Advances in electrical impedance tomography-based brain imaging

Electrical impedance tomography provides information of brain injury during total aortic arch replacement through its correlation with relative difference of neurological biomarkers

Postoperative neurological dysfunction (PND) is one of the most common complications after a total aortic arch replacement (TAAR). Electrical impedance tomography (EIT) monitoring of cerebral hypoxia injury during TAAR is a promising technique for preventing the occurrence of PND. This study aimed to explore the feasibility of electrical impedance tomography (EIT) for warning of potential brain injury during total aortic arch replacement (TAAR) through building the correlation between EIT extracted parameters and variation of neurological biomarkers in serum. Patients with Stanford type A aortic dissection and requiring TAAR who were admitted between December 2021 to March 2022 were included. A 16-electrode EIT system was adopted to monitor each patient's cerebral impedance intraoperatively. Five parameters of EIT signals regarding to the hypothermic circulatory arrest (HCA) period were extracted. Meanwhile, concentration of four neurological biomarkers in serum were measured regarding to time before and right after surgery, 12 h, 24 h and 48 h after surgery. The correlation between EIT parameters and variation of serum biomarkers were analyzed. A total of 57 TAAR patients were recruited. The correlation between EIT parameters and variation of biomarkers were stronger for patients with postoperative neurological dysfunction (PND(+)) than those without postoperative neurological dysfunction (PND(-)) in general. Particularly, variation of S100B after surgery had significantly moderate correlation with two parameters regarding to the difference of impedance between left and right brain which were MRAIabs and TRAIabs (0.500 and 0.485 with p < 0.05, respectively). In addition, significantly strong correlations were seen between variation of S100B at 24 h and the difference of average resistivity value before and after HCA phase (ΔARVHCA), the slope of electrical impedance during HCA (kHCA) and MRAIabs (0.758, 0.758 and 0.743 with p < 0.05, respectively) for patients with abnormal S100B level before surgery. Strong correlations were seen between variation of TAU after surgery and ΔARVHCA, kHCA and the time integral of electrical impedance for half flow of perfusion (TARVHP) (0.770, 0.794 and 0.818 with p < 0.01, respectively) for patients with abnormal TAU level before surgery. Another two significantly moderate correlations were found between TRAIabs and variation of GFAP at 12 h and 24 h (0.521 and 0.521 with p < 0.05, respectively) for patients with a normal GFAP serum level before surgery. The correlations between EIT parameters and serum level of neurological biomarkers were significant in patients with PND, especially for MRAIabs and TRAIabs, indicating that EIT may become a powerful assistant for providing a real-time warning of brain injury during TAAR from physiological perspective and useful guidance for intensive care units.

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.

A novel framework for three-dimensional electrical impedance tomography reconstruction of maize ear via feature reconfiguration and residual networks

Electrical impedance tomography (EIT) provides an indirect measure of the physiological state and growth of the maize ear by reconstructing the distribution of electrical impedance. However, the two-dimensional (2D) EIT within the electrode plane finds it challenging to comprehensively represent the spatial distribution of conductivity of the intact maize ear, including the husk, kernels, and cob. Therefore, an effective method for 3D conductivity reconstruction is necessary. In practical applications, fluctuations in the contact impedance of the maize ear occur, particularly with the increase in the number of grids and computational workload during the reconstruction of 3D spatial conductivity. These fluctuations may accentuate the ill-conditioning and nonlinearity of the EIT. To address these challenges, we introduce RFNetEIT, a novel computational framework specifically tailored for the absolute imaging of the three-dimensional electrical impedance of maize ear. This strategy transforms the reconstruction of 3D electrical conductivity into a regression process. Initially, a feature map is extracted from measured boundary voltage via a data reconstruction module, thereby enhancing the correlation among different dimensions. Subsequently, a nonlinear mapping model of the 3D spatial distribution of the boundary voltage and conductivity is established, utilizing the residual network. The performance of the proposed framework is assessed through numerical simulation experiments, acrylic model experiments, and maize ear experiments. Our experimental results indicate that our method yields superior reconstruction performance in terms of root-mean-square error (RMSE), correlation coefficient (CC), structural similarity index (SSIM), and inverse problem-solving time (IPST). Furthermore, the reconstruction experiments on maize ears demonstrate that the method can effectively reconstruct the 3D conductivity distribution.

ViTab Transformer Framework for Predicting Induced Electric Field and Focality in Transcranial Magnetic Stimulation

Transcranial magnetic stimulation is an electromagnetic induction-based non-invasive therapeutic technique for neurological diseases. For finding new clinical applications and enhancing the efficacy of TMS in existing neurological disorders, the current study focuses on a deep learning-based prediction model as an alternative to time-consuming electromagnetic (EM) simulation software. The main bottleneck of the existing prediction models is to consider very few input parameters of a standard coil such as coil type and coil position for predicting an output of electric field value. To overcome this limitation, a transformer-based prediction model titled as ViTab transformer is developed in this work to predict electric field (E-max), focality or area of stmulation (S-half), and volume of stimulation (V-half) by considering several input parameters such as sources of MRI images, types of coils, coil position, rate of change of current, brain tissues conductivity, and coil distance from the scalp. The proposed framework consists of a vision and a tab transformer to handle both image and tabular-type data. The prediction performance of the offered model is evaluated in terms of coefficient determination, R2 score, for E-max, V-half, and S-half in the testing phase. The obtained result in terms of R2 score for E-max, V-half, and S-half are found 0.97, 0.87, and 0.90 respectively. The results indicate that the suggested ViTab transformer model can predict electric field as well as focality more accurately than the current state-of-the-art methods. The reduced computational time, as well as efficient prediction accuracy, resembles that ViTab transformer can assist the neuroscientist and neurosurgeon prior to providing superior TMS treatment in near future.

Electrical bioimpedance measurement and near-infrared spectroscopy in pediatric postoperative neurocritical care: a prospective observational study

Background

To investigate the clinical significance of the disturbance coefficient (DC) and regional cerebral oxygen saturation (rSO₂) as obtained through the use of electrical bioimpedance and near-infrared spectroscopy (NIRS) in pediatric neurocritical care.

Participants and methods

We enrolled 45 pediatric patients as the injury group and 70 healthy children as the control group. DC was derived from impedance analysis of 0.1 mA-50 kHz current via temporal electrodes. rSO₂ was the percentage of oxyhemoglobin measured from reflected NIR light on the forehead. DC and rSO₂ were obtained at 6, 12, 24, 48 and 72 h after surgery for the injury group and during the health screening clinic visit for the control group. We compared DC and rSO₂ between the groups, their changes over time within the injury group and their correlation with intracranial pressure (ICP), cerebral perfusion pressure (CPP), Glasgow coma scale (GCS) score, Glasgow outcome scale (GOS) score, and their ability to diagnose postoperative cerebral edema and predict poor prognosis.

Results

DC and rSO₂ were significantly lower in the injury group than in the control group. In the injury group, ICP increased over the monitoring period, while DC, CPP and rSO₂ decreased. DC was negatively correlated with ICP and positively correlated with GCS score and GOS score. Additionally, lower DC values were observed in patients with signs of cerebral edema, with a DC value of 86.5 or below suggesting the presence of brain edema in patients aged 6-16 years. On the other hand, rSO₂ was positively correlated with CPP, GCS score, and GOS score, with a value of 64.4% or below indicating a poor prognosis. Decreased CPP is an independent risk factor for decreased rSO₂.

Conclusion

DC and rSO₂ monitoring based on electrical bioimpedance and near-infrared spectroscopy not only reflect the degree of brain edema and oxygenation, but also reflect the severity of the disease and predict the prognosis of the patients. This approach offers a real-time, bedside, and accurate method for assessing brain function and detecting postoperative cerebral edema and poor prognosis.

Finite velocity of ECG signal propagation: preliminary theory, results of a pilot experiment and consequences for medical diagnosis

A satisfactory model of the biopotentials propagating through the human body is essential for medical diagnostics, particularly for cardiovascular diseases. In our study, we develop the theory, that the propagation of biopotential of cardiac origin (ECG signal) may be treated as the propagation of low-frequency endogenous electromagnetic wave through the human body. We show that within this approach, the velocity of the ECG signal can be theoretically estimated, like for any other wave and physical medium, from the refraction index of the tissue in an appropriate frequency range. We confirm the theoretical predictions by the comparison with a direct measurement of the ECG signal propagation velocity and obtain mean velocity as low as v=1500 m/s. The results shed new light on our understanding of biopotential propagation through living tissue. This propagation depends on the frequency band of the signal and the transmittance of the tissue. This finding may improve the interpretation of the electric measurements, such as ECG and EEG when the frequency dependence of conductance and the phase shift introduced by the tissue is considered. We have shown, that the ECG propagation modifies the amplitude and phase of signal to a considerable extent. It may also improve the convergence of inverse problem in electrocardiographic imaging.

Electrical Impedance Tomography: From the Traditional Design to the Novel Frontier of Wearables

Electrical impedance tomography (EIT) is a medical imaging technique based on the injection of a current or voltage pattern through electrodes on the skin of the patient, and on the reconstruction of the internal conductivity distribution from the voltages collected by the electrodes. Compared to other imaging techniques, EIT shows significant advantages: it does not use ionizing radiation, is non-invasive and is characterized by high temporal resolution. Moreover, its low cost and high portability make it suitable for real-time, bedside monitoring. However, EIT is also characterized by some technical limitations that cause poor spatial resolution. The possibility to design wearable devices based on EIT has recently given a boost to this technology. In this paper we reviewed EIT physical principles, hardware design and major clinical applications, from the classical to a wearable setup. A wireless and wearable EIT system seems a promising frontier of this technology, as it can both facilitate making clinical measurements and open novel scenarios to EIT systems, such as home monitoring.

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.

Cerebral edema after ischemic stroke: Pathophysiology and underlying mechanisms

Ischemic stroke is associated with increasing morbidity and has become the main cause of death and disability worldwide. Cerebral edema is a serious complication arising from ischemic stroke. It causes an increase in intracranial pressure, rapid deterioration of neurological symptoms, and formation of cerebral hernia, and is an important risk factor for adverse outcomes after stroke. To date, the detailed mechanism of cerebral edema after stroke remains unclear. This limits advances in prevention and treatment strategies as well as drug development. This review discusses the classification and pathological characteristics of cerebral edema, the possible relationship of the development of cerebral edema after ischemic stroke with aquaporin 4, the SUR1-TRPM4 channel, matrix metalloproteinase 9, microRNA, cerebral venous reflux, inflammatory reactions, and cerebral ischemia/reperfusion injury. It also summarizes research on new therapeutic drugs for post-stroke cerebral edema. Thus, this review provides a reference for further studies and for clinical treatment of cerebral edema after ischemic stroke.