedance asymmetry in human heads

Stroke has a high mortality and disability rate and should be rapidly diagnosed to improve prognosis. Diagnosing stroke is not a problem for hospitals with CT, MRI, and other imaging devices but is difficult for community hospitals without these devices. Based on the mechanism that the electrical impedance of the two hemispheres of a normal human head is basically symmetrical and a stroke can alter this symmetry, a fast electrical impedance imaging method called symmetrical electrical impedance tomography (SEIT) is proposed. In this technique, electrical impedance tomography (EIT) data measured from the undamaged craniocerebral hemisphere (CCH) is regarded as reference data for the remaining EIT data measured from the other CCH for difference imaging to identify the differences in resistivity distribution between the two CCHs. The results of SEIT imaging based on simulation data from the 2D human head finite element model and that from the physical phantom of human head verified this method in detection of unilateral stroke.

Multi-frequency electrical impedance tomography system with automatic self-calibration for long-term monitoring

Electrical Impedance Tomography (EIT) is a safe medical imaging technology, requiring no ionizing or heating radiation, as opposed to most other imaging modalities. This has led to a clinical interest in its use for long-term monitoring, possibly at the bedside, for ventilation monitoring, bleeding detection, gastric emptying and epilepsy foci diagnosis. These long-term applications demand auto-calibration and high stability over long time periods. To address this need we have developed a new multi-frequency EIT system called the KHU Mark2.5 with automatic self-calibration and cooperation with other devices via a timing signal for synchronization with other medical instruments. The impedance measurement module (IMM) for flexible configuration as a key component includes an independent constant current source, an independent differential voltmeter, and a current source calibrator, which allows automatic self-calibration of the current source within each IMM. We installed a resistor phantom inside the KHU Mark2.5 EIT system for intra-channel and inter-channel calibrations of all voltmeters in multiple IMMs. We show the deterioration of performance of an EIT system over time and the improvement due to automatic self-calibration. The system is able to maintain SNR of 80 dB for frequencies up to 250 kHz and below 0.5% reciprocity error over continuous operation for 24 hours. Automatic calibration at least every 3 days is shown to maintain SNR above 75 dB and reciprocity error below 0.7% over 7 days at 1 kHz. A clear degradation in performance results with increasing time between automatic calibrations allowing the tailoring of calibration to suit the performance requirements of each application.

Real-time imaging of cerebral infarction in rabbits using electrical impedance tomography

Objective

To investigate the possible use of electrical impedance tomography (EIT) in monitoring focal cerebral infarction in a rabbit model.

Methods

A model of focal cerebral infarction was established in eight New Zealand rabbits using a photochemical method without craniectomy. Focal cerebral infarction was confirmed by histopathological examination. Intracranial impedance variation was measured using 16 electrodes placed in a circle on the scalp. EIT images were obtained using a damped least-squares reconstruction algorithm. The average resistivity value (ARV) of the infarct region on EIT images was calculated to quantify relative resistivity changes. A symmetry index was calculated to evaluate the relative difference in resistivity between the two sides of the cerebrum.

Results

EIT images and ARV curves showed that impedance changes caused by cerebral infarction increased linearly with irradiation time. A difference in ARV was found between measurements taken before and after infarct induction.

Conclusions

Focal cerebral infarction can be monitored by EIT in the proposed animal model. The results are sufficiently encouraging that the authors plan to extend this study to humans, after further technical improvements.

Stroke damage detection using classification trees on electrical bioimpedance cerebral spectroscopy measurements

After cancer and cardio-vascular disease, stroke is the third greatest cause of death worldwide. Given the limitations of the current imaging technologies used for stroke diagnosis, the need for portable non-invasive and less expensive diagnostic tools is crucial. Previous studies have suggested that electrical bioimpedance (EBI) measurements from the head might contain useful clinical information related to changes produced in the cerebral tissue after the onset of stroke. In this study, we recorded 720 EBI Spectroscopy (EBIS) measurements from two different head regions of 18 hemispheres of nine subjects. Three of these subjects had suffered a unilateral haemorrhagic stroke. A number of features based on structural and intrinsic frequency-dependent properties of the cerebral tissue were extracted. These features were then fed into a classification tree. The results show that a full classification of damaged and undamaged cerebral tissue was achieved after three hierarchical classification steps. Lastly, the performance of the classification tree was assessed using Leave-One-Out Cross Validation (LOO-CV). Despite the fact that the results of this study are limited to a small database, and the observations obtained must be verified further with a larger cohort of patients, these findings confirm that EBI measurements contain useful information for   assessing on the health of brain tissue after stroke and supports the hypothesis that classification features based on Cole parameters, spectral information and the geometry of EBIS measurements are useful to differentiate between healthy and stroke damaged brain tissue.

Intracranial electrical impedance tomography: a method of continuous monitoring in an animal model of head trauma

BACKGROUND

Electrical impedance tomography (EIT) is a method that can render continuous graphical cross-sectional images of the brain's electrical properties. Because these properties can be altered by variations in water content, shifts in sodium concentration, bleeding, and mass deformation, EIT has promise as a sensitive instrument for head injury monitoring to improve early recognition of deterioration and to observe the benefits of therapeutic intervention. This study presents a swine model of head injury used to determine the detection capabilities of an inexpensive bedside EIT monitoring system with a novel intracranial pressure (ICP)/EIT electrode combination sensor on induced intraparenchymal mass effect, intraparenchymal hemorrhage, and cessation of brain blood flow. Conductivity difference images are shown in conjunction with ICP data, confirming the effects.

METHODS

Eight domestic piglets (3-4 weeks of age, mean 10 kg), under general anesthesia, were subjected to 4 injuries: induced intraparenchymal mass effect using an inflated, and later, deflated 0.15-mL Fogarty catheter; hemorrhage by intraparenchymal injection of 1-mL arterial blood; and ischemia/infarction by euthanasia. EIT and ICP data were recorded 10 minutes before inducing the injury until 10 minutes after injury. Continuous EIT and ICP monitoring were facilitated by a ring of circumferentially disposed cranial Ag/AgCl electrodes and 1 intraparenchymal ICP/EIT sensor electrode combination. Data were recorded at 100 Hz. Two-dimensional tomographic conductivity difference (Δσ) images, rendered using data before and after an injury, were displayed in real time on an axial circular mesh. Regions of interest (ROI) within the images were automatically selected as the upper or lower 5% of conductivity data depending on the nature of the injury. Mean Δσ within the ROIs and background were statistically analyzed. ROI Δσ was compared with the background Δσ after an injury event using an unpaired, unequal variance t test. Conductivity change within an ROI after injury was likewise compared with the same ROI before the injury making use of unpaired t tests with unequal variance.

RESULTS

Eight animal subjects were studied, each undergoing 4 injury events including euthanasia. Changes in conductivity due to injury showed expected pathophysiologic effects in an ROI identified within the middle of the left hemisphere; this localization is reasonable given the actual site of injury (left hemisphere) and spatial warping associated with estimating a 3-dimensional conductivity distribution in 2-dimensional space. Results are shown as mean ± 1 SD. When averaged across all 8 animals, balloon inflation caused the mean Δσ within the ROI to shift by -11.4 ± 10.9 mS/m; balloon deflation by +9.4 ± 8.8 mS/m; blood injection by +19.5 ± 11.5 mS/m; death by -12.6 ± 13.2 mS/m. All induced injuries were detectable to statistical significance (P < 0.0001).

CONCLUSION

This study confirms that the bedside EIT system with ICP/EIT combination sensor can detect induced trauma. Such a technique may hold promise for further research in the monitoring and management of traumatically brain-injured individuals.