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. 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. 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%. 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.

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. 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. 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. 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.

Purpose: The purpose of this study was to evaluate the ability of global inhomogeneity index (GI) and left-right asymmetry index (AI) based on electrical impedance tomography (EIT) to be used in assessing cerebral perfusion heterogeneity. The diagnostic value of these two indices in identifying abnormalities in the degree of cerebral perfusion heterogeneity was also explored.Methods: In this study, Transcranial Doppler (TCD) was used as a control, and unilateral carotid artery was compressed to change the degree of heterogeneity of cerebral perfusion in 15 healthy volunteers. The control group consisted of an additional 15 volunteers without any intervention. EIT perfusion images were obtained by calculating the impedance difference between at the beginning and end of cerebral vasodilation. Subsequently, GI and AI were calculated based on the pixel values of intracranial regions.Results: The GI and AI values in the non-carotid artery compression (NCAC) group were significantly lower than those in the unilateral carotid artery compression (UCAC) group (P < 0.001), whereas there was no significant difference between the left carotid artery compression (LCAC) and right carotid artery compression (RCAC) groups. ROC analysis showed that the area under the curve (AUC), specificity and sensitivity of GI in distinguishing between NCAC and UCAC were 0.94, 0.90 and 0.87, respectively. The AUC, specificity and sensitivity of AI in distinguishing between NCAC and UCAC were 0.86, 0.87 and 0.73, respectively.Conclusion: The results demonstrated that the GI and AI effectively quantify the distribution of intracranial perfusion, demonstrating excellent validity and interindividual comparability, and the ability to detect abnormal cerebral perfusion heterogeneity.

Acute and critical neurological diseases are often accompanied with elevated intracranial pressure (ICP), leading to insufficient cerebral perfusion, which may cause severe secondary lesion. Existing ICP monitoring techniques often fail to effectively meet the demand for real-time noninvasive ICP monitoring and warning. This study aimed to explore the use of electrical impedance tomography (EIT) to provide real-time early warning of elevated ICP by observing cerebral perfusion. An intracranial hypertension model was prepared by injecting autologous un-anticoagulated blood into the brain parenchyma of twelve Landrace swine. Invasive ICP monitoring was used as a control method, and a high-precision EIT system was used to acquire and analyze the changing patterns of cerebral perfusion EIT image parameters with respect to ICP. Four EIT parameters related to cerebral perfusion were extracted from the images, and their potential application in detecting ICP elevation was analyzed. When ICP increased, all EIT perfusion parameters decreased significantly (P < 0.05). When the subjects were in a state of intracranial hypertension (ICP > 22 mmHg), the correlation between EIT perfusion parameters and ICP was more significant (P < 0.01), with correlation coefficients ranging from −0.72 to −0.83. We tested the objects when they were in baseline ICP and in ICP of 15–40 mmHg. Under both circumstances, ROC curve analysis showed that the comprehensive model of perfusion parameters based on the random forest algorithm had a sensitivity and specificity of more than 90% and an area under the curve (AUC) of more than 0.9 for detecting ICP increments of both 5 and 10 mmHg. This study demonstrates the feasibility of using perfusion EIT to detect ICP increases in real time, which may provide a new method for real-time non-invasive monitoring of patients with increased ICP.

Objective: Differentiating pediatric medulloblastoma (MB) from ependymoma (EA) in the fourth ventricle remains challenging due to overlapping clinical and imaging features. This study aimed to identify distinctive semantic features and develop a feature‐based model for differentiating MB from EA using conventional MRI. Methods: This multi‐center retrospective MRI study enrolled 295 pediatric patients, including 184 MB and 111 EA cases, allocated to the training, internal validation set, and external testing set. Subsequently, 13 semantic features were extracted. After feature selection, quantitative parametric models and mixed parametric models were constructed and evaluated. Finally, three junior and three senior radiologists completed independent and model‐assisted assessments. Results: MB showed significantly greater left–right/upper‐lower (0.98 vs. 0.76) and anterior–posterior/upper‐lower (0.88 vs. 0.66) diameter ratios compared to EA (both p < 0.001). The pathognomonic “sea anemone sign” (100% specific for MB) occurred in 38.60% of MB cases. The support vector machine (SVM) model achieved optimal performance, with areas under the receiver operating characteristic curves (AUCs) of 0.946/0.921/0.915 and accuracies of 0.906/0.907/0.832 across training/internal validation/external testing sets. With SVM model assistance, diagnostic performance improved for both senior and junior radiologists across all datasets. In the external testing set, the AUCs increased to 0.849–0.893 for junior radiologists and 0.893–0.913 for senior radiologists, with the accuracies of 0.832–0.858 and 0.850–0.867, respectively. Conclusion: The sea anemone sign is highly specific for MB. The SVM model using conventional MRI semantic features achieved robust discrimination between MB and EA, significantly augmenting the diagnostic accuracy of radiologist assessment.