Search Results (209)

Electrical impedance tomography (EIT) is a promising imaging tool in critical care. Its capacity to provide noninvasive bedside visualization of regional ventilation and perfusion with high temporal resolution makes it an ideal monitoring modality for patients on ventilation. However, its widespread implementation has been hindered by physical limitations in spatial resolution and a lack of robust evidence linking its use to improved clinical outcomes. In recent years, the commercialization of several bedside devices has led to growing clinical experience, gradually yielding concrete evidence regarding its clinical utility. Furthermore, beyond respiratory monitoring, data are increasingly accumulating in non-pulmonary fields, including perfusion, neuro-critical care and gastroenterology. Therefore, the objective of this review is to synthesize emerging evidence regarding the recent clinical applications of electrical impedance tomography and discuss future perspectives. Full article

Background: The optimal intraoperative positive end-expiratory pressure (PEEP) obtained by titrating to the lowest tolerable fraction of inspired oxygen (FiO₂) has been proposed recently; however, whether its performance in obtaining optimal PEEP is comparable to that from electrical impedance tomography (EIT) titration remains unknown. Methods: Ninety-three adult patients undergoing robotic-assisted laparoscopic prostatectomy under general anesthesia were enrolled in this study. They underwent the determination of optimal PEEP obtained either by titrating to the lowest tolerable FiO₂ (PEEP_O2) or using EIT (PEEP_EIT). The primary endpoint was intraoperative optimal PEEP values. Secondary endpoints included pre-extubation arterial oxygen partial pressure (PaO₂)/FiO₂, intraoperative mean arterial blood pressure (MAP), the incidence of hypoxemia in the postanesthesia care unit (PACU), and postoperative pulmonary complications (PPCs) up to discharge from hospital. Results: Group PEEP_O2 (n = 47) exhibited a higher optimal PEEP compared to Group PEEP_EIT (n = 46) [Median (IQR): 18 (16–18 cmH₂O) vs. 16 (14–16 cmH₂O), p < 0.001]. Pre-extubation PaO₂/FiO₂ was higher in Group PEEP_O2 (510.5 ± 80.0 vs. 471.8 ± 69.0 mmHg, p = 0.015), while lung dynamic compliance (41.1 ± 7.7 vs. 37.3 ± 6.4 mL cmH₂O⁻¹, p = 0.011) and static compliance (36.4 ± 5.8 vs. 33.6 ± 5.5 mL cmH₂O⁻¹, p = 0.017) were also higher in Group PEEP_O2. Additionally, driving pressure (11.0 ± 2.0 vs. 12.1 ± 1.9 cmH₂O, p = 0.006) was lower in Group PEEP_O2. There were no significant differences in intraoperative MAP and the incidences of PACU hypoxemia and PPCs between the two groups. Conclusions: The optimal PEEP obtained by titrating to the lowest tolerable FiO₂ is a clinically acceptable alternative of that obtained using EIT. Therefore, this technique could be a viable alternative to EIT for obtaining optimal PEEP. Full article

Electrical Impedance Tomography (EIT) is a promising non-invasive sensing technique, yet its practical application in resource-constrained environments is often limited by the high computational cost of inverse image reconstruction. To address this challenge, we focus on specific sensing objectives rather than full image recovery. In this study, we propose a lightweight, task-oriented inference framework for object localization in EIT that bypasses the need to solve computationally expensive inverse reconstruction problems. This approach addresses the high computational demands and hardware complexity of conventional iterative methods, which often hinder real-time monitoring in resource-constrained edge computing environments. Training datasets were generated via finite element method (FEM) simulations for Opposite and Adjacent current injection configurations. A feedforward neural network was developed to independently estimate the radial and angular object positions as probability distributions. Our systematic evaluation revealed that the localization performance depends on the injection configuration and model depth; notably, the Opposite method achieved perfect classification accuracy (1.00) for radial estimation with an optimized architecture of four hidden layers, whereas the Adjacent method exhibited higher ambiguity. Results quantitatively evaluated using the Wasserstein distance show that the Opposite configuration produces more localized, unimodal probability distributions than the Adjacent configuration by utilizing current fields that traverse the entire domain. Compared with existing image-based reconstruction methods, including the conventional electrical impedance tomography and diffuse optical tomography reconstruction software (EIDORS ver.3.12), the proposed framework reduced energy consumption from 3.09 to 0.96 Wh, demonstrating an approximately 70% improvement in energy efficiency while maintaining a high localization accuracy without the need for iterative Jacobian updates. This task-oriented framework enables reliable, high-speed, and energy-efficient localization, making it well-suited for low-power EIT applications in mobile and embedded sensor systems. Full article

Gesture recognition with electrical impedance tomography (EIT) is an enormous potential tool for human–machine interaction because of its low cost, low complexity and high temporal resolution. Although high-precision EIT-based gesture recognition has been achieved in ideal scenarios, ensuring its consistent performance under interference remains challenging. This article presents a novel method to alleviate the effect of confounding factors on EIT gesture recognition. An EIT armband was designed to mitigate the effect of contact impedance variation based on equivalent circuit analysis, and a spatial–temporal fusion network, named the Fold Atrous Spatial Pyramid Pooling-Gated Recurrent Unit (FASPP-GRU), was developed for robust gesture classification. The results showed that the proposed two-layer electrode maintained a stable contact impedance when its contact force with the skin was changed. Although confounding factors caused significant changes in baseline forearm impedance, FASPP-GRU achieved 80% accuracy under the effect of limb position changes and dynamic changes in muscle state over time, which outperforms conventional classifiers. With an 87 μs inference time, the proposed system shows enormous potential in real-time applications. Full article

Electrical Impedance Tomography (EIT) represents a promising and non-invasive technique for the characterisation of biological tissues, but its diagnostic performance strongly depends on the electrode configuration, system geometry, and electronic acquisition strategies. In this work, a three-dimensional model based on the Finite Element Method (FEM) is developed to investigate the detectability of epithelial neoplasms through optimised electrode excitation schemes. The adjacent and opposite configurations are systematically compared in terms of impedance contrast, spatial sensitivity, and neoplastic inclusion localisation capability. The simulations were implemented using an open-source finite element solver with heterogeneous multilayer tissue models. The results show that the configuration with opposite electrodes significantly improves impedance contrast and sensitivity in three-dimensional models, allowing for better detection of localised conductivity anomalies. The proposed approach contributes to the design of optimised EIT electronic systems for early and non-invasive screening applications of epithelial cancer. Full article

Electrical impedance tomography (EIT) provides noninvasive, high-temporal-resolution imaging for medical and industrial applications. However, accurate image reconstruction remains challenging due to the severe ill-posedness and nonlinearity of the inverse problem, as well as the limited robustness of existing single-source learning-based methods in real measurement scenarios. To address these limitations, a data-constrained and physics-guided Multi-Source Conditional Diffusion Model (MS-CDM) is proposed for EIT image reconstruction. Unlike conventional conditional diffusion methods that rely on a single measurement or an image prior, MS-CDM utilizes boundary voltage measurements as data-driven constraints and incorporates coarse reconstructions as physics-guided structural priors. This multi-source conditioning strategy provides complementary guidance during the reverse diffusion process, enabling balanced recovery of fine boundary details and global topological consistency. To support this framework, a Hybrid Swin–Mamba Denoising U-Net is developed, combining hierarchical window-based self-attention for local spatial modeling with bidirectional state-space modeling for efficient global dependency capture. Extensive experiments on simulated datasets and three real EIT experimental platforms demonstrate that MS-CDM consistently outperforms state-of-the-art numerical, supervised, and diffusion-based methods in terms of reconstruction accuracy, structural consistency, and noise robustness. Moreover, the proposed model exhibits robust cross-system applicability without system-specific retraining under multi-protocol training, highlighting its practical applicability in diverse real-world EIT scenarios. Full article

Acute respiratory distress syndrome (ARDS) has traditionally been managed with population-based, protocolized mechanical ventilation strategies designed to limit ventilator-induced lung injury. While these approaches have improved outcomes, they fail to account for the pronounced biological, mechanical, radiological, and temporal heterogeneity that characterizes ARDS. Accumulating evidence shows that patients differ markedly in functional lung size, recruitability, chest wall mechanics, inflammatory burden, and tolerance to ventilatory stress, making uniform ventilatory targets physiologically imprecise and, at times, harmful. This narrative review examines the evolution from conventional lung-protective ventilation toward a precision-based paradigm that aligns ventilatory support with individual patient physiology. We conceptualize ARDS not as a static syndrome but as a dynamic spectrum, viewing the injured lung as a heterogeneous mechanical system susceptible to regionally amplified stress and strain. Within this framework, we discuss key principles underlying precision ventilation, including functional lung size (the “baby lung”), driving pressure, mechanical power, patient–ventilator interaction, spontaneous breathing-associated injury, and the time-dependent evolution of lung mechanics. We synthesize current evidence supporting mechanical, biological, and radiological subphenotyping as complementary strategies to individualize ventilatory management, while critically appraising their current limitations. This review also evaluates bedside tools that may operationalize precision ventilation in clinical practice, including esophageal pressure monitoring, lung ultrasound, and electrical impedance tomography, and examines the role of artificial intelligence as a clinician-directed decision-support aid rather than a prescriptive substitute for physiological reasoning. Implications for clinical trial design, ethical considerations, and future directions toward predictive and adaptive ventilation strategies are also addressed. Precision mechanical ventilation represents a shift from rigid thresholds toward proportional, physiology-guided intervention across the disease trajectory. By integrating evolving lung mechanics, ventilatory load, and patient effort over time, this approach provides a coherent framework for safer and more effective mechanical ventilation in ARDS while preserving the core principles of lung protection. Full article

Electrical Impedance Tomography (EIT) is a non-invasive imaging technique that enables real-time monitoring of human organ function by reconstructing the internal conductivity distribution. In clinical settings, electrode placement is often constrained, resulting in incomplete boundary measurements and a consequent severe degradation of image reconstruction quality. In this work, we investigate EIT reconstruction under partially covered electrode boundaries using our recently proposed nonlinear weighted anisotropic total variation (NWATV) regularizer. Through numerical simulations and water tank experiments with three-quarter and half-circumferential electrode configurations, we demonstrate that NWATV significantly improves both structural fidelity and robustness in reconstructed images under partial electrode coverage. Our findings indicate that NWATV holds potential for clinical use in limited-electrode configurations. Full article

The influence of different voltage measurement and current injection configurations on the quality of image reconstruction in electrical impedance tomography (EIT) was investigated using numerical simulations. Adjacent and opposing techniques were systematically used to examine their effectiveness in voltage acquisition and current delivery. The simulation model employed 16 equally spaced electrodes arranged around a circular domain, with an injected alternating current of 1 mA at a frequency of 50 kHz. A circular object with a conductivity of 0.9 units was sequentially positioned at five distinct locations within the imaging domain, each spaced 0.05 units apart. The reconstructed images were analyzed for positional accuracy and contrast resolution. While each configuration offers specific advantages, they exhibit inherent limitations depending on the application. The results of this study enable the understanding of the trade-offs involved in selecting electrode drive and measurement strategies for optimizing image quality in EIT systems. Full article

We explore the differences between real and absolute values of signals in Electrical Impedance Tomography image reconstruction, with a focus on their impact on image quality and accuracy. Simulations were conducted using a finite element mesh model containing three inclusions with varying conductivity values. The inclusions representing regions with moderate, poor, and high conductivity were carefully chosen to create sharp contrasts in conductivity. In the experiment, 16 electrodes were placed around a circle, a current injection pattern was applied, and the resulting boundary voltages were recorded. The reconstruction based on absolute signal values, depicted in the center image, tended to smooth out sharp conductivity contrasts, leading to significant artifacts and reduced accuracy in localizing the inclusions. In contrast, the reconstruction based on real signal values provided an accurate representation of the true conductivity distribution, improving the localization of the inclusions. The results underscore the critical role of considering the real component of the signal in electrical impedance tomography image reconstruction to achieve improved accuracy and higher fidelity in the resulting images. Full article

Background: Extubation failure increases morbidity and mortality. Non-invasive ventilation (NIV), including high-flow nasal cannula (HFNC), can reduce reintubation rates. Current practice often involves prophylactic use of NIV post-extubation. Electrical Impedance Tomography (EIT) provides real-time monitoring of pulmonary distribution and ventilation. Recent adult studies suggest that EIT has potential in extubation failure prediction, but evidence in children is limited. Our objectives were to evaluate peri-extubation regional lung volume/distribution and to explore EIT-derived physiological changes and on post-extubation respiratory support patterns in critically ill children. Methods: A prospective observational study included intubated patients aged 1 month to 18 years in the PICU who were intubated for over 24 h. Vital signs and chest EIT were recorded pre-extubation (H0), immediately post-extubation (H1), at 30 min (H2), and at 4 h (H3). Patients were categorized by chest X-ray findings into abnormal or normal groups. Results: Among 209 ventilated patients, 54 were included. End-expiratory lung impedance (∆EELI), tidal impedance (TID), and the global inhomogeneity index (GI) demonstrated significant changes across predefined peri-extubation time points. Thirty-eight (70.4%) patients received HFNC or NIV immediately after extubation. No extubation failures occurred, precluding evaluation of extubation failure predictors. In the subgroup analyzed based on chest X-ray findings, differences in TID and ODCL were observed between patients with normal and abnormal chest X-rays immediately after extubation. Conclusions: The ∆EELI, TID, and GI demonstrated significant changes across predefined peri-extubation time points. In the absence of extubation failure events, the ability of EIT monitoring to evaluate extubation failure could not be assessed. The frequent use of prophylactic NIV support after extubation may have influenced post-extubation physiology. Full article

We apply a new operator neural network to solve the Electrical Impedance Tomography (EIT) inverse problem. The EIT inverse problem involves reconstructing the conductivity inside a specific body or domain, given the electric potential along the boundary of said body. Mathematically speaking, the inverse problem is known to be severely ill-posed, that is, hard to reliably solve. However, we demonstrate the efficacy of our proposed algorithm utilizing the aforementioned neural network, dubbed the Radial Basis Operator Network (RBON) in its seminal work, when applied to the EIT inverse problem. Full article

Pulmonary electrical impedance tomography (EIT) offers non-invasive and real-time imaging in a compact device size, making it valuable for pulmonary ventilation monitoring. However, conventional EIT stimulation patterns face a trade-off dilemma between anti-noise performance and image interpretability. To address this challenge, we propose a novel mixed stimulation pattern that integrates opposite and adjacent stimulation patterns with a tunable weight ratio. The results of simulations and human experiments (involving 30 subjects) demonstrated that the mixed stimulation pattern uses 200 stimulation–measurement channels, preserves a high signal-to-noise ratio, improves lung separation, and reduces artifacts compared with the opposite and adjacent stimulation patterns. It maintained stable imaging at 600 μA of stimulation current amplitude (equivalent to 1 mA) and preserved most imaging and clinical indicators’ stability at 200 μA (except GI/RVD_SD). The adjustable weight ratio enables imaging performance to be flexibly adjusted according to different noise levels in acquisition environments. In conclusion, the pattern we proposed offers a superior alternative to traditional patterns, achieving a favorable balance of real-time capability, anti-noise performance, and image interpretability for pulmonary EIT imaging. Full article

High-flow nasal cannula (HFNC) therapy is frequently described as a positive pressure modality, yet this classification lacks mechanistic support. This critical narrative review integrates experimental, computational, and clinical evidence to examine the established physiological mechanisms underlying HFNC, with emphasis on precise terminology. The study clarifies that labeling HFNC as “positive pressure” is conceptually inaccurate, as the system delivers transient, flow-dependent pressures characteristic of open-circuit administration. Evidence is synthesized to quantify the relative contributions of nasopharyngeal dead-space clearance versus emergent pressure generation. Unlike CPAP, HFNC produces pressures ranging from 0.2 to 13.5 cmH₂O, determined by airway geometry, leak magnitude, and mouth position. Fluid dynamic modeling using Bernoulli and Darcy–Weisbach equations demonstrates oscillatory rather than sustained pressures, with magnitudes linked to nasopharyngeal Reynolds numbers (2400–6000) and turbulent energy dissipation (30–60%). Clinical efficacy persists despite variable pressures, reflecting synergistic mechanisms: inspiratory flow matching (40–50% reduction in work of breathing), dead-space clearance (CO₂ reduction, r = −0.77, p < 0.05), emergent pressure effects (10–20%), and thermal humidification (10–20%). Electrical impedance tomography reveals heterogeneous alveolar recruitment, with high-potential (54%) and low-potential (46%) phenotypes. Based on these mechanistic insights, this review proposes the term “emergent hydrodynamic pressure” to accurately describe HFNC’s transient, flow-dependent pressures. This terminology differentiates HFNC from conventional positive pressure systems and aligns language with the principles of fluid dynamics and respiratory physiology. Full article

Background: Early reports highlighted unique features of COVID-19-associated ARDS. The combination of prone position (PP) and positive end-expiratory pressure (PEEP)-induced lung recruitment maneuver (LRM) has demonstrated efficacy in enhancing oxygenation and improving outcomes in patients with ARDS, but it remains unknown whether there is a difference between COVID-19 ARDS and non-COVID-19 ARDS. Method: This study is a secondary analysis of a previously conducted randomized controlled trial. Patients with moderate to severe ARDS were consecutively enrolled during the study period (June–December 2023). After initiation of PP, patients received a PEEP-induced LRM followed by 12 h of daily PP. The interventions were repeated at least three times over the subsequent 3 days. Clinical outcomes, respiratory mechanics, and electrical impedance tomography (EIT) results were evaluated. Results: Twenty-eight patients were included in the final analysis, half of whom were infected with COVID-19 (50%). The PEEP-induced LRM led to greater improvement in oxygenation among COVID-19 ARDS than non-COVID-19 ARDS (∆PaO₂/FiO₂ ratio 90.5 mmHg vs. 65.5 mmHg, p < 0.05). Based on EIT measurement, compared with the non-COVID-19 ARDS group, PEEP-induced LRM resulted in a greater increase in ventilation distribution, mainly in the dorsal regions of interest 4 (ROI 4) ventilation distribution (∆ROI4 4.5% vs. 1.0%, p = 0.01) and in dorsal regional ventilation (∆dorsal regional ventilation 10.0% vs. 5.5%, p = 0.04) in the COVID-19 ARDS group. Conclusions: Compared to typical ARDS, PEEP-induced LRM combined with PP may be more effective in enhancing oxygenation in COVID-19-related ARDS. Full article