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.

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.

Optimal combination of electrodes and conductive gels for brain electrical impedance tomography.

BACKGROUND: Electrical impedance tomography (EIT) is an emerging imaging technology that has been used to monitor brain injury and detect acute stroke. The time and frequency properties of electrode-skin contact impedance are important for brain EIT because brain EIT measurement is performed over a long period when used to monitor brain injury, and is carried out across a wide range of frequencies when used to detect stroke. To our knowledge, no study has simultaneously investigated the time and frequency properties of both electrode and conductive gel for brain EIT. METHODS: In this study, the contact impedance of 16 combinations consisting of 4 kinds of clinical electrode and five types of commonly used conductive gel was measured on ten volunteers' scalp for a period of 1 h at frequencies from 100 Hz to 1 MHz using the two-electrode method. And then the performance of each combination was systematically evaluated in terms of the magnitude of contact impedance, and changes in contact impedance with time and frequency. RESULTS: Results showed that combination of Ag+/Ag+Cl- powder electrode and low viscosity conductive gel performed best overall (Ten 20® in this study); it had a relatively low magnitude of contact impedance and superior performance regarding contact impedance with time (p < 0.05) and frequency (p < 0.05). CONCLUSIONS: Experimental results indicates that the combination of Ag+/Ag+Cl- powder electrode and low viscosity conductive gel may be the best choice for brain EIT.

Noninvasive Cerebral Imaging and Monitoring Using Electrical Impedance Tomography During Total Aortic Arch Replacement.

OBJECTIVE: To explore the feasibility of using electrical impedance tomography (EIT) to provide noninvasive cerebral imaging and monitoring in total aortic arch replacement (TAAR). DESIGN: A prospective, observational study. SETTING: Department of cardiovascular surgery in a university hospital. PARTICIPANTS: Forty-two patients undergoing TAAR using hypothermic circulatory arrest and unilateral antegrade cerebral perfusion. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Cerebral impedances of the patients were monitored intraoperatively by an EIT system. The prognostic information of the patients, including postoperative neurological dysfunction, was collected during their hospitalizations. Eight (19.0%) subjects had at least 1 postoperative neurological dysfunction complication. The results show that cerebral impedance was related negatively to perfusion flow, and the gradual increase in cerebral resistivity might reflect the evolving process of brain tissue caused by hypoxia. Maximum resistivity asymmetry index was extracted from the reconstructed images to quantify the pathological changes of the brain. The area under the receiver operating characteristic curve of maximum resistivity asymmetry index for postoperative neurological dysfunction was 0.864. In multivariate logistic regression, maximum resistivity asymmetry index was the strongest independent predictor of neurological dysfunction with an odds ratio of 24.3. CONCLUSION: EIT is a promising technique to provide noninvasive cerebral imaging and monitoring in TAAR.

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.

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

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

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

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

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.

A high-efficiency real-time digital signal averager for time-of-flight mass spectrometry.

RATIONALE: Analog-to-digital converter (ADC)-based acquisition systems are widely applied in time-of-flight mass spectrometers (TOFMS) due to their ability to record the signal intensity of all ions within the same pulse. However, the acquisition system raises the requirement for data throughput, along with increasing the conversion rate and resolution of the ADC. It is therefore of considerable interest to develop a high-performance real-time acquisition system, which can relieve the limitation of data throughput. METHODS: We present in this work a high-efficiency real-time digital signal averager, consisting of a signal conditioner, a data conversion module and a signal processing module. Two optimization strategies are implemented using field programmable gate arrays (FPGAs) to enhance the efficiency of the real-time processing. A pipeline procedure is used to reduce the time consumption of the accumulation strategy. To realize continuous data transfer, a high-efficiency transmission strategy is developed, based on a ping-pong procedure. RESULTS: The digital signal averager features good responsiveness, analog bandwidth and dynamic performance. The optimal effective number of bits reaches 6.7 bits. For a 32 µs record length, the averager can realize 100% efficiency with an extraction frequency below 31.23 kHz by modifying the number of accumulation steps. In unit time, the averager yields superior signal-to-noise ratio (SNR) compared with data accumulation in a computer. CONCLUSIONS: The digital signal averager is combined with a vacuum ultraviolet single-photon ionization time-of-flight mass spectrometer (VUV-SPI-TOFMS). The efficiency of the real-time processing is tested by analyzing the volatile organic compounds (VOCs) from ordinary printed materials. In these experiments, 22 kinds of compounds are detected, and the dynamic range exceeds 3 orders of magnitude.

Discrimination between human normal renal tissue and renal cell carcinoma by dielectric properties using in-vitro BIA.

Renal cell carcinoma (RCC) poses a serious threat to human health, which urgently requires a method that can quickly distinguish between human normal renal tissue (NRT) and RCC for the purpose of accurate detection in clinical practice. The significant difference in cell morphology between NRT and RCC tissue underlies the great potential of the bioelectrical impedance analysis (BIA) to distinguish two types of human tissues. The study aims to achieve such discrimination through comparison of their dielectric properties within the frequency range from 10 Hz to 100 MHz. The dielectric properties of 69 cases of human normal and cancer renal tissue were measured 15 min after tissue isolation in a strictly controlled environment (37°C, 90% humidity). In addition to the impedance parameters (resistivity, conductivity and relative permittivity), the characteristic parameters extracted from the Cole curve were also compared between NRT and RCC. Furthermore, a novel index, distinguishing coefficient (DC), was used to obtain the optimal frequency for discrimination between NRT and RCC. In terms of impedance parameters, the RCC conductivity at low frequencies (<1 kHz) was about 1.4 times as large as that of NRT, and its relative permittivity was also significantly higher (p < 0.05). In terms of characteristic parameters, two characteristic frequencies (14.1 ± 1.1 kHz and 1.16 ± 0.13 MHz) were found for NRT while only one for RCC (0.60 ± 0.05 MHz). A significant difference of low-frequency resistance (R0) between RCC and NRT was also observed (p < 0.05). As for the new index DC, relative permittivity DCs below 100 Hz and at around 14 kHz were both greater than 1. These findings further confirm the feasibility of discrimination between RCC and NRT and also provide data in favor of further clinical study of BIA to detect the surgical margins.

[Comparison of three dynamic algorithms of electrical impedance tomography based on simulation study].

Three algorithms of electrical impedance tomography (EIT) are studied in this paper. The image resolution, anti-noise property and computation rapidity of the reconstruction algorithms are compared. As a result, it shows that back-projection algorithm has good anti-noise property, that NOSER algorithm generates images with good resolution, and that sensitivity matrix algorithm has moderate property.

Suppressing interferences of EIT on synchronous recording EEG based on comb filter for seizure detection.

Background and objective: The purpose of this study was to eliminate the interferences of electrical impedance tomography (EIT) on synchronous recording electroencephalography (EEG) for seizure detection. Methods: The simulated EIT signal generated by COMSOL Multiphysics was superimposed on the clinical EEG signal obtained from the CHB-MIT Scalp EEG Database, and then the spectrum features of superimposed mixed signals were analyzed. According to the spectrum analysis, in addition to high-frequency interference at 51.2 kHz related to the drive current, there was also low-frequency interference caused by switching of electrode pairs, which were used to inject drive current. A low pass filter and a comb filter were used to suppress the high-frequency interference and low-frequency interference, respectively. Simulation results suggested the low-pass filter and comb filter working together effectively filtered out the interference of EIT on EEG in the process of synchronous monitoring. Results: As a result, the normal EEG and epileptic EEG could be recognized effectively. Pearson correlation analysis further confirmed the interference of EIT on EEG was effectively suppressed. Conclusions: This study provides a simple and effective interference suppression method for the synchronous monitoring of EIT and EEG, which could be served as a reference for the synchronous monitoring of EEG and other medical electromagnetic devices.

Estimation of postmortem interval using an electric impedance spectroscopy technique: a preliminary study.

The objective of this study was to develop a rapid method for the estimation of postmortem interval (PMI) using electric impedance spectroscopy. Postmortem rat spleens were studied at 10°C, 20°C, and 30°C; The results obtained demonstrated that postmortem interval negatively correlated with the absolute value of Im Z(//) (capacitive reactance component) in electrical impedance. This suggests that electric impedance spectroscopy may be a sensitive tool to determine the postmortem interval.

Dielectric Properties of Human Active Liver, Kidney and Spleen Compared to Those of Respective Inactive Tissues, Porcine Tissues and the Data Provided by a Database in the Frequency Range of 10 Hz to 100 MHz.

OBJECTIVE: The purpose of this work is to study whether the active state and species of biological tissues can influence changes in their dielectric properties. METHODS: In this paper, the dielectric properties of liver, kidney and spleen tissues from human active, human inactive and animal tissues are measured in the frequency range of 10 Hz to 100 MHz. The four- and two-electrode methods are used to measure dielectric properties at different frequencies. Statistical analysis and the pattern recognition method are used to compare the dielectric properties of human active tissues, human inactive tissues, animal tissues and data provided by the IFAC database. RESULTS: The results show that the dielectric properties of human active tissues are significantly different from those of human inactive tissues and animal tissues, resulting in a great difference between the dielectric properties provided by the IFAC database and those of human active tissues. The dielectric properties of human active tissues can be identified by the pattern recognition method based on principal component analysis, which further proves that the dielectric properties of human active tissues cannot be replaced. CONCLUSION: The dielectric properties of biological tissues are closely related to the activity and species of tissues. The dielectric properties of human active tissues cannot be replaced by those of human cadaver tissues or animal tissues. SIGNIFICANCE: The significance of this study is suggesting that the IFAC database should be updated with the dielectric properties of human active tissues to provide accurate data for bioelectromagnetics research.

Exploring the relationship between the dielectric properties and viability of human normal hepatic tissues from 10 Hz to 100 MHz based on grey relational analysis and BP neural network.

Liver is an important parenchyma organ, and its tissue viability plays an important role in liver transplantation and liver ischemic injury assessment. Dielectric property is a useful biophysical feature that provides insights into the structure and composition of biological tissues. This work aims to establish the relationship between the dielectric properties and viability of human normal hepatic tissues and explore the possibility of evaluating tissue viability by using dielectric properties. First, data on dielectric properties and tissue viability (including cell morphology and enzyme indicators) were collected from human liver tissues at 0.25-24 h after isolation. Grey relational analysis was conducted to select dielectric property and tissue viability indices that were highly correlated with prolonged ex vivo time as the inputs and outputs, respectively, of back-propagation (BP) neural network analysis. Finally, a BP neural network was developed with the Levenberg-Marquardt algorithm to explore the possibility of using dielectric properties as the basis for tissue viability evaluation. Results showed that the mean relative error for prediction was 2.40%, indicating that the model showed potential in forecasting liver tissue viability by applying dielectric properties.

Adaptive threshold split Bregman algorithm based on magnetic induction tomography for brain injury monitoring imaging.

Objective. Traditional magnetic induction tomography (MIT) algorithms have problems in reconstruction, such as large area error (AE), blurred boundaries of reconstructed targets, and considerable image noise (IN). As the size and boundary of a lesion greatly affect the treatment plan, more accurate algorithms are necessary to meet clinical needs.Approach. In this study, adaptive threshold split Bregman (ATSB) is proposed for brain injury monitoring imaging in MIT. We established a 3D brain MIT simulation model with the actual anatomical structure and a phantom model and obtained the reconstructed images of single targets in different positions and multiple targets, using the Tikhonov, eigenvalue threshold regularisation (ETR), split Bregman (SB), and ATSB algorithms.Main results. Compared with the Tikhonov and ETR algorithms, the ATSB algorithm reduced the AE by 95% and the IN by 17% in a simulation and reduced the AE by 87% and IN by 6% in phantom experiments. Compared with the SB algorithm, the ATSB algorithm can reduce the difficulty of adjusting parameters and is easier to use in clinical practice. The simulation and phantom experiments results showed that the ATSB algorithm could reconstruct the target size more accurately and could distinguish multiple targets more effectively than the other three algorithms.Significance. The ATSB algorithm could improve the image quality of MIT and better meet the needs of clinical applications and is expected to promote brain injury monitoring imaging via MIT.

Prospective study on combination of electrical impedance scanning and ultrasound in estimating risk of development of breast cancer in young women.

Based on cost-effective ratio, there has not yet been imaging methods suitable for breast cancer screening in young women. The aim of this study was to evaluate the sensitivity and specificity of the combination of electrical impedance scanning (EIS) and ultrasound in identifying breast cancer for young women, to calculate relative risks, and to determine whether there has been some more accurate imaging method used in early detection of breast cancer in young women. A prospective and multicenter clinical study was conducted in young women aged 45 years and under. The young women (583 cases) scheduled for mammary biopsy underwent EIS and ultrasound, respectively. EIS and ultrasound results were compared with final histopathology results. Study end points included sensitivities and specificities of EIS, ultrasound and the combination method, as well as relative probability of breast cancer of positive patients detected by the combination of EIS and ultrasound. Of the 583 cases, 143 were diagnosed with breast cancer. The sensitivities of EIS, ultrasound and the combination method were 86.7% (124/143), 72% (103/143), and 93.7% (134/143); the specificities were 72.9% (321/440), 82.5% (363/440), and 64.1% (282/440), and the relative possibilities of breast cancer for the positive young women detected by EIS, ultrasound, and the combination method were 8.67, 5.77, and 14.84, respectively. The combination of EIS and ultrasound is likely to become an applicable method for early detection of breast cancer in young women.

Managing erroneous measurements of dynamic brain electrical impedance tomography after reconnection of faulty electrodes.

OBJECTIVE: Electrode detachment may occur during dynamic brain electrical impedance tomography (EIT) measurements. After the faulty electrodes have been reset, EIT can restore to steady monitoring but the corrupted data, which will challenge interpretation of the results, are notoriously difficult to recover. APPROACH: Here, a piecewise processing method (PPM) is introduced to manage the erroneous EIT data after reattachment of faulty electrodes. In the PPM, we define the three phases before, during and after reconnection of the faulty electrode as PI, PII and PIII, respectively. Using this definition, an empirical mode decomposition-based interpolation method is introduced to compensate the corrupted data in PII, using the valid measurements in PI and PIII. Then, the compensated data in PII are spliced at the end of PI. Thus, there will be a surge at the junction of PII and PIII due to the changes in contact state of the repositioned electrodes. Finally, to ensure all the EIT data are obtained under constant electrode settings, we calculate the above changes and eliminate them from the data after PII. To verify the performance of the PPM, experiments based on head models, with anatomical structures and with human subjects were conducted. Metrics including permutation entropy (PE) and image correlation (IC) were proposed to measure the stability of the signal and the quality of the reconstructed EIT images, respectively. MAIN RESULTS: The results demonstrated that the PE of the processed data was reduced to 0.25 and the IC improved to 0.78. SIGNIFICANCE: Without iterative calculations the PPM could efficiently manage the erroneous EIT data after reattachment of the faulty electrodes.

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.