Enhancing premature ventricular contraction localization through electrocardiographic imaging and cardiac digital twins

Premature ventricular contractions (PVCs) represent a common and clinically significant cardiac arrhythmia, contributing to a spectrum of cardiovascular disorders. Accurate localization of the origin of PVCs is essential for devising targeted therapeutic strategies and refining our comprehension of ventricular arrhythmogenesis. Traditionally, the 12-lead ECG has been the go-to diagnostic tool for PVCs. However, individual anatomical differences and inter-patient electrophysiology variability limit its effectiveness. This study presents a new method that combines electrocardiographic imaging (ECGI) with the concept of cardiac digital twins (ECGI-DT) to improve the accuracy of pinpointing the source of PVCs. By simulating a database of PVCs, we developed an ECGI-DT capable of estimating the origins of PVCs with much greater precision than possible previously. This study shows a notable improvement in identifying the initial site of PVC origin using ECGI-DT compared to ECGI alone: the average localization error dropped from 30.69 ± 23.71 mm with standard ECGI to 7.81 ± 3.82 mm using the ECGI-DT method. This marked reduction in error highlights the potential of ECGI-DT in revolutionizing PVC diagnosis and treatment. With its ability to provide more accurate and reliable data, ECGI-DT could improve the planning of catheter ablation treatments, a preferred intervention for managing PVCs that face challenges such as high costs and in some cases long procedure times.

Can a Cochlear Implant Be Used as an Electrical Impedance Tomography Device?

The imaging of the live cochlea is a challenging task. Regardless of the quality of images obtained from modern clinical imaging techniques, the internal structures of the cochlea mainly remain obscured. Electrical impedance tomography (EIT) is a safe, low-cost alternative medical imaging technique with applications in various clinical scenarios. In this article, EIT is investigated as an alternative method to image and extract the centre of gravity of the modiolus in vivo. This information can be used to augment present postoperative medical imaging techniques to investigate the cochlea. The cochlear implant EIT system was simulated by modelling user-specific electrode array trajectories within a simple conductive medium containing an inhomogeneity representing the modiolus. The method included an adapted adjacent stimulation protocol for data collection. For the image reconstruction, NOSER and Tikhonov priors were considered. A parameter analysis was conducted to find the most robust combination of image priors and hyperparameters for this application. The cochlear implant EIT methodology was validated at different noise levels for four electrode array trajectories. Comparing the NOSER and Tikhonov priors, it was observed that the NOSER prior exhibits superior centre of gravity localisation performance in cochlear implant EIT image reconstruction for different noise levels and user-dependent variability in electrode array trajectories. Image reconstruction, using a NOSER prior at a hyperparameter value of approximately 0.001, resulted in an average centre of gravity localisation error of less than 4% for all electrode array trajectories using difference imaging and less than 5.5% using absolute imaging.

Past, present, and future of electrical impedance tomography and myography for medical applications: a scoping review

This scoping review summarizes two emerging electrical impedance technologies: electrical impedance myography (EIM) and electrical impedance tomography (EIT). These methods involve injecting a current into tissue and recording the response at different frequencies to understand tissue properties. The review discusses basic methods and trends, particularly the use of electrodes: EIM uses electrodes for either injection or recording, while EIT uses them for both. Ag/AgCl electrodes are prevalent, and current injection is preferred over voltage injection due to better resistance to electrode wear and impedance changes. Advances in digital processing and integrated circuits have shifted EIM and EIT toward digital acquisition, using voltage-controlled current sources (VCCSs) that support multiple frequencies. The review details powerful processing algorithms and reconstruction tools for EIT and EIM, examining their strengths and weaknesses. It also summarizes commercial devices and clinical applications: EIT is effective for detecting cancerous tissue and monitoring pulmonary issues, while EIM is used for neuromuscular disease detection and monitoring. The role of machine learning and deep learning in advancing diagnosis, treatment planning, and monitoring is highlighted. This review provides a roadmap for researchers on device evolution, algorithms, reconstruction tools, and datasets, offering clinicians and researchers information on commercial devices and clinical studies for effective use and innovative research.

Development and Validation of a Portable EIT System for Real-Time Respiratory Monitoring

This work contributes to the improvement of novel medical technologies for the prevention and treatment of diseases. Electrical impedance tomography (EIT) has gained attention as a valuable tool for non-invasive monitoring providing real-time insights. The purpose of this work is to develop and validate a novel portable EIT system with a small form factor for respiratory monitoring. The device uses a 16-electrode architecture with adjacent stimulation and measurement patterns, an integrated circuit current source and a single high-speed ADC operating with multiplexers to stimulate and measure across all electrodes. Tests were conducted on 25 healthy subjects who performed a pulmonary function test with a flowmeter while using the EIT device. The results showed a good performance of the device, which was able to recognize all respirations correctly, and from the EIT signals and images, correlations of 96.7% were obtained for instantaneous respiratory rate and 96.1% for tidal volume prediction. These results validate the preliminary technical feasibility of the EIT system and demonstrates its potential as a reliable tool for non-invasive respiratory assessment. The significance of this work lies in its potential to democratize advanced respiratory monitoring technologies, making them accessible to a wider population, including those in remote or underserved areas.

DEIT-Based Bone Position and Orientation Estimation for Robotic Support in Total Knee Arthroplasty-A Computational Feasibility Study

Total knee arthroplasty (TKA) is a well-established and successful treatment option for patients with end-stage osteoarthritis of the knee, providing high patient satisfaction. Robotic systems have been widely adopted to perform TKA in orthopaedic centres. The exact spatial positions of the femur and tibia are usually determined through pinned trackers, providing the surgeon with an exact illustration of the axis of the lower limb. The drilling of holes required for mounting the trackers creates weak spots, causing adverse events such as bone fracture. In the presented computational feasibility study, time differential electrical impedance tomography is used to locate the femur positions, thereby the difference in conductivity distribution between two distinct states s0 and s1 of the measured object is reconstructed. The overall approach was tested by simulating five different configurations of thigh shape and considered tissue conductivity distributions. For the cylinder models used for verification and reference, the reconstructed position deviated by about ≈1 mm from the actual bone centre. In case of models mimicking a realistic cross section of the femur position deviated between 7.9 mm 24.8 mm. For all models, the bone axis was off by about φ=1.50° from its actual position.

Determination of optimal positive end-expiratory pressure using electrical impedance tomography in infants under general anesthesia: Comparison between supine and prone positions

AIMS

This study determined the optimal positive end-expiratory pressure levels in infants in supine and prone positions under general anesthesia using electrical impedance tomography (EIT).

METHODS

This prospective observational single-centre study included infants scheduled for surgery in the prone position. An electrical impedance tomography sensor was applied after inducing general anesthesia. The optimal positive end-expiratory pressure in the supine position was determined in a decremental trial based on EIT and compliance. Subsequently, the patient's position was changed to prone. Electrical impedance tomography parameters, including global inhomogeneity index, regional ventilation delay, opening pressure, the centre of ventilation, and pendelluft volume, were continuously obtained up to 1 h after prone positioning. The optimal positive end-expiratory pressure in the prone position was similarly determined.

RESULTS

Data from 30 infants were analyzed. The mean value of electrical impedance tomography-based optimal positive end-expiratory pressure in the prone position was significantly higher than that in the supine position [10.9 (1.6) cmH2O and 6.1 (0.9) cmH2O, respectively (p < .001)]. Significant differences were observed between electrical impedance tomography- and compliance-based optimal positive end-expiratory pressure. Peak and mean airway, plateau, and driving pressures increased 1 h after prone positioning compared with those in the supine position. In addition, the centre of ventilation for balance in ventilation between the ventral and dorsal regions improved.

CONCLUSION

The prone position required higher positive end-expiratory pressure than the supine position in mechanically ventilated infants under general anesthesia. EIT is a promising tool to find the optimal positive end-expiratory pressure, which needs to be individualized.

Multi-Path Fusion in SFCF-Net for Enhanced Multi-Frequency Electrical Impedance Tomography

Multi-frequency electrical impedance tomography (mfEIT) offers a nondestructive imaging technology that reconstructs the distribution of electrical characteristics within a subject based on the impedance spectral differences among biological tissues. However, the technology faces challenges in imaging multi-class lesion targets when the conductivity of background tissues is frequency-dependent. To address these issues, we propose a spatial-frequency cross-fusion network (SFCF-Net) imaging algorithm, built on a multi-path fusion structure. This algorithm uses multi-path structures and hyper-dense connections to capture both spatial and frequency correlations between multi-frequency conductivity images, which achieves differential imaging for lesion targets of multiple categories through cross-fusion of information. According to both simulation and physical experiment results, the proposed SFCF-Net algorithm shows an excellent performance in terms of lesion imaging and category discrimination compared to the weighted frequency-difference, U-Net, and MMV-Net algorithms. The proposed algorithm enhances the ability of mfEIT to simultaneously obtain both structural and spectral information from the tissue being examined and improves the accuracy and reliability of mfEIT, opening new avenues for its application in clinical diagnostics and treatment monitoring.

A 3 MHz Low-Error Adaptive Howland Current Source for High-Frequency Bioimpedance Applications

Bioimpedance is a diagnostic sensing method used in medical applications, ranging from body composition assessment to detecting skin cancer. Commonly, discrete-component (and at times integrated) circuit variants of the Howland Current Source (HCS) topology are employed for injection of an AC current. Ideally, its amplitude should remain within 1% of its nominal value across a frequency range, and that nominal value should be programmable. However, the method's applicability and accuracy are hindered due to the current amplitude diminishing at frequencies above 100 kHz, with very few designs accomplishing 1 MHz, and only at a single nominal amplitude. This paper presents the design and implementation of an adaptive current source for bioimpedance applications employing automatic gain control (AGC). The "Adaptive Howland Current Source" (AHCS) was experimentally tested, and the results indicate that the design can achieve less than 1% amplitude error for both 1 mA and 100 µA currents for bandwidths up to 3 MHz. Simulations also indicate that the system can be designed to achieve up to 19% noise reduction relative to the most common HCS design. AHCS addresses the need for high bandwidth AC current sources in bioimpedance spectroscopy, offering automatic output current compensation without constant recalibration. The novel structure of AHCS proves crucial in applications requiring higher β-dispersion frequencies exceeding 1 MHz, where greater penetration depths and better cell status assessment can be achieved, e.g., in the detection of skin or breast cancer.

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.

Layered Fusion Reconstruction Based on Fuzzy Features for Multi-Conductivity Electrical Impedance Tomography

In medical imaging, detecting tissue anomalies is vital for accurate diagnosis and effective treatment. Electrical impedance tomography (EIT) is a non-invasive technique that monitors the changes in electrical conductivity within tissues in real time. However, the current challenge lies in simply and accurately reconstructing multi-conductivity distributions. This paper introduces a layered fusion framework for EIT to enhance imaging in multi-conductivity scenarios. The method begins with pre-imaging and extracts the main object from the fuzzy image to form one layer. Then, the voltage difference in the other layer, where the local anomaly is located, is estimated. Finally, the corresponding conductivity distribution is established, and multiple layers are fused to reconstruct the multi-conductivity distribution. The simulation and experimental results demonstrate that compared to traditional methods, the proposed method significantly improves multi-conductivity separation, precise anomaly localization, and robustness without adding uncertain parameters. Notably, the proposed method has demonstrated exceptional accuracy in local anomaly detection, with positional errors as low as 1% and size errors as low as 33%, which significantly outperforms the traditional method with respective minimum errors of 9% and 228%. This method ensures a balance between the simplicity and accuracy of the algorithm. At the same time, it breaks the constraints of traditional linear methods, struggling to identify multi-conductivity distributions, thereby providing new perspectives for clinical EIT.

How Impedance Measurements and Imaging Can Be Used to Characterize the Conductivity of Tissues During the Workflow of an Electroporation-Based Therapy

In this article we investigate the possibility of using needles, which the interventional radiologist inserts near a deep-seated tumor during an electroporation-based therapy, to characterize the electrical conductivity of patient's tissues. Specifically, we propose to exploit voltage/current measurements and imaging that are performed prior to the application of electroporation pulses. The approach is partly based on the concepts of electrical impedance tomography; however, imaging is used to build a specific geometric model and compensate for the lack of information resulting from the small number of electrodes available. 3D canonical and clinical examples, where a few electrodes surround a tumor, demonstrate the feasibility of this method: solving the inverse problem to estimate tissues conductivity converges in a few iterations. For a given error on the measurement, it is also possible to calculate the error on the estimated conductivities. The uncertainty error with clinical data is at best 5% for one of the tissues identified, due to the limitations of the clinical device used. Various improvements to clinical devices are discussed to make the conductivity estimation more accurate but also to extract more information.

Comparison of noninvasive cardiac output and stroke volume measurements using electrical impedance tomography with invasive methods in a swine model

Pulmonary artery catheterization (PAC) has been used as a clinical standard for cardiac output (CO) measurements on humans. On animals, however, an ultrasonic flow sensor (UFS) placed around the ascending aorta or pulmonary artery can measure CO and stroke volume (SV) more accurately. The objective of this paper is to compare CO and SV measurements using a noninvasive electrical impedance tomography (EIT) device and three invasive devices using UFS, PAC-CCO (continuous CO) and arterial pressure-based CO (APCO). Thirty-two pigs were anesthetized and mechanically ventilated. A UFS was placed around the pulmonary artery through thoracotomy in 11 of them, while the EIT, PAC-CCO and APCO devices were used on all of them. Afterload and contractility were changed pharmacologically, while preload was changed through bleeding and injection of fluid or blood. Twenty-three pigs completed the experiment. Among 23, the UFS was used on 7 pigs around the pulmonary artery. The percentage error (PE) between COUFS and COEIT was 26.1%, and the 10-min concordance was 92.5%. Between SVUFS and SVEIT, the PE was 24.8%, and the 10-min concordance was 94.2%. On analyzing the data from all 23 pigs, the PE between time-delay-adjusted COPAC-CCO and COEIT was 34.6%, and the 10-min concordance was 81.1%. Our results suggest that the performance of the EIT device in measuring dynamic changes of CO and SV on mechanically-ventilated pigs under different cardiac preload, afterload and contractility conditions is at least comparable to that of the PAC-CCO device. Clinical studies are needed to evaluate the utility of the EIT device as a noninvasive hemodynamic monitoring tool.

Iterative Reconstruction Algorithms in Magneto-Acousto-Electrical Computed Tomography (MAE-CT) for Image Quality Improvement

Magneto-acousto-electrical computed tomography (MAE-CT) is a recently developed rotational magneto-acousto-electrical tomography (MAET) method, which can map the conductivity parameter of tissues with high spatial resolution. Since the imaging mode of MAE-CT is similar to that of CT, the reconstruction algorithms for CT are possible to be adopted for MAE-CT. Previous studies have demonstrated that the filtered back-projection (FBP) algorithm, which is one of the most common CT reconstruction algorithms, can be used for MAE-CT reconstruction. However, FBP has some inherent shortcomings of being sensitive to noise and non-uniform distribution of views. In this study, we introduced iterative reconstruction (IR) method in MAE-CT reconstruction and compared its performance with that of the FBP. The numerical simulation, the phantom, and in vitro experiments were performed, and several IR algorithms (ART, SART, SIRT) were used for reconstruction. The results show that the images reconstructed by the FBP and IR are similar when the data is noise-free in the simulation. As the noise level increases, the images reconstructed by SART and SIRT are more robust to the noise than FBP. In the phantom experiment, noise and some stripe artifacts caused by the FBP are removed by SART and SIRT algorithms. In conclusion, the IR method used in CT is applicable in MAE-CT, and it performs better than FBP, which indicates that the state-of-the-art achievements in the CT algorithm can also be adopted for the MAE-CT reconstruction in the future.

A novel two-dimensional phantom for electrical impedance tomography using 3D printing

Electrical impedance tomography (EIT) is an imaging method that can be used to image electrical impedance contrasts within various tissues of the body. To support development of EIT measurement systems, a phantom is required that represents the electrical characteristics of the imaging domain. No existing type of EIT phantom combines good performance in all three characteristics of resistivity resolution, spatial resolution, and stability. Here, a novel EIT phantom concept is proposed that uses 3D printed conductive material. Resistivity is controlled using the 3D printing infill percentage parameter, allowing arbitrary resistivity contrasts within the domain to be manufactured automatically. The concept of controlling resistivity through infill percentage is validated, and the manufacturing accuracy is quantified. A method for making electrical connections to the 3D printed material is developed. Finally, a prototype phantom is printed, and a sample EIT analysis is performed. The resulting phantom, printed with an Ultimaker S3, has high reported spatial resolution of 6.9 µm, 6.9 µm, and 2.5 µm for X, Y, and Z axis directions, respectively (X and Y being the horizontal axes, and Z the vertical). The number of resistivity levels that are manufacturable by varying infill percentage is 15 (calculated by dividing the available range of resistivities by two times the standard deviation of the manufacturing accuracy). This phantom construction technique will allow assessment of the performance of EIT devices under realistic physiological scenarios.

Posterior Approximate Clustering-Based Sensitivity Matrix Decomposition for Electrical Impedance Tomography

This paper introduces a sensitivity matrix decomposition regularization (SMDR) method for electric impedance tomography (EIT). Using k-means clustering, the EIT-reconstructed image can be divided into four clusters, derived based on image features, representing posterior information. The sensitivity matrix is then decomposed into distinct work areas based on these clusters. The elimination of smooth edge effects is achieved through differentiation of the images from the decomposed sensitivity matrix and further post-processing reliant on image features. The algorithm ensures low computational complexity and avoids introducing extra parameters. Numerical simulations and experimental data verification highlight the effectiveness of SMDR. The proposed SMDR algorithm demonstrates higher accuracy and robustness compared to the typical Tikhonov regularization and the iterative penalty term-based regularization method (with an improvement of up to 0.1156 in correlation coefficient). Moreover, SMDR achieves a harmonious balance between image fidelity and sparsity, effectively addressing practical application requirements.

Applied machine learning for stroke differentiation by electrical impedance tomography with realistic numerical models

Electrical impedance tomography (EIT) may have potential to overcome existing limitations in stroke differentiation, enabling low-cost, rapid, and mobile data collection. Combining bioimpedance measurement technologies such as EIT with machine learning classifiers to support decision-making can avoid commonly faced reconstruction challenges due to the nonlinear and ill-posed nature of EIT imaging. Therefore, in this work, we advance this field through a study integrating realistic head models with clinically relevant test scenarios, and a robust architecture consisting of nested cross-validation and principal component analysis. Specifically, realistic head models are designed which incorporate the highly conductive layers of cerebrospinal fluid in the subarachnoid space and ventricles. In total, 135 unique models are created to represent a large patient population, with normal, haemorrhagic, and ischemic brains. Simulated EIT voltage data generated from these models are used to assess the classification performance of support vector machines. Parameters explored include driving frequency, signal-to-noise ratio, kernel function, and composition of binary classes. Classifier accuracy at 60 dB signal-to-noise ratio, reported as mean and standard deviation, are (79.92% ± 10.82%) for lesion differentiation, (74.78% ± 3.79%) for lesion detection, (77.49% ± 15.90%) for bleed detection, and (60.31% ± 3.98%) for ischemia detection (after ruling out bleed). The results for each method were obtained with statistics from 3 independent runs with 17,280 observations, polynomial kernel functions, and feature reduction of 76% by PCA (from 208 to 50 features). While results of this study show promise for stroke differentiation using EIT data, our findings indicate that the achievable accuracy is highly dependent on the classification scenario and application-specific classifiers may be necessary to achieve acceptable accuracy.

MMV-Net: A Multiple Measurement Vector Network for Multifrequency Electrical Impedance Tomography

Multifrequency electrical impedance tomography (mfEIT) is an emerging biomedical imaging modality to reveal frequency-dependent conductivity distributions in biomedical applications. Conventional model-based image reconstruction methods suffer from low spatial resolution, unconstrained frequency correlation, and high computational cost. Deep learning has been extensively applied in solving the EIT inverse problem in biomedical and industrial process imaging. However, most existing learning-based approaches deal with the single-frequency setup, which is inefficient and ineffective when extended to the multifrequency setup. This article presents a multiple measurement vector (MMV) model-based learning algorithm named MMV-Net to solve the mfEIT image reconstruction problem. MMV-Net considers the correlations between mfEIT images and unfolds the update steps of the Alternating Direction Method of Multipliers for the MMV problem (MMV-ADMM). The nonlinear shrinkage operator associated with the weighted l2,1 regularization term of MMV-ADMM is generalized in MMV-Net with a cascade of a Spatial Self-Attention module and a Convolutional Long Short-Term Memory (ConvLSTM) module to better capture intrafrequency and interfrequency dependencies. The proposed MMV-Net was validated on our Edinburgh mfEIT Dataset and a series of comprehensive experiments. The results show superior image quality, convergence performance, noise robustness, and computational efficiency against the conventional MMV-ADMM and the state-of-the-art deep learning methods.

Microscale 3-D Capacitance Tomography with a CMOS Sensor Array

Electrical capacitance tomography (ECT) is a non-optical imaging technique in which a map of the interior permittivity of a volume is estimated by making capacitance measurements at its boundary and solving an inverse problem. While previous ECT demonstrations have often been at centimeter scales, ECT is not limited to macroscopic systems. In this paper, we demonstrate ECT imaging of polymer microspheres and bacterial biofilms using a CMOS microelectrode array, achieving spatial resolution of 10 microns. Additionally, we propose a deep learning architecture and an improved multi-objective training scheme for reconstructing out-of-plane permittivity maps from the sensor measurements. Experimental results show that the proposed approach is able to resolve microscopic 3-D structures, achieving 91.5% prediction accuracy on the microsphere dataset and 82.7% on the biofilm dataset, including an average of 4.6% improvement over baseline computational methods.

Cross modality generative learning framework for anatomical transitive Magnetic Resonance Imaging (MRI) from Electrical Impedance Tomography (EIT) image

This paper presents a cross-modality generative learning framework for transitive magnetic resonance imaging (MRI) from electrical impedance tomography (EIT). The proposed framework is aimed at converting low-resolution EIT images to high-resolution wrist MRI images using a cascaded cycle generative adversarial network (CycleGAN) model. This model comprises three main components: the collection of initial EIT from the medical device, the generation of a high-resolution transitive EIT image from the corresponding MRI image for domain adaptation, and the coalescence of two CycleGAN models for cross-modality generation. The initial EIT image was generated at three different frequencies (70 kHz, 140 kHz, and 200 kHz) using a 16-electrode belt. Wrist T1-weighted images were acquired on a 1.5T MRI. A total of 19 normal volunteers were imaged using both EIT and MRI, which resulted in 713 paired EIT and MRI images. The cascaded CycleGAN, end-to-end CycleGAN, and Pix2Pix models were trained and tested on the same cohort. The proposed method achieved the highest accuracy in bone detection, with 0.97 for the proposed cascaded CycleGAN, 0.68 for end-to-end CycleGAN, and 0.70 for the Pix2Pix model. Visual inspection showed that the proposed method reduced bone-related errors in the MRI-style anatomical reference compared with end-to-end CycleGAN and Pix2Pix. Multifrequency EIT inputs reduced the testing normalized root mean squared error of MRI-style anatomical reference from 67.9% ± 12.7% to 61.4% ± 8.8% compared with that of single-frequency EIT. The mean conductivity values of fat and bone from regularized EIT were 0.0435 ± 0.0379 S/m and 0.0183 ± 0.0154 S/m, respectively, when the anatomical prior was employed. These results demonstrate that the proposed framework is able to generate MRI-style anatomical references from EIT images with a good degree of accuracy.

Improved Electrical Impedance Tomography Reconstruction via a Bayesian Approach With an Anatomical Statistical Shape Model

OBJECTIVE

electrical impedance tomography (EIT) is a promising technique for rapid and continuous bedside monitoring of lung function. Accurate and reliable EIT reconstruction of ventilation requires patient-specific shape information. However, this shape information is often not available and current EIT reconstruction methods typically have limited spatial fidelity. This study sought to develop a statistical shape model (SSM) of the torso and lungs and evaluate whether patient-specific predictions of torso and lung shape could enhance EIT reconstructions in a Bayesian framework.

METHODS

torso and lung finite element surface meshes were fitted to computed tomography data from 81 participants, and a SSM was generated using principal component analysis and regression analyses. Predicted shapes were implemented in a Bayesian EIT framework and were quantitatively compared to generic reconstruction methods.

RESULTS

Five principal shape modes explained 38% of the cohort variance in lung and torso geometry, and regression analysis yielded nine total anthropometrics and pulmonary function metrics that significantly predicted these shape modes. Incorporation of SSM-derived structural information enhanced the accuracy and reliability of the EIT reconstruction as compared to generic reconstructions, demonstrated by reduced relative error, total variation, and Mahalanobis distance.

CONCLUSION

As compared to deterministic approaches, Bayesian EIT afforded more reliable quantitative and visual interpretation of the reconstructed ventilation distribution. However, no conclusive improvement of reconstruction performance using patient specific structural information was observed as compared to the mean shape of the SSM.

SIGNIFICANCE

The presented Bayesian framework builds towards a more accurate and reliable method for ventilation monitoring via EIT.

Influence of Excitation Frequency on the Performance of Peripheral Blood Flow Imaging using Electrical Impedance Tomography

This paper presents a method for selecting the efficient excitation frequency of Electrical Impedance Tomography (EIT) for imaging peripheral blood flow with high spatial-temporal performance. Using a simulation study, we selected the excitation frequency of 16 kHz to visualize the pulsation of arteries with a high sensitivity. We then conducted a subjective study using 16 electrodes and showed that the conductivity distribution is similar to the anatomical structure of the forearm. Moreover, the integrated conductivity spectrum showed a peak corresponding to a heart rate measurement obtained using a PPG sensor at the fingertip. Therefore, we conclude that this system can capture the spatial-temporal signals related to peripheral artery blood flow by using the selected excitation frequency.

Effective Electrical Impedance Tomography Based on Enhanced Encoder-Decoder Using Atrous Spatial Pyramid Pooling Module

Electrical impedance tomography (EIT) is a noninvasive and radiation-free imaging method. As a "soft-field" imaging technique, in EIT, the target signal in the center of the measured field is frequently swamped by the target signal at the edge, which restricts its further application. To alleviate this problem, this study presents an enhanced encoder-decoder (EED) method with an atrous spatial pyramid pooling (ASPP) module. The proposed method enhances the ability to detect central weak targets by constructing an ASPP module that integrates multiscale information in the encoder. The multilevel semantic features are fused in the decoder to improve the boundary reconstruction accuracy of the center target. The average absolute error of the imaging results by the EED method reduced by 82.0%, 83.6%, and 36.5% in simulation experiments and 83.0%, 83.2%, and 36.1% in physical experiments compared with the errors of the damped least-squares algorithm, Kalman filtering method, and U-Net-based imaging method, respectively. The average structural similarity improved by 37.3%, 42.9%, and 3.6%, and 39.2%, 45.2%, and 3.8% in the simulation and physical experiments, respectively. The proposed method provides a practical and reliable means of extending the application of EIT by solving the problem of weak central target reconstruction under the effect of strong edge targets in EIT.

Optimization of electrode positions for equalizing local spatial performance of a tomographic tactile sensor

A tomographic tactile sensor based on the contact resistance of conductors is a high sensitive pressure distribution imaging method and has advantages on the flexibility and scalability of device. While the addition of internal electrodes improves the sensor's spatial resolution, there still remain variations in resolution that depend on the contact position. In this study, we propose an optimization algorithm for electrode positions that improves entire spatial resolution by compensating for local variations in spatial resolution. Simulation results for sensors with 16 or 64 electrodes show that the proposed algorithm improves performance to 0.81 times and 0.93 times in the worst spatial resolution region of the detection area compared to equally spaced grid electrodes. The proposed methods enable tomographic tactile sensors to detect contact pressure distribution more accurately than the conventional methods, providing high-performance tactile sensing for many applications.

Towards continuous EIT monitoring for hemorrhagic stroke patients

The practical implementation of continuous monitoring of stroke patients by Electrical Impedance Tomography (EIT) is addressed. In a previous paper, we have demonstrated EIT sensitivity to cerebral hemodynamics, using scalp-mounted electrodes, very low-noise measurements, and a novel image reconstruction method. In the present paper, we investigate the potential to adapt that system for clinical application, by using 50% fewer electrodes and by incorporating into the measurement protocol an additional high-frequency measurement to provide an effective reference. Previously published image reconstruction methods for multi-frequency EIT are substantially improved by exploiting the forward calculations enabled by the detailed head model, particularly to make the referencing method more robust and to attempt to remove the effects of modelling error. Images are presented from simulation of a typical hemorrhagic stroke and its growth. These results are encouraging for exploration of the potential clinical benefit of the methodology in long-term monitoring of hemorrhagic stroke.

Machine learning-based signal quality assessment for cardiac volume monitoring in electrical impedance tomography

Owing to recent advances in thoracic electrical impedance tomography (EIT), a patient’s hemodynamic function can be noninvasively and continuously estimated in real-time by surveilling a cardiac volume signal (CVS) associated with stroke volume and cardiac output. In clinical applications, however, a CVS is often of low quality, mainly because of the patient’s deliberate movements or inevitable motions during clinical interventions. This study aims to develop a signal quality indexing method that assesses the influence of motion artifacts on transient CVSs. The assessment is performed on each cardiac cycle to take advantage of the periodicity and regularity in cardiac volume changes. Time intervals are identified using the synchronized electrocardiography system. We apply divergent machine-learning methods, which can be sorted into discriminative-model and manifold-learning approaches. The use of machine-learning could be suitable for our real-time monitoring application that requires fast inference and automation as well as high accuracy. In the clinical environment, the proposed method can be utilized to provide immediate warnings so that clinicians can minimize confusion regarding patients’ conditions, reduce clinical resource utilization, and improve the confidence level of the monitoring system. Numerous experiments using actual EIT data validate the capability of CVSs degraded by motion artifacts to be accurately and automatically assessed in real-time by machine learning. The best model achieved an accuracy of 0.95, positive and negative predictive values of 0.96 and 0.86, sensitivity of 0.98, specificity of 0.77, and AUC of 0.96.

Regularization Solver Guided FISTA for Electrical Impedance Tomography

Electrical impedance tomography (EIT) is non-destructive monitoring technology that can visualize the conductivity distribution in the observed area. The inverse problem for imaging is characterized by a serious nonlinear and ill-posed nature, which leads to the low spatial resolution of the reconstructions. The iterative algorithm is an effective method to deal with the imaging inverse problem. However, the existing iterative imaging methods have some drawbacks, such as random and subjective initial parameter setting, very time consuming in vast iterations and shape blurring with less high-order information, etc. To solve these problems, this paper proposes a novel fast convergent iteration method for solving the inverse problem and designs an initial guess method based on an adaptive regularization parameter adjustment. This method is named the Regularization Solver Guided Fast Iterative Shrinkage Threshold Algorithm (RS-FISTA). The iterative solution process under the L1-norm regular constraint is derived in the LASSO problem. Meanwhile, the Nesterov accelerator is introduced to accelerate the gradient optimization race in the ISTA method. In order to make the initial guess contain more prior information and be independent of subjective factors such as human experience, a new adaptive regularization weight coefficient selection method is introduced into the initial conjecture of the FISTA iteration as it contains more accurate prior information of the conductivity distribution. The RS-FISTA method is compared with the methods of Landweber, CG, NOSER, Newton-Raphson, ISTA and FISTA, six different distributions with their optimal parameters. The SSIM, RMSE and PSNR of RS-FISTA methods are 0.7253, 3.44 and 37.55, respectively. In the performance test of convergence, the evaluation metrics of this method are relatively stable at 30 iterations. This shows that the proposed method not only has better visualization, but also has fast convergence. It is verified that the RS-FISTA algorithm is the better algorithm for EIT reconstruction from both simulation and physical experiments.

Real-Time Measurements of Relative Tidal Volume and Stroke Volume Using Electrical Impedance Tomography with Spatial Filters: A Feasibility Study in a Swine Model Under Normal and Reduced Ventilation

Continuous monitoring of both hemodynamic and respiratory parameters would be beneficial to patients, e.g., those in intensive care unit. The objective of this exploratory animal study was to test the feasibility of simultaneous measurements of relative tidal volume (rTV) and relative stroke volume (rSV) using an electrical impedance tomography (EIT) device equipped with a new real-time source separation algorithm implemented as two spatial filters. Five pigs were anesthetized and mechanically ventilated. The supplied tidal volume from a mechanical ventilator was reduced to 70, 50 and 30% from the 100% normal volume to simulate hypoventilation. The respiratory volume signal and cardiac volume signal were generated by applying the spatial filters to the acquired EIT data, from which values of rTV and rSV were extracted. The measured rTV values were compared with the TV values from the mechanical ventilator using the four-quadrant concordance analysis method. For changes in TV, the concordance rate in each animal ranged from 81.8% to 100%, while it was 92.5% when the data from all five animals were pooled together. When the measured rTV values for each animal were scaled to the absolute TV_EIT values in mL using the TV_Vent data from the mechanical ventilator, the smallest 95% limits of agreement (LoA) were - 6.04 and 7.44 mL for the 70% ventilation level, and the largest 95% LoA were - 18.1 and 19.4 mL for the 50% ventilation level. The percentage error between TV_EIT and TV_Vent was 10.3%. Although similar statistical analyses on rSV data could not be performed due to limited intra-animal variability, changes in rSV values measured by the EIT device were comparable to those measured by an invasive hemodynamic monitor. In this animal study, we were able to demonstrate the feasibility of an EIT device for noninvasive and simultaneous measurements of rTV and rSV in real time. However, the performance of the real-time source separation method needs to be further validated on animals and human subjects, particularly over a wide range of SV values. Future clinical studies are needed to assess the potential usefulness of the new method in dynamic cardiopulmonary monitoring and explore other clinical applications.

Structural priors represented by discrete cosine transform improve EIT functional imaging

Structural prior information can improve electrical impedance tomography (EIT) reconstruction. In this contribution, we introduce a discrete cosine transformation-based (DCT-based) EIT reconstruction algorithm to demonstrate a way to incorporate the structural prior with the EIT reconstruction process. Structural prior information is obtained from other available imaging methods, e.g., thorax-CT. The DCT-based approach creates a functional EIT image of regional lung ventilation while preserving the introduced structural information. This leads to an easier interpretation in clinical settings while maintaining the advantages of EIT in terms of bedside monitoring during mechanical ventilation. Structural priors introduced in the DCT-based approach are of two categories in terms of different levels of information included: a contour prior only differentiates lung and non-lung region, while a detail prior includes information, such as atelectasis, within the lung area. To demonstrate the increased interpretability of the EIT image through structural prior in the DCT-based approach, the DCT-based reconstructions were compared with reconstructions from a widely applied one-step Gauss-Newton solver with background prior and from the advanced GREIT algorithm. The comparisons were conducted both on simulation data and retrospective patient data. In the simulation, we used two sets of forward models to simulate different lung conditions. A contour prior and a detail prior were derived from simulation ground truth. With these two structural priors, the reconstructions from the DCT-based approach were compared with the reconstructions from both the one-step Gauss-Newton solver and the GREIT. The difference between the reconstructions and the simulation ground truth is calculated by the ℓ2-norm image difference. In retrospective patient data analysis, datasets from six lung disease patients were included. For each patient, a detail prior was derived from the patient's CT, respectively. The detail prior was used for the reconstructions using the DCT-based approach, which was compared with the reconstructions from the GREIT. The reconstructions from the DCT-based approach are more comprehensive and interpretable in terms of preserving the structure specified by the priors, both in simulation and retrospective patient data analysis. In simulation analysis, the ℓ2-norm image difference of the DCT-based approach with a contour prior decreased on average by 34% from GREIT and 49% from the Gauss-Newton solver with background prior; for reconstructions of the DCT-based approach with detail prior, on average the ℓ2-norm image difference is 53% less than GREIT and 63% less than the reconstruction with background prior. In retrospective patient data analysis, the reconstructions from both the DCT-based approach and GREIT can indicate the current patient status, but the DCT-based approach yields more interpretable results. However, it is worth noting that the preserved structure in the DCT-based approach is derived from another imaging method, not from the EIT measurement. If the structural prior is outdated or wrong, the result might be misleadingly interpreted, which induces false clinical conclusions. Further research in terms of evaluating the validity of the structural prior and detecting the outdated prior is necessary.

Deep feature-domain matching for cardiac-related component separation from a chest electrical impedance tomography image series: proof-of-concept study

Objectives.The cardiac-related component in chest electrical impedance tomography (EIT) measurement is of potential value to pulmonary perfusion monitoring and cardiac function measurement. In a spontaneous breathing case, cardiac-related signals experience serious interference from ventilation-related signals. Traditional cardiac-related signal-separation methods are usually based on certain features of signals. To further improve the separation accuracy, more comprehensive features of the signals should be exploited.Approach.We propose an unsupervised deep-learning method called deep feature-domain matching (DFDM), which exploits the feature-domain similarity of the desired signals and the breath-holding signals. This method is characterized by two sub-steps. In the first step, a novel Siamese network is designed and trained to learn common features of breath-holding signals; in the second step, the Siamese network is used as a feature-matching constraint between the separated signals and the breath-holding signals.Main results.The method is first tested using synthetic data, and the results show satisfactory separation accuracy. The method is then tested using the data of three patients with pulmonary embolism, and the consistency between the separated images and the radionuclide perfusion scanning images is checked qualitatively.Significance.The method uses a lightweight convolutional neural network for fast network training and inference. It is a potential method for dynamic cardiac-related signal separation in clinical settings.

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.

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.

Characteristics and topic trends on electrical impedance tomography hardware publications

Objective: Electrical impedance tomography (EIT) is a technique to measure electrical properties of tissue. With the progress of modern integrated circuits and microchips, EIT instrumentation becomes an active research area to improve all aspects of device performance. Plenty of studies on EIT hardware have been presented in prestigious journals. This study explores publications on EIT hardware to identify the developing hotspots and trends. Method: Publications covering EIT hardware on the Web of Science Core Collection (WoSCC) database from 1989 to 2021 were collected for bibliometric analysis. CiteSpace and VOS viewer were used to study the characteristics of the publications. Main results: A total of 592 publications were analyzed, showing that the number of annual publications steadily increased. China, England, and South Korea were the most prolific countries on EIT hardware publications with productive native institutions and authors. Research topics spread out in "bio-electrical impedance imaging", "hardware optimization", "algorithms" and "clinical applications" (e.g., tissue, lung, brain, and oncology). Hardware research in "pulmonary" and "hemodynamic" applications focused on monitoring and were represented by silhouette recognition and dynamic imaging while research in "tumor and tissue" and "brain" applications focused on diagnosis and were represented by optimization of precision. Electrode development was a research focus through the years. Imaging precision and bioavailability of hardware optimization may be the future trend. Conclusion: Overall, system performance, particularly in the areas of system bandwidth and precision in applications may be the future directions of hardware research.

Electrical Impedance Tomography Based on Grey Wolf Optimized Radial Basis Function Neural Network

Electrical impedance tomography (EIT) is a non-invasive, radiation-free imaging technique with a lot of promise in clinical monitoring. However, since EIT image reconstruction is a non-linear, pathological, and ill-posed issue, the quality of the reconstructed images needs constant improvement. To increase image reconstruction accuracy, a grey wolf optimized radial basis function neural network (GWO-RBFNN) is proposed in this paper. The grey wolf algorithm is used to optimize the weights in the radial base neural network, determine the mapping between the weights and the initial position of the grey wolf, and calculate the optimal position of the grey wolf to find the optimal solution for the weights, thus improving the image resolution of EIT imaging. COMSOL and MATLAB were used to numerically simulate the EIT system with 16 electrodes, producing 1700 simulation samples. The standard Landweber, RBFNN, and GWO-RBFNN approaches were used to train the sets separately. The obtained image correlation coefficient (ICC) of the test set after training with GWO-RBFNN is 0.9551. After adding 30, 40, and 50 dB of Gaussian white noise to the test set, the attained ICCs with GWO-RBFNN are 0.8966, 0.9197, and 0.9319, respectively. The findings reveal that the proposed GWO-RBFNN approach outperforms the existing methods when it comes to image reconstruction.

Multi-frequency Electrical Impedance Pneumography System as Point-Of-Care Device

In this work we present the development of a multi-frequency electrical impedance pneumography (EIP) system based on a portable acquisition device and a mobile platform. This design is intended as an upgrade to our previous device for clinical use in the screening of patients with pulmonary diseases. The acquisition device uses the bioimpedance analog front end MAX30001, a mux/demux stage, Bluetooth 4.0 communication and an ESP32 microcontroller unit. It generates an excitation current of 8 μA_pp in a range of selectable frequencies from 1 kHz to 130 kHz. The mobile platform provides a real-time respiration signal at 64 S/s and allows the configuration of the device. Results show less error in higher frequencies, which are the most common in these applications, ensuring the feasibility of the system for use in humans. Some hardware constraints related to EIP integrated circuits are discussed, an their effect in the signal acquisition.

Minimal sensor arrays for localizing objects using an electric sense

Weakly electric fish encode perturbations in a self-generated electric field to sense their environment. Localizing objects using this electric sense requires that distance be decoded from a two-dimensional \emph{electric image} of the field perturbations on their skin. Many studies of object localization by weakly electric fish, and by electric sensing in a generic context, have focused on extracting location information from different features of the electric image. Some of these studies have also considered the additional information gained from sampling the electric image at different times, and from different viewpoints. Here, we take a different perspective and instead consider the information available at a single point in space (i.e. a single sensor or receptor) at a single point in time (i.e. constant field). By combining the information from multiple receptors, we show that an object's distance can be unambiguously encoded by as few as four receptors at specific locations on a sensing surface in a manner that is relatively robust to environmental noise. This provides a lower bound on the information (i.e. receptor array size) required to decode the three-dimensional location of an object using an electric sense.

Surfactant Treatment Shows Higher Correlation Between Ventilator and EIT Tidal Volumes in an RDS Animal Model

Neonatal respiratory distress syndrome (RDS) is a condition of pulmonary surfactant insufficiency in the premature newborn. As such, artificial pulmonary surfactant administration is a key treatment. Despite continued improvement in the clinical outcomes of RDS, there are currently no established bedside tools to monitor whether pulmonary surfactant is effectively instilled throughout the lungs. Electrical impedance tomography (EIT) is an emerging technique in which physiological functions are monitored on the basis of temporal changes in conductivity of different tissues in the body. In this preliminary study, we aimed to assess how EIT tidal volumes correlate with ventilator tidal volumes in an RDS animal model, namely untreated, surfactant-treated, and normal control rabbit pups. Tidal volumes were measured simultaneously on an EIT system and a mechanical ventilator and compared at different peak inspiratory pressures. The linear correlation between tidal volumes measured by EIT and by ventilator had an R² of 0.71, 0.76 and 0.86 in the untreated, surfactant-treated, and normal control groups, respectively. Bland–Altman analysis showed a good correlation between the measurements obtained with these two modalities. The intraclass correlation coefficients (ICC) between ventilator tidal volume and EIT tidal volume were 0.83, 0.87, and 0.93 (all p < 0.001) in the untreated, surfactant-treated, and normal control groups, respectively, indicating that the higher ICC value, the better inflated status of the lung. In conclusion, we demonstrated that EIT tidal volume correlated with ventilator tidal volume. ICC was higher in the surfactant treated group.

An alternative excitation method for electrical impedance tomography

Electrical impedance tomography (EIT) can be utilized to image the conductivity distribution of material under test. The EIT measurements depend on the quality in the current injection and voltage measuring circuits. The current source plays a vital role in the EIT instruments. In most of the research studies, the push-pull current sources were employed for the source and sink signal generation. It usually requires frequent calibration to achieve proper functioning, especially for the sweeping frequency measurements. In this paper, an alternative excitation method has been proposed for simplifying the design of the current source in EIT instruments, which aims to achieve the performance of the push-pull current source by using a single-ended current source. It could offer the following advantages: (1) hardware simplification and (2) reduced requirements on current source calibration. The corrected measurements could be consistent with that using push-pull excitation, as confirmed by the numerical simulations. In addition, the reconstructed images have also been investigated to illustrate the effectiveness of the proposed method.

3-Dimensional electrical impedance spectroscopy for in situ endoluminal mapping of metabolically active plaques

Electrical impedance spectroscopy (EIS) has been recognized to characterize oxidized low-density lipoprotein (oxLDL) in the metabolically active plaque. However, intravascular deployment of 3-D EIS-derived electrical impedance tomography (EIT) for endoluminal mapping of oxLDL-laden arterial walls remains an unmet clinical challenge. To this end, we designed the 6-point microelectrode arrays that were circumferentially configurated onto the balloon catheter for 15 intravascular EIS permutations. In parallel, we created the metabolically active plaques by performing partial ligation of right carotid artery in Yorkshire mini-pigs (n = 6 males), followed by demonstrating the plaque progression at baseline, 8 weeks, and 16 weeks of high-fat diet via computed tomography (CT) angiogram. Next, we deployed the 3-D EIS sensors to the right and left carotid arteries, and we demonstrated 3-D EIS mapping of metabolically active endolumen in the right but not left carotid arteries as evidenced by the positive E06 immunostaining for oxLDL-laden regions. By considering electrical conductivity (σ) and permittivity (ε) properties of collagen, lipid, and smooth muscle presence in the arterial wall, we further validated the 3-D EIS-derived EIT by reconstructing the histology of right and left carotid arteries for the finite element modeling of the oxLDL-laden endolumen, and we accurately predicted 3-D EIS mapping. Thus, we establish the capability of 3-D EIS-derived EIT to detect oxLDL-laden arterial walls with translational implication to predict metabolically active plaques prone to acute coronary syndromes or stroke.

Adjunctive Diagnostic Methods for Skin Cancer Detection: A Review of Electrical Impedance-Based Techniques

Skin cancer is among the fastest-growing cancers with an excellent prognosis, if detected early. However, the current method of diagnosis by visual inspection has several disadvantages such as overlapping tumor characteristics, subjectivity, low sensitivity, and specificity. Hence, several adjunctive diagnostic techniques such as thermal imaging, optical imaging, ultrasonography, tape stripping methods, and electrical impedance imaging are employed along with visual inspection to improve the diagnosis. Electrical impedance-based skin cancer detection depends upon the variations in electrical impedance characteristics of the transformed cells. The information provided by this technique is fundamentally different from other adjunctive techniques and thus has good prospects. Depending on the stage, type, and location of skin cancer, various impedance-based devices have been developed. These devices when used as an adjunct to visual methods have increased the sensitivity and specificity of skin cancer detection up to 100% and 87%, respectively, thus demonstrating their potential to minimize unnecessary biopsies. In this review, the authors track the advancements and progress made in this technique for the detection of skin cancer, focusing mainly on the advantages and limitations in the clinical setting. © 2022 Bioelectromagnetics Society.

An Ex Vivo Study of Outward Electrical Impedance Tomography (OEIT) for Intravascular Imaging

OBJECTIVE

Atherosclerosis is a chronic immuno-inflammatory condition emerging in arteries and considered the cause of a myriad of cardiovascular diseases. Atherosclerotic lesion characterization through invasive imaging modalities is essential in disease evaluation and determining intervention strategy. Recently, electrical properties of the lesions have been utilized in assessing its vulnerability mainly owing to its capability to differentiate lipid content existing in the lesion, albeit with limited detection resolution. Electrical impedance tomography is the natural extension of conventional spectrometric measurement by incorporating larger number of interrogating electrodes and advanced algorithm to achieve imaging of target objects and thus provides significantly richer information. It is within this context that we develop Outward Electrical Impedance Tomography (OEIT), aimed at intravascular imaging for atherosclerotic lesion characterization.

METHODS

We utilized flexible electronics to establish the 32-electrode OEIT device with outward facing configuration suitable for imaging of vessels. We conducted comprehensive studies through simulation model and ex vivo setup to demonstrate the functionality of OEIT.

RESULTS

Quantitative characterization for OEIT regarding its proximity sensing and conductivity differentiation was achieved using well-controlled experimental conditions. Imaging capability for OEIT was further verified with phantom setup using porcine aorta to emulate in vivo environment.

CONCLUSION

We have successfully demonstrated a novel tool for intravascular imaging, OEIT, with unique advantages for atherosclerosis detection.

SIGNIFICANCE

This study demonstrates for the first time a novel electrical tomography-based platform for intravascular imaging, and we believe it paves the way for further adaptation of OEIT for intravascular detection in more translational settings and offers great potential as an alternative imaging tool for medical diagnosis.

Respiration monitoring in PACU using ventilation and gas exchange parameters

The importance of perioperative respiration monitoring is highlighted by high incidences of postoperative respiratory complications unrelated to the original disease. The objectives of this pilot study were to (1) simultaneously acquire respiration rate (RR), tidal volume (TV), minute ventilation (MV), SpO₂ and PetCO₂ from patients in post-anesthesia care unit (PACU) and (2) identify a practical continuous respiration monitoring method by analyzing the acquired data in terms of their ability and reliability in assessing a patient’s respiratory status. Thirteen non-intubated patients completed this observational study. A portable electrical impedance tomography (EIT) device was used to acquire RR_EIT, TV and MV, while PetCO₂, RR_Cap and SpO₂ were measured by a Capnostream35. Hypoventilation and respiratory events, e.g., apnea and hypopnea, could be detected reliably using RR_EIT, TV and MV. PetCO₂ and SpO₂ provided the gas exchange information, but were unable to detect hypoventilation in a timely fashion. Although SpO₂ was stable, the sidestream capnography using the oronasal cannula was often unstable and produced fluctuating PetCO₂ values. The coefficient of determination (R²) value between RR_EIT and RR_Cap was 0.65 with a percentage error of 52.5%. Based on our results, we identified RR, TV, MV and SpO₂ as a set of respiratory parameters for robust continuous respiration monitoring of non-intubated patients. Such a respiration monitor with both ventilation and gas exchange parameters would be reliable and could be useful not only for respiration monitoring, but in making PACU discharge decisions and adjusting opioid dosage on general hospital floor. Future studies are needed to evaluate the potential clinical utility of such an integrated respiration monitor.

A Multi-Frequency Focused Impedance Measurement System Based on Analogue Synchronous Peak Detection

Monitoring of anatomical structures and physiological processes by electrical impedance has attracted scientists as it is noninvasive, nonionizing and the instrumentation is relatively simple. Focused Impedance Method (FIM) is attractive in this context, as it has enhanced sensitivity at the central region directly beneath the electrode configuration minimizing contribution from neighboring regions. FIM essentially adds or averages two concentric and orthogonal combinations of conventional Tetrapolar Impedance Measurements (TPIM) and has three versions with 4, 6, and 8 electrodes. This paper describes the design and testing of a multi-frequency FIM (MFFIM) system capable of measuring all three versions of FIM at 8 frequencies in the range 10 kHz—1 MHz. A microcontroller based multi-frequency signal generator and a balanced Howland current source with high output impedance (476 kΩ at 10 kHz and 58.3 kΩ at 1 MHz) were implemented for driving currents into biological tissues with an error &lt;1%. The measurements were carried out at each frequency sequentially. The peak values of the amplified voltage signals were measured using a novel analogue synchronous peak detection technique from which the transfer impedances were obtained. The developed system was tested using TPIM measurements on a passive RC Cole network placed between two RC networks, the latter representing skin-electrode contact impedances. Overall accuracy of the measurement was very good (error &lt;4% at all frequencies except 1 MHz, with error 6%) and the resolution was 0.1 Ω. The designed MFFIM system had a sampling rate of &gt;45 frames per second which was deemed adequate for noninvasive real-time impedance measurements on biological tissues.

Multi-channel Trans-impedance Leadforming for Cardiopulmonary Monitoring: Algorithm Development and Feasibility Assessment using In Vivo Animal Data

OBJECTIVE

The objectives of this study were to (1) develop a multi-channel trans-impedance leadforming method for beat-to-beat stroke volume (SV) and breath-by-breath tidal volume (TV) measurements and (2) assess its feasibility on an existing in vivo animal dataset.

METHODS

A deterministic leadforming algorithm was developed to extract a cardiac volume signal (CVS) and a respiratory volume signal (RVS) from 208-channel trans-impedance data acquired every 20 ms by an electrical impedance tomography (EIT) device. SV_EIT and TV_EIT values were computed as a valley-to-peak value in the CVS and RVS, respectively. The method was applied to the existing dataset from five mechanically-ventilated pigs undergoing ten mini-fluid challenges. An invasive hemodynamic monitor was used in the arterial pressure-based cardiac output (APCO) mode to simultaneously measure SV_APCO values while a mechanical ventilator provided TV_Vent values.

RESULTS

The leadforming method could reliably extract the CVS and RVS from the 208-channel trans-impedance data measured with the EIT device, from which SV_EIT and TV_EIT were computed. The SV_EIT and TV_EIT values were comparable to those from the invasive hemodynamic monitor and mechanical ventilator. Using the data from 5 pigs and a simple calibration method to remove bias, the error in SV<sub>EIT<sub> and TV<sub>EIT<sub> was 9.5% and 5.4%, respectively.

CONCLUSION

We developed a new leadforming method for the EIT device to robustly extract both SV and TV values in a deterministic fashion. Future animal and clinical studies are needed to validate this leadforming method in various subject populations.

SIGNIFICANCE

The leadforming method could be an integral component for a new cardiopulmonary monitor in the future to simultaneously measure SV and TV noninvasively, which would be beneficial to patients.

Impact of Local Electrodes on Brain Stroke Type Differentiation using Electrical Impedance Tomography

Electrical impedance tomography (EIT) of the head has the potential to provide rapid characterization of brain stroke. This study builds on previous work by implementing a more anatomically complex head model, contrasting results of bleed and clot simulations, and by establishing the electrodes which dominate in voltage difference measurements. This work provides the basis for machine learning with clusters of small numbers of electrodes as unique features for stroke-type detection and differentiation.Clinical Relevance- This application of EIT can aid in early detection, classification, and localization of brain stroke, allowing for faster treatment.

Noninvasive Beat-To-Beat Stroke Volume Measurements to Determine Preload Responsiveness During Mini-Fluid Challenge in a Swine Model: A Preliminary Study

ABSTRACT

Cardiac output (CO) is an important parameter in fluid management decisions for treating hemodynamically unstable patients in intensive care unit. The gold standard for CO measurements is the thermodilution method, which is an invasive procedure with intermittent results. Recently, electrical impedance tomography (EIT) has emerged as a new method for noninvasive measurements of stroke volume (SV). The objectives of this paper are to compare EIT with an invasive pulse contour analysis (PCA) method in measuring SV during mini-fluid challenge in animals and determine preload responsiveness with EIT. Five pigs were anesthetized and mechanically ventilated. After removing 25% to 30% of the total blood from each animal, multiple fluid injections were conducted. The EIT device successfully tracked changes in SV beat-to-beat during varying volume states, i.e., from hypovolemia and preload responsiveness to target volume and volume overload. From a total of 50 100-mL fluid injections on five pigs (10 injections per pig), the preload responsiveness value was as large as 32.3% in the preload responsiveness state while in the volume overload state it was as low as -4.9%. The bias of the measured SV data using EIT and PCA was 0 mL, and the limits of agreement were ±3.6 mL in the range of 17.6 mL to 51.0 mL. The results of the animal experiments suggested that EIT is capable of measuring beat-to-beat SV changes during mini-fluid challenge and determine preload responsiveness. Further animal and clinical studies will be needed to demonstrate the feasibility of the EIT method as a new tool for fluid management.

Dry Wearable Textile Electrodes for Portable Electrical Impedance Tomography

Electrical impedance tomography (EIT), a noninvasive and radiation-free medical imaging technique, has been used for continuous real-time regional lung aeration. However, adhesive electrodes could cause discomfort and increase the risk of skin injury during prolonged measurement. Additionally, the conductive gel between the electrodes and skin could evaporate in long-term usage and deteriorate the signal quality. To address these issues, in this work, textile electrodes integrated with a clothing belt are proposed to achieve EIT lung imaging along with a custom portable EIT system. The simulation and experimental results have verified the validity of the proposed portable EIT system. Furthermore, the imaging results of using the proposed textile electrodes were compared with commercial electrocardiogram electrodes to evaluate their performance.

Robust parameter estimation based on the generalized log-likelihood in the context of Sharma-Taneja-Mittal measure

The problem of obtaining physical parameters that cannot be directly measured from observed data arises in several scientific fields. In the classic approach, the well-known maximum likelihood estimation associated with a Gaussian distribution is employed to obtain the model parameters of a complex system. Although this approach is quite popular in statistical physics, only a handful of spurious observations (outliers) make this approach ineffective, violating the Gauss-Markov theorem. In this work, starting from the generalized logarithmic function associated to the Sharma-Taneja-Mittal (STM) information measure, we propose an outlier-resistant approach based on the generalized log-likelihood estimation. In particular, our proposal deforms the Gaussian distribution based on a two-parameter generalization of the ordinary logarithmic function. We have tested the effectiveness of our proposal considering a classic geophysical inverse problem with a very noisy data set. The results show that the task of obtaining physical parameters based on the STM measure from noisy data with several outliers outperforms the classic approach, and therefore, our proposal is a useful tool for statistical physics, information theory, and statistical inference problems.