Evaluation of surrogate measures of pulmonary function derived from electrical impedance tomography data in children with cystic fibrosis

Review of electrical impedance tomography in the pediatric patient

Electrical impedance tomography (EIT) is a new method of monitoring non-invasive mechanical ventilation, at the bedside and useful in critically ill patients. It allows lung monitoring of ventilation and perfusion, obtaining images that provide information on lung function. It is based on the physical principle of impedanciometry or the body's ability to conduct an electrical current. Various studies have shown its usefulness both in adults and in pediatrics in respiratory distress syndrome, pneumonia and atelectasis in addition to pulmonary thromboembolism and pulmonary hypertension by also providing information on pulmonary perfusion, and may be very useful in perioperative medicine; especially in pediatrics avoiding repetitive imaging tests with ionizing radiation.

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.

Using electrical impedance tomography to characterize lung impairment of children with primary ciliary dyskinesia: A pilot cross-sectional study

BACKGROUND

In children with primary ciliary dyskinesia (PCD), measures more sensitive than spirometry are needed to characterize underlying pulmonary impairment. Electrical impedance tomography (EIT) is a promising noninvasive method for monitoring the distribution of lung ventilation, and it does not require patient collaboration. We aimed to provide an assessment of the feasibility and clinical usefulness of EIT in characterizing lung impairment in children with PCD, compared to spirometry and multiple breath nitrogen washout (MBWN₂ ) test.

METHODS

Children and adolescents with PCD underwent MBWN₂ test as first respiratory assessment, followed by EIT monitoring and spirometry during outpatient follow-up.

RESULTS

We included 12 out of 16 individuals regularly followed at our clinic. A total of 41.7% (5/12) showed abnormal forced expiratory volume in 1 s (FEV₁ ), whereas 11/12 (91.7%) had abnormal ventilation inhomogeneity measured with MBWN₂ test. Using EIT, the global inhomogeneity (GI_TOT ) index showed moderate to strong correlation with FEV₁ (ρ = -0.55, 95% confidence interval [CI]: -0.87 to 0.02) and ranged from 37 to 44, with the highest inhomogeneity detected in the dorsal right quadrant. GI_TOT was moderately correlated with RV/TLC %predicted (ρ = 0.38, 95% CI: -0.17 to 0.74), while we detected a weak correlation between GI_TOT and lung clearance index (ρ = 0.29, 95% CI: -0.45 to 0.82).

CONCLUSION

EIT appears promising as a noninvasive technique to characterize ventilation distribution in children with PCD, thus providing a complementary assessment to static and dynamic lung function measures of PCD disease.

Improved resolution of D-bar images of ventilation using a Schur complement property and an anatomical atlas

BACKGROUND

Electrical impedance tomography (EIT) is a nonionizing imaging technique for real-time imaging of ventilation of patients with respiratory distress. Cross-sectional dynamic images are formed by reconstructing the conductivity distribution from measured voltage data arising from applied alternating currents on electrodes placed circumferentially around the chest. Since the conductivity of lung tissue depends on air content, blood flow, and the presence of pathology, the dynamic images provide regional information about ventilation, pulsatile perfusion, and abnormalities. However, due to the ill-posedness of the inverse conductivity problem, EIT images have a coarse spatial resolution. One method of improving the resolution is to include prior information in the reconstruction.

PURPOSE

In this work, we propose a technique in which a statistical prior built from an anatomical atlas is used to postprocess EIT reconstructions of human chest data. The effectiveness of the method is demonstrated on data from two patients with cystic fibrosis.

METHODS

A direct reconstruction algorithm known as the D-bar method was used to compute a two-dimensional reconstruction of the conductivity distribution in the plane of the electrodes. Reconstructions using one step in an iterative (regularized) Newton's method were also computed for comparison. An anatomical atlas consisting of 1589 synthetic EIT images computed from X-ray computed tomography (CT) scans of 74 adult male subjects was computed for use as a statistical prior. The resolution of the D-bar images was then improved by maximizing the conditional probability density function of an image that is consistent with the a priori information and the statistical model. A new method to evaluate the accuracy of the EIT images using CT scans of the imaged patient as ground truth is presented. The novel approach is tested on data from two patients with cystic fibrosis.

RESULTS AND CONCLUSIONS

The D-bar images resulted in better structural similarity index measures (SSIM) and multiscale (MS) SSIM measures for both subjects using the mask or amplitude evaluation approach than the one-step (regularized) Newton's method. Further improvement was achieved using the Schur complement (SC) approach, with MS-SSIM values of 0.718 and 0.682 using SC evaluated with the mask and amplitude approach, respectively, for Patient 1, and MS-SSIM values of 0.726 and 0.692 using SC evaluated with the mask and amplitude approach, respectively, for Patient 2. The results from applying an anatomical atlas and statistical prior to EIT data from two patients with cystic fibrosis suggest that the spatial resolution of the EIT image can be improved to reveal pathology that may be difficult to discern in the original EIT image. The novel metric of evaluation is consistent with the appearance of improved spatial resolution and provides a new way to evaluate the accuracy of EIT reconstructions when a CT scan is available.

Evaluation of Pulmonary Structure and Function in Patients with Cystic Fibrosis from Electrical Impedance Tomography Data

Electrical impedance tomography (EIT) is a medical imaging technique in which low frequency, low amplitude electromagnetic fields applied through electrodes on the skin are used to compute the conductivity and/or permittivity inside the body and form functional images from the reconstructed values. This work describes methods of computing EIT-derived surrogate measures of pulmonary function and identifying regions of air trapping and consolidation from functional EIT images. These methods were developed for pediatric patients with cystic fibrosis, for whom a real-time non-ionizing imaging modality can be of great benefit for monitoring disease progression or a pulmonary exacerbation.

3D ELECTRICAL IMPEDANCE TOMOGRAPHY RECONSTRUCTIONS FROM SIMULATED ELECTRODE DATA USING DIRECT INVERSION texp AND CALDERÓN METHODS

The first numerical implementation of a t ^exp method in 3D using simulated electrode data is presented. Results are compared to Calderón's method as well as more common TV and smoothness regularization-based methods. The t ^exp method for EIT is based on tailor-made non-linear Fourier transforms involving the measured current and voltage data. Low-pass filtering in the non-linear Fourier domain is used to stabilize the reconstruction process. In 2D, t ^exp methods have shown great promise for providing robust real-time absolute and time-difference conductivity reconstructions but have yet to be used on practical electrode data in 3D, until now. Results are presented for simulated data for conductivity and permittivity with disjoint non-radially symmetric targets on spherical domains and noisy voltage data. The 3D t ^exp and Calderón methods are demonstrated to provide comparable quality to their 2D counterparts, and hold promise for real-time reconstructions due to their fast, non-optimized, computational cost.

Electrical impedance tomography detects changes in ventilation after airway clearance in spinal muscular atrophy type I

The effect of mechanical insufflation-exsufflation (MIE) for airway clearance in patients with spinal muscular atrophy type I (SMA-I) on the distribution of ventilation in the lung is unknown, as is the duration of its beneficial effects. A pilot study to investigate the feasibility of using three dimensional (3-D) electrical impedance tomography (EIT) images to estimate lung volumes pre- and post-MIE for assessing the effectiveness of mechanical insufflation-exsufflation (MIE) was conducted in 6 pediatric patients with SMA-I in the neuromuscular clinic at Children's Hospital Colorado. EIT data were collected before, during, and after the MIE procedure on two rows of 16 electrodes placed around the chest. Lung volumes were computed from the images and compared before, during, and after the MIE procedure to assess the ability of EIT to estimate changes in lung volume during insufflation and exsufflation. Images of pulsatile pulmonary perfusion were computed in subjects able to perform breath-holding. In four of the six subjects, lung volumes during tidal breathing increased after MIE (average change from pre to post MIE was 58.8±55.1 mL). The time-dependent plots of lung volume computed from the EIT data clearly show when the MIE device insufflates and exsufflates air and the rest periods between mechanical coughs. Images of pulmonary pulsatile perfusion were computed from data collected during breathing pauses. The results suggest that EIT holds promise for estimating lung volumes and ventilation/perfusion mismatch, both of which are useful for assessing the effectiveness of MIE in clearing mucus plugs.

A narrative review of electrical impedance tomography in lung diseases with flow limitation and hyperinflation: methodologies and applications

Electrical impedance tomography (EIT) is a functional radiation-free imaging technique that measures regional lung ventilation distribution by calculating the impedance changes in the corresponding regions. The aim of the present review was to summarize the current literature concerning the methodologies and applications of EIT in lung diseases with flow limitation and hyperinflation. PubMed was searched up to May 2020 to identify studies investigating the use of EIT in patients with asthma, bronchiectasis, bronchitis, bronchiolitis, chronic obstructive pulmonary disease, and cystic fibrosis. The extracted data included study design, EIT methodologies, interventions, validation and comparators, population characteristics, and key findings. Of the 44 included studies, seven were related to simulation, animal experimentation, or reconstruction algorithm development with evaluation on patients; 27 studies had the primary objective of validating EIT technique and measures including regional ventilation distribution, regional EIT-spirometry parameters, end-expiratory lung impedance, and regional time constants; and 10 studies had the primary objective of applying EIT to monitor the response to therapeutic interventions, including various ventilation supports, patient repositioning, and airway suctioning. In pediatric and adult patients, EIT has been successfully validated for assessing spatial and temporal ventilation distribution, measuring changes in lung volume and flow, and studying regional respiratory mechanics. EIT has also demonstrated potential as an alternative or supplement to well-established measurement modalities (e.g., conventional pulmonary function testing) to monitor the progression of obstructive lung diseases, although the existing literature lacks prediction values as references and lacks clinical outcome evidence.

Introduction of Sample Based Prior into the D-Bar Method Through a Schur Complement Property

Electrical impedance tomography (EIT) is a non-invasive medical imaging technique in which images of the conductivity in a region of interest in the body are computed from measurements of voltages on electrodes arising from low-frequency, low-amplitude applied currents. Mathematically, the inverse conductivity problem is nonlinear and ill-posed, and the reconstructions have characteristically low spatial resolution. One approach to improve the spatial resolution of EIT images is to include anatomically and physiologically-based prior information in the reconstruction algorithm. Statistical inversion theory provides a means of including prior information from a representative sample population. In this paper, a method is proposed to introduce statistical prior information into the D-bar method based on Schur complement properties. The method presents an improvement of the image obtained by the D-bar method by maximizing the conditional probability density function of an image that is consistent with a prior information and the model, given a D-bar image computed from the voltage measurements. Experimental phantoms show an improved spatial resolution by the use of the proposed method for the D-bar image reconstructions.

The D-bar method for electrical impedance tomography-demystified

Electrical impedance tomography (EIT) is an imaging modality where a patient or object is probed using harmless electric currents. The currents are fed through electrodes placed on the surface of the target, and the data consists of voltages measured at the electrodes resulting from a linearly independent set of current injection patterns. EIT aims to recover the internal distribution of electrical conductivity inside the target. The inverse problem underlying the EIT image formation task is nonlinear and severely ill-posed, and hence sensitive to modeling errors and measurement noise. Therefore, the inversion process needs to be regularized. However, traditional variational regularization methods, based on optimization, often suffer from local minima because of nonlinearity. This is what makes regularized direct (non-iterative) methods attractive for EIT. The most developed direct EIT algorithm is the D-bar method, based on Complex Geometric Optics solutions and a nonlinear Fourier transform. Variants and recent developments of D-bar methods are reviewed, and their practical numerical implementation is explained.

Reconstruction of Organ Boundaries With Deep Learning in the D-Bar Method for Electrical Impedance Tomography

OBJECTIVE

Medical electrical impedance tomography is a non-ionizing imaging modality in which low-amplitude, low-frequency currents are applied on electrodes on the body, the resulting voltages are measured, and an inverse problem is solved to determine the conductivity distribution in the region of interest. Due the ill-posedness of the inverse problem, the boundaries of internal organs are typically blurred in the reconstructed image.

METHODS

A deep learning approach is introduced in the D-bar method for reconstructing a 2-D slice of the thorax to recover the boundaries of organs. This is accomplished by training a deep neural network on labeled pairs of scattering transforms and the boundaries of the organs in the data from which the transforms were computed. This allows the network to "learn" the nonlinear mapping between them by minimizing the error between the output of the network and known actual boundaries. Further, a "sparse" reconstruction is computed by fusing the results of the standard D-bar reconstruction with reconstructed organ boundaries from the neural network.

RESULTS

Results are shown on simulated and experimental data collected on a saline-filled tank with agar targets simulating the conductivity of the heart and lungs.

CONCLUSIONS AND SIGNIFICANCE

The results demonstrate that deep neural networks can successfully learn the mapping between scattering transforms and the internal boundaries of structures.

B-Spline Level Set Method for Shape Reconstruction in Electrical Impedance Tomography

A B-spline level set (BLS) based method is proposed for shape reconstruction in electrical impedance tomography (EIT). We assume that the conductivity distribution to be reconstructed is piecewise constant, transforming the image reconstruction problem into a shape reconstruction problem. The shape/interface of inclusions is implicitly represented by a level set function (LSF), which is modeled as a continuous parametric function expressed using B-spline functions. Starting from modeling the conductivity distribution with the B-spline based LSF, we show that the shape modeling allows us to compute the solution by restricting the minimization problem to the space spanned by the B-splines. As a consequence, the solution to the minimization problem is obtained in terms of the B-spline coefficients. We illustrate the behavior of this method using simulated as well as water tank data. In addition, robustness studies considering varying initial guesses, differing numbers of control points, and modeling errors caused by inhomogeneity are performed. Both simulation and experimental results show that the BLS-based approach offers clear improvements in preserving the sharp features of the inclusions in comparison to the recently published parametric level set method.

The ACE1 Electrical Impedance Tomography System for Thoracic Imaging

The design and performance of the ACE1 (Active Complex Electrode) electrical impedance tomography system for single-ended phasic voltage measurements is presented. The design of the hardware and calibration procedures allow for reconstruction of conductivity and permittivity images. Phase measurement is achieved with the ACE1 active electrode circuit which measures the amplitude and phase of the voltage and the applied current at the location at which current is injected into the body. An evaluation of the system performance under typical operating conditions includes details of demodulation and calibration and an in-depth look at insightful metrics, such as signal-to-noise ratio variations during a single current pattern. Static and dynamic images of conductivity and permittivity are presented from ACE1 data collected on tank phantoms and human subjects to illustrate the system's utility.

A Review of Electrical Impedance Tomography in Lung Applications: Theory and Algorithms for Absolute Images

Electrical Impedance Tomography (EIT) is under fast development, the present paper is a review of some procedures that are contributing to improve spatial resolution and material properties accuracy, admitivitty or impeditivity accuracy. A review of EIT medical applications is presented and they were classified into three broad categories: ARDS patients, obstructive lung diseases and perioperative patients. The use of absolute EIT image may enable the assessment of absolute lung volume, which may significantly improve the clinical acceptance of EIT. The Control Theory, the State Observers more specifically, have a developed theory that can be used for the design and operation of EIT devices. Electrode placement, current injection strategy and electrode electric potential measurements strategy should maximize the number of observable and controllable directions of the state vector space. A non-linear stochastic state observer, the Unscented Kalman Filter, is used directly for the reconstruction of absolute EIT images. Historically, difference images were explored first since they are more stable in the presence of modelling errors. Absolute images require more detailed models of contact impedance, stray capacitance and properly refined finite element mesh where the electric potential gradient is high. Parallelization of the forward program computation is necessary since the solution of the inverse problem often requires frequent solutions of the forward problem. Several reconstruction algorithms benefit by the Bayesian inverse problem approach and the concept of prior information. Anatomic and physiologic information are used to form the prior information. An already tested methodology is presented to build the prior probability density function using an ensemble of CT scans and in vivo impedance measurements. Eight absolute EIT image algorithms are presented.