Electrical Impedance Tomography Research Articles

Sensitivity volume as figure-of-merit for maximizing data importance in electrical impedance tomography.

Objective.Electrical impedance tomography (EIT) is a noninvasive imaging method whereby electrical measurements on the periphery of a heterogeneous conductor are inverted to map its internal conductivity. The EIT method proposed here aims to improve computational speed and noise tolerance by introducing sensitivity volume as a figure-of-merit for comparing EIT measurement protocols.Approach.Each measurement is shown to correspond to a sensitivity vector in model space, such that the set of measurements, in turn, corresponds to a set of vectors that subtend a sensitivity volume in model space. A maximal sensitivity volume identifies the measurement protocol with the greatest sensitivity and greatest mutual orthogonality. A distinguishability criterion is generalized to quantify the increased noise tolerance of high sensitivity measurements.Main result.The sensitivity volume method allows the model space dimension to be minimized to match that of the data space, and the data importance to be increased within an expanded space of measurements defined by an increased number of contacts.Significance.The reduction in model space dimension is shown to increasecomputational efficiency, accelerating tomographic inversion by several orders of magnitude, while the enhanced sensitivitytolerates higher noiselevels up to several orders of magnitude larger than standard methods.

Research on Human Lung Impedance Tomography Based on Soft Thresholding Image Segmentation and Reduced-Order Tikhonov Regularization

The lung is one of the most vital organs in the human body, and its condition is closely correlated with overall health. Electrical impedance tomography (EIT), as a biomedical imaging technique, often produces low-quality reconstructed images due to its inherent ill-posedness in solving the inverse problem. To address this issue, this paper proposes a soft-threshold region segmentation algorithm with a relaxation factor. This algorithm segments the reconstructed lung images into internal regions, edge regions, and background regions, resulting in clearer boundaries in the reconstructed images. This facilitates the intuitive identification of regions of interest by healthcare professionals. Additionally, this segmentation algorithm is suitably combined with a dimension-reduced Tikhonov regularization algorithm. By utilizing the joint capabilities of these algorithms, the partition points belonging to the background region can be excluded from the sought grayscale vector, thereby improving the ill-posedness of the image reconstruction process and enhancing the quality of image reconstruction. Finally, a 16-electrode human lung EIT simulation model is established for the thoracic region and verified through simulation. Experimental validation is conducted using a human lung tank simulation platform to further demonstrate the effectiveness of the proposed method.

Is EIT-guided positive end-expiratory pressure titration for optimizing PEEP in ARDS the white elephant in the room? A systematic review with meta-analysis and trial sequential analysis.

Electrical Impedance Tomography (EIT) is a novel real-time lung imaging technology for personalized ventilation adjustments, indicating promising results in animals and humans. The present study aimed to assess its clinical utility for improved ventilation and oxygenation compared to traditional protocols. Comprehensive electronic database screening was done until 30th November, 2023. Randomized controlled trials,controlled clinical trials, comparative cohort studies, and assessments of EIT-guided PEEP titration and conventional methods in adult ARDS patients regarding outcome, ventilatory parameters, and P/F ratio were included. Our search retrieved five controlled cohort studies and two RCTs with 515 patients and overall reduced risk of mortality [RR = 0.68; 95% CI: 0.49 to 0.95; I2 = 0%], better dynamic compliance [MD = 3.46; 95% CI: 1.59 to 5.34; I2 = 0%] with no significant difference in PaO2/FiO2 ratio [MD = 6.5; 95%CI -13.86 to 26.76; I2 = 74%]. The required information size except PaO2/FiO2 was achieved for a power of 95% based on the 50% reduction in risk of mortality, 10% improved compliance as the cumulative Z-score of the said outcomes crossed the alpha spending boundary and did not dip below the inner wedge of futility. EIT-guided individualized PEEP titration is a novel modality; further well-designed studies are needed to substantiate its utility.

Oxygenation and ventilation during prolonged experimental cardiopulmonary resuscitation with either continuous or 30:2 compression-to-ventilation ratios together with 10 cmH20 positive end-expiratory pressure

BackgroundIn refractory out-of-hospital cardiac arrest, the patient is commonly transported to hospital with mechanical continuous chest compressions (CCC). Limited data are available on the optimal ventilation strategy. Accordingly, we compared arterial oxygenation and haemodynamics during manual asynchronous continuous ventilation and compressions with a 30:2 compression-to-ventilation ratio together with the use of 10 cmH2O positive end-expiratory pressure (PEEP).MethodsIntubated and anaesthetized landrace pigs with electrically induced ventricular fibrillation were left untreated for 5 min (n = 31, weight ca. 55 kg), after which they were randomized to either the CCC group or the 30:2 group with the the LUCAS® 2 piston device and bag-valve ventilation with 100% oxygen targeting a tidal volume of 8 ml/kg with a PEEP of 10 cmH2O for 35 min. Arterial blood samples were analysed every 5 min, vital signs, near-infrared spectroscopy and electrical impedance tomography (EIT) were measured continuously, and post-mortem CT scans of the lungs were obtained.ResultsThe arterial blood values (median + interquartile range) at the 30-min time point were as follows: PaO2: 180 (86–302) mmHg for the 30:2 group; 70 (49–358) mmHg for the CCC group; PaCO2: 41 (29–53) mmHg for the 30:2 group; 44 (21–67) mmHg for the CCC group; and lactate: 12.8 (10.4–15.5) mmol/l for the 30:2 group; 14.7 (11.8–16.1) mmol/l for the CCC group. The differences were not statistically significant. In linear mixed models, there were no significant differences between the groups. The mean arterial pressures from the femoral artery, end-tidal CO2, distributions of ventilation from EIT and mean aeration of lung tissue in post-mortem CTs were similar between the groups. Eight pneumothoraces occurred in the CCC group and 2 in the 30:2 group, a statistically significant difference (p = 0.04).ConclusionsThe 30:2 and CCC protocols with a PEEP of 10 cmH2O resulted in similar gas exchange and vital sign outcomes in an experimental model of prolonged cardiac arrest with mechanical compressions, but the CCC protocol resulted in more post-mortem pneumothoraces.

Simulation of a synchronized methodology for MR-based electromechanical property imaging during transcranial electrical stimulation

Introduction: Recent investigations into the biomechanics of the brain have unveiled alteration in tissue stiffness triggered by external stimuli. For instance, visual stimulation effects can be measured in elasticity images of the cortex generated by functional magnetic resonance elastography (MRE). Such a mechanical characterization method combined with non-invasive brain stimulation (NIBS), a technique that seeks to selectively modulate particular parts of the brain using weak electrical currents, has the potential to influence research on various neurological disorders. In this in silico study, we aimed to elucidate individual and interdependent aspects related to a synchronized biomechanical imaging and non-invasive brain stimulation methodology. Magnetic resonance electrical impedance tomography (MREIT) was incorporated to the pipeline, providing a promising way of evaluating NIBS-induced electrical current patterns in the brain while leveraging MRE and transcranial alternating current stimulation (tACS) experimental settings.Methods: A mouse head model was assembled using open-access atlases to include five anatomical structures: skin/subcutaneous tissue, skull, cerebrospinal fluid (CSF), brain white and grey matters. MRE, tACS, and MREIT experiments were simulated using Comsol Multiphysics with Matlab Livelink. Synthetic MRE and MREIT data were processed using the subzone non-linear inversion and harmonic Bz algorithm, respectively, to reconstruct images of the distributed complex shear modulus and electrical conductivity.Results and Discussion: Lorentz body forces arising from simultaneous MRE and tACS elicited elastic waves of negligible amplitude compared with the extrinsic actuation levels reported in the literature, which allowed accurate reconstructions of the complex shear modulus. Qualitative electrical conductivity maps retrieved by MREIT accurately delineated anatomical regions of the brain model and could be used to recover reasonably accurate distributions of tACS-induced currents. This multi-physics approach has potential for translation to human brain imaging, and may provide more possibilities for the characterization of brain function together than in isolation.

A non-invasive approach to skin cancer diagnosis via graphene electrical tattoos and electrical impedance tomography.

Making up one of the largest shares of diagnosed cancers worldwide, skin cancer is also one of the most treatable. However, this is contingent upon early diagnosis and correct skin cancer-type differentiation. Currently, methods for early detection that are accurate, rapid, and non-invasive are limited. However, literature demonstrating the impedance differences between benign and malignant skin cancers, as well as between different types of skin cancer, show that methods based on impedance differentiation may be promising. In this work, we propose a novel approach to rapid and non-invasive skin cancer diagnosis that leverages the technologies of difference-based electrical impedance tomography (EIT) and graphene electronic tattoos (GETs). We demonstrate the feasibility of this first-of-its-kind system using both computational numerical and experimental skin phantom models. We considered variations in skin cancer lesion impedance, size, shape, and position relative to the electrodes and evaluated the impact of using individual and multi-electrode GET (mGET) arrays. The results demonstrate that this approach has the potential to differentiate based on lesion impedance, size, and position, but additional techniques are needed to determine shape. In this way, the system proposed in this work, which combines both EIT and GET technology, exhibits potential as an entirely non-invasive and rapid approach to skin cancer diagnosis.

Respiratory rate monitoring in ICU patients and healthy volunteers using electrical impedance tomography: a validation study.

The respiratory rate (RR) is considered one of the most informative vital signals. A well-validated standard for RR measurement in mechanically ventilated patient is capnography; a noninvasive technique for expiratory CO2 measurements. Reliable RR measurements in spontaneously breathing patients remains a challenge as continuous mainstream capnography measurements are not available. This study aimed to assess the accuracy of RR measurement using electrical impedance tomography (EIT) in healthy volunteers and intensive care unit (ICU) patients on mechanical ventilation and spontaneously breathing post-extubation. Comparator methods included RR derived from both capnography and bioimpedance electrocardiogram (ECG) measurements. 
Approach: Twenty healthy volunteers wore an EIT belt and ECG electrodes while breathing through a capnometer within a 10 - 40 breaths per minute (BPM) range. Nineteen ICU patients underwent similar measurements during pressure support ventilation and spontaneously breathing after extubation from mechanical ventilation. Stable periods with regular breathing and no artefacts were selected, and agreement between measurement methods was assessed using Bland-Altman analysis for repeated measurements. 
Main Result: Bland-Altman analysis revealed a bias less than 0.2 BPM, with tight limits of agreement (LOA) 1.5 BPM in healthy volunteers and ventilated ICU patients when comparing EIT to capnography. Spontaneously breathing ICU patients had wider LOA (2.5 BPM) when comparing EIT to ECG bioimpedance, but gold standard comparison was unavailable. RR measurements were stable for 91% of the time for capnography, 68% for EIT, and 64% of the ECG bioimpedance signals. After extubation, the percentage of stable periods decreased to 48% for EIT signals and to 55% for ECG bioimpedance. 
Significance: In periods of stable breathing, EIT demonstrated excellent RR measurement accuracy in healthy volunteers and ICU patients. However, stability of both EIT and ECG bioimpedance RR measurements declined in spontaneously breathing patients to approximately 50% of the time. &#xD.

A Blockchain-Based Secure Sharing Scheme for Electrical Impedance Tomography Data

Real-time electrical impedance tomography (EIT) data sharing is becoming increasingly necessary, due to the extensive use of EIT technology in various sectors, including material analysis, biomedicine, and industrial process monitoring. The prevalence of portable EIT equipment and remote imaging technology has led to a predominance of centralized storage, Internet protocol transmission, and certificates from certificate authorities (CA) in telemedicine data. This has resulted in compromised data security, network communication delays, high CA maintenance costs, increased risks of medical data privacy breaches, and low security. Therefore, this paper offers a consortia blockchain-based method for exchanging EIT data that addresses security and integrity concerns during data storage and exchange, while maintaining transparency and traceability. Proprietary re-encryption techniques are employed to guarantee traceability when exchanging anonymous data, enabling precise control over data access. This scheme serves to protect both data and identity privacy, as well as to trace the actual identity of potential malicious users, while also thwarting any coordinated efforts between partially trusted parties and data requesters seeking unauthorized access to confidential information. Additionally, a combination of blockchain and InterPlanetary File System (IPFS) distributed storage technology is utilized to ease the burden of EIT data storage. The feasibility and effectiveness of the proposed solution were validated through a series of experiments, demonstrating its ability to effectively prevent data tampering and misuse, reduce data management costs, and enhance the efficiency and quality of data sharing.

Assessing pulmonary function in ALS using electrical impedance tomography

Objective: We sought to determine whether thoracic electrical impedance tomography (EIT) could characterize pulmonary function in amyotrophic lateral sclerosis (ALS) patients, including those with facial weakness. Thoracic EIT is a noninvasive, technology in which a multi-electrode belt is placed across the chest, producing real-time impedance imaging of the chest during breathing. Methods: We enrolled 32 ALS patients and 32 age- and sex-matched healthy controls (HCs) without underlying lung disease. All participants had EIT measurements performed simultaneously with standard pulmonary function tests (PFTs), including slow and forced vital capacity (SVC and FVC) in upright and supine positions and maximal inspiratory and expiratory pressures (MIPs and MEPs, respectively). Intraclass correlation coefficients (ICCs) were calculated to assess the immediate reproducibility of EIT measurements and Pearson’s correlations were used to explore the relationships between EIT and PFT values. Results: Data from 30 ALS patients and 27 HCs were analyzed. Immediate upright SVC reproducibility was very high (ICC 0.98). Correlations were generally strongest between EIT and spirometry measures, with R values ranging from 0.64 to 0.82 (p < 0.001) in the ALS cohort. There were less robust correlations between EIT values and both MIPs and MEPs in the ALS patients, with R values ranging from 0.33 to 0.44. There was no significant difference for patients with and without facial weakness. There were no reported adverse events. Conclusion: EIT-based pulmonary measures hold the promise of providing an alternative approach for lung function assessment in ALS patients. Based on these early results, further development and study of this technology are warranted.

TSS-ConvNet for electrical impedance tomography image reconstruction.

In this paper, we present a novel data-driven approach for solving ill-posed inverse problems, such as Electrical Impedance Tomography (EIT). Our approach introduces a new layer architecture composed of three paths: spatial, spectral, and truncated spectral paths. The spatial path processes information locally, while the spectral and truncated spectral paths provide the network with a global receptive field. Such architecture helps eliminate the ill-posedness and nonlinearity of the inverse problem. The three paths are interconnected, allowing for information exchange on different receptive fields with different learning abilities. The network has a bottleneck architecture which enables it to recover signal information from noisy redundant measurements. We call our proposed model Truncated Spatial-Spectral Convolutional neural Network (TSSConvNet). The model overcomes the receptive field limitation of the most existing models which use only the local information in Euclidean space. We trained the network on a large dataset that covers various configurations with random parameters to ensure generalization over the training samples. Our model achieves superior accuracy with relatively high resolution on both simulation and experimental data.

Deep unrolling networks with recurrent momentum acceleration for nonlinear inverse problems

Combining the strengths of model-based iterative algorithms and data-driven deep learning solutions, deep unrolling networks (DuNets) have become a popular tool to solve inverse imaging problems. Although DuNets have been successfully applied to many linear inverse problems, their performance tends to be impaired by nonlinear problems. Inspired by momentum acceleration techniques that are often used in optimization algorithms, we propose a recurrent momentum acceleration (RMA) framework that uses a long short-term memory recurrent neural network (LSTM-RNN) to simulate the momentum acceleration process. The RMA module leverages the ability of the LSTM-RNN to learn and retain knowledge from the previous gradients. We apply RMA to two popular DuNets—the learned proximal gradient descent (LPGD) and the learned primal-dual (LPD) methods, resulting in LPGD-RMA and LPD-RMA, respectively. We provide experimental results on two nonlinear inverse problems: a nonlinear deconvolution problem, and an electrical impedance tomography problem with limited boundary measurements. In the first experiment we have observed that the improvement due to RMA largely increases with respect to the nonlinearity of the problem. The results of the second example further demonstrate that the RMA schemes can significantly improve the performance of DuNets in strongly ill-posed problems.

Imaging the pulmonary vasculature in acute respiratory distress syndrome

Acute respiratory distress syndrome (ARDS) is characterized by a redistribution of regional lung perfusion that impairs gas exchange. While speculative, experimental evidence suggests that perfusion redistribution may contribute to regional inflammation and modify disease progression. Unfortunately, tools to visualize and quantify lung perfusion in patients with ARDS are lacking. This review explores recent advances in perfusion imaging techniques that aim to understand the pulmonary circulation in ARDS. Dynamic contrast-enhanced computed tomography captures first-pass kinetics of intravenously injected dye during continuous scan acquisitions. Different contrast characteristics and kinetic modeling have improved its topographic measurement of pulmonary perfusion with high spatial and temporal resolution. Dual-energy computed tomography can map the pulmonary blood volume of the whole lung with limited radiation exposure, enabling its application in clinical research. Electrical impedance tomography can obtain serial topographic assessments of perfusion at the bedside in response to treatments such as inhaled nitric oxide and prone position. Ongoing technological improvements and emerging techniques will enhance lung perfusion imaging and aid its incorporation into the care of patients with ARDS.

Serial electrical impedance tomography course in different treatment groups; The MaastrICCht cohort

PurposeTo describe the effect of dexamethasone and tocilizumab on regional lung mechanics over admission in all mechanically ventilated COVID-19 patients. Materials and methodsDynamic compliance, alveolar overdistension and collapse were serially determined using electric impedance tomography (EIT). Patients were categorized into three groups; no anti-inflammatory therapy, dexamethasone therapy, dexamethasone + tocilizumab therapy. The EIT variables were (I) visualized using polynomial regression, (II) evaluated throughout admission using linear mixed-effects models, and (III) average respiratory variables were compared. ResultsVisual inspection of EIT variables showed a pattern of decreasing dynamic compliance. Overall, optimal set PEEP was lower in the dexamethasone group (−1.4 cmH2O, −2.6; −0.2). Clinically applied PEEP was lower in the dexamethasone and dexamethasone + tocilizumab group (−1.5 cmH2O, −2.6; −0.2; −2.2 cmH2O, −5.1; 0.6). Dynamic compliance, alveolar overdistension, and alveolar collapse at optimal set PEEP did not significantly differ between the three groups. ConclusionOptimal and clinically applied PEEP were lower in the dexamethasone and dexamethasone + tocilizumab groups. The results suggest that the potential beneficial effects of these therapies do not affect lung mechanics favorably. However, this study cannot fully rule out any beneficial effect of anti-inflammatory treatment on pulmonary function due to its observational nature.

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.

Electrical Impedance Tomography for Real-Time Monitoring of Muscle Activity in Robot-Assisted Rehabilitation

This work looks at how Electrical Impedance Tomography (EIT) can be used in robot-assisted rehabilitation to make it easier to watch how muscle activity changes in real time. Quantitative methods are used in this study to look into how well EIT works, including full analyses and comparisons with common tracking methods like Electromyography (EMG). Both EIT and EMG are used to record the participants' mean and peak muscle activation levels as they do a set of Upper Limb, Lower Limb, and Full Body exercises. The study's findings show that EIT is incredibly flexible when it comes to capturing complex patterns of muscle activation across a wide range of rehabilitation routines. Each participant's involvement is shown by different patterns of muscle activation, and these patterns can be seen across different types of exercise. We can learn a lot about how muscles work during rehabilitation by measuring their mean and peak activation levels. This helps us see how well EIT works as a tracking tool. A very important step in validating EIT is to compare it to EMG readings. This shows how consistent and accurate EIT is in real-time monitoring situations. The high level of agreement between the two methods shows that EIT is a reliable alternative to standard monitoring methods, providing an easy to use and non-invasive way to measure muscle activity. The results of this study are even stronger because they include quantitative analyses and a detailed look at system performance data. We carefully check things like signal-to-noise ratio, reconstruction accuracy, and reliability, which shows how strong and dependable the combined EIT system is. These metrics are important for showing how well the system can measure things accurately and reliably in changing rehabilitation situations. This study adds to the growing amount of research on EIT uses in rehabilitation by showing that it can be a useful tool for keeping track of real-time muscle activity. This study opens the door for EIT to be widely used in rehabilitation by showing how muscle activation changes over time and proving its effectiveness against well-known methods. This will eventually lead to better patient outcomes and more effective rehabilitation.

Efficient Jacobian Computations for Complex ECT/EIT Imaging

The reconstruction of the spatial complex conductivity σ+jωε0εr from complex valued impedance measurements forms the inverse problem of complex electrical impedance tomography or complex electrical capacitance tomography. Regularized Gauß-Newton schemes have been proposed for their solution. However, the necessary computation of the Jacobian is known to be computationally expensive, as standard techniques such as adjoint field methods require additional simulations. In this work, we show a more efficient way to computationally access the Jacobian matrix. In particular, the presented techniques do not require additional simulations, making the use of the Jacobian, free of additional computational costs.

Awake pronation with helmet CPAP in early COVID-19 ARDS patients: effects on respiratory effort and distribution of ventilation assessed by EIT.

Prone positioning with continuous positive airway pressure (CPAP) is widely used for respiratory support in awake patients with COVID-19-associated acute respiratory failure. We aimed to assess the respiratory mechanics and distribution of ventilation in COVID-19-associated ARDS treated by CPAP in awake prone position. We studied 16 awake COVID-19 patients with moderate-to-severe ARDS. The study protocol consisted of a randomized sequence of supine and prone position with imposed positive end-expiratory pressure (PEEP) of 5 and 10 cmH2O delivered by helmet CPAP. Respiratory mechanics and distribution of ventilation were assessed through esophageal pressure (PES) and electrical impedance tomography (EIT). At the end of each 20-min phase, arterial blood gas analysis was performed, and PES swing and EIT tracings were recorded for the calculation of the respiratory mechanics and regional ventilation. The patient's position had no significant effects on respiratory mechanics. EIT analysis did not detect differences among global indices of ventilation. A significant proportion of pixels in the sternal region of interest showed an increase in compliance from the supine to prone position and PaO2/FIO2 increased accordingly. The best improvement of both PaO2/FIO2 and sternal compliance was obtained in the prone position with PEEP 10 cmH2O. In the studied subjects, prone positioning during CPAP treatment raised oxygenation without improvement of "protective" ventilation or global ventilatory inhomogeneity indices. Prone positioning with higher PEEP significantly increased the compliance of sternal regions.

Physiologic Effects of ECMO in Patients with Severe Acute Respiratory Distress Syndrome.

Blood flow rate affects mixed venous oxygenation (SvO2) during venovenous extracorporeal membrane oxygenation (ECMO), with possible effects on the pulmonary circulation and the right heart function. We aimed at describing the physiologic effects of different levels of SvO2 obtained by changing ECMO blood flow, in patients with severe ARDS receiving ECMO and controlled mechanical ventilation. Low (SvO2 target 70-75%), intermediate (SvO2 target 75-80%) and high (SvO2 target > 80%) ECMO blood flows were applied for 30 minutes in random order in 20 patients. Mechanical ventilation settings were left unchanged. The hemodynamic and pulmonary effects were assessed with pulmonary artery catheter and electrical impedance tomography (EIT). Cardiac output decreased from low to intermediate and to high blood flow/SvO2 (9.2 [6.2-10.9] vs 8.3 [5.9-9.8] vs 7.9 [6.5-9.1] L/min, p = 0.014), as well as mean pulmonary artery pressure (34 ± 6 vs 31 ± 6 vs 30 ± 5 mmHg, p < 0.001), and right ventricle stroke work index (14.2 ± 4.4 vs 12.2 ± 3.6 vs 11.4 ± 3.2 g*m/beat/m2, p = 0.002). Cardiac output was inversely correlated with mixed venous and arterial PO2 values (R2 = 0.257, p = 0.031 and R2 = 0.324, p = 0.05). Pulmonary artery pressure was correlated with decreasing mixed venous PO2 (R2 = 0.29, p <0.001) and with increasing cardiac output (R2 = 0.378 p < 0.007). Measures of ventilation/perfusion mismatch did not differ between the three steps. In severe ARDS patients, increased ECMO blood flow rate resulting in higher SvO2 decreases pulmonary artery pressure, cardiac output, and right heart workload.

Generative-Adversarial-Network-Based Image Reconstruction for the Capacitively Coupled Electrical Impedance Tomography of Stroke.

This study investigated the potential of machine-learning-based stroke image reconstruction in capacitively coupled electrical impedance tomography. The quality of brain images reconstructed using the adversarial neural network (cGAN) was examined. The big data required for supervised network training were generated using a two-dimensional numerical simulation. The phantom of an axial cross-section of the head without and with impact lesions was an average of a three-centimeter-thick layer corresponding to the height of the sensing electrodes. Stroke was modeled using regions with characteristic electrical parameters for tissues with reduced perfusion. The head phantom included skin, skull bone, white matter, gray matter, and cerebrospinal fluid. The coupling capacitance was taken into account in the 16-electrode capacitive sensor model. A dedicated ECTsim toolkit for Matlab was used to solve the forward problem and simulate measurements. A conditional generative adversarial network (cGAN) was trained using a numerically generated dataset containing samples corresponding to healthy patients and patients affected by either hemorrhagic or ischemic stroke. The validation showed that the quality of images obtained using supervised learning and cGAN was promising. It is possible to visually distinguish when the image corresponds to the patient affected by stroke, and changes caused by hemorrhagic stroke are the most visible. The continuation of work towards image reconstruction for measurements of physical phantoms is justified.

A Wearable Open-Source electrical impedance tomography device

Electrical impedance tomography (EIT) is medical imaging technique in which small electrical signals are used to map the electrical impedance distribution within the body. It is safe and non-invasive, which make it attractive for use in continuous monitoring or outpatient applications, but the high cost of commercial devices is an impediment to its adoption. Over the last 10 years, many research groups have developed their own EIT devices, but few designs for open-source EIT hardware are available. In this work, we present a complete open-source EIT system that is designed to be suitable for monitoring the lungs of free breathing subjects. The device is low-cost, wearable, and is designed to comply with the industry accepted safety standard for EIT. The device has been tested in two regimes: Firstly in terms of measurement uncertainty as a voltage measurement system, and secondly against a set of measures that have been proposed specifically for EIT hardware. The voltage measurement uncertainty of the device was measured to be − 0.7 % ± 0.36 mV. The EIT specific performance was measured in a phantom test designed to be as physiologically representative as practicable, and the device performed similarly to other published devices. This work will contribute to increased accessibility of EIT for study and will contribute to consensus on testing methodology for EIT devices.