Electrical impedance tomography is clinically used to trace ventilation related changes in electrical conductivity of lung tissue. Estimating regional pulmonary perfusion using electrical impedance tomography is still a matter of research. To support clinical decision making, reliable bedside information of pulmonary perfusion is needed. We introduce a method to robustly detect pulmonary perfusion based on indicator-enhanced electrical impedance tomography and validate it by dynamic multidetector computed tomography in two experimental models of acute respiratory distress syndrome. The acute injury was induced in a sublobar segment of the right lung by saline lavage or endotoxin instillation in eight anesthetized mechanically ventilated pigs. For electrical impedance tomography measurements, a conductive bolus (10% saline solution) was injected into the right ventricle during breath hold. Electrical impedance tomography perfusion images were reconstructed by linear and normalized Gauss-Newton reconstruction on a finite element mesh with subsequent element-wise signal and feature analysis. An iodinated contrast agent was used to compute pulmonary blood flow via dynamic multidetector computed tomography. Spatial perfusion was estimated based on first-pass indicator dilution for both electrical impedance and multidetector computed tomography and compared by Pearson correlation and Bland-Altman analysis. Strong correlation was found in dorsoventral (r = 0.92) and in right-to-left directions (r = 0.85) with good limits of agreement of 8.74% in eight lung segments. With a robust electrical impedance tomography perfusion estimation method, we found strong agreement between multidetector computed and electrical impedance tomography perfusion in healthy and regionally injured lungs and demonstrated feasibility of electrical impedance tomography perfusion imaging.
BACKGROUND: Electrical impedance tomography (EIT) with indicator dilution may be clinically useful to measure relative lung perfusion, but there is limited information on the performance of this technique. METHODS: Thirteen pigs (50-66 kg) were anaesthetised and mechanically ventilated. Sequential changes in ventilation were made: (i) right-lung ventilation with left-lung collapse, (ii) two-lung ventilation with optimised PEEP, (iii) two-lung ventilation with zero PEEP after saline lung lavage, (iv) two-lung ventilation with maximum PEEP (20/25 cm HO to achieve peak airway pressure 45 cm HO), and (v) two-lung ventilation under unilateral pulmonary artery occlusion. Relative lung perfusion was assessed with EIT and central venous injection of saline 3%, 5%, and 10% (10 ml) during breath holds. Relative perfusion was determined by positron emission tomography (PET) using Gallium-labelled microspheres. EIT and PET were compared in eight regions of equal ventro-dorsal height (right, left, ventral, mid-ventral, mid-dorsal, and dorsal), and directional changes in regional perfusion were determined. RESULTS: Differences between methods were relatively small (95% of values differed by less than 8.7%, 8.9%, and 9.5% for saline 10%, 5%, and 3%, respectively). Compared with PET, EIT underestimated relative perfusion in dependent, and overestimated it in non-dependent, regions. EIT and PET detected the same direction of change in relative lung perfusion in 68.9-95.9% of measurements. CONCLUSIONS: The agreement between EIT and PET for measuring and tracking changes of relative lung perfusion was satisfactory for clinical purposes. Indicator-based EIT may prove useful for measuring pulmonary perfusion at bedside.
Heart rate variability (HRV) plays an important role in medicine and psychology because it is used to quantify imbalances of the autonomic nervous system (ANS). An important manifestations of the ANS on HRV is also directly related to respiration and it is called respiratory sinus arrhythmia (RSA). This is a controlled phenomenon that leads to a synchronized coupling between respiration and instantaneous heart rate. Thus, the portion of HRV that is not related to respiration, and could potentially contain undiscovered diagnostic value, is overlapped and remains hidden in a standard HRV analysis. In such cases, a decoupling procedure would deliver a discriminated HRV analysis and possible new insights about the regulation of the cardiovascular system. In this work, we propose an algorithm based on Granger's causality to measure coupling between respiration and HRV. In the case of significant coupling, we estimate and cancel the respiration driven HRV component using a linear filtering approach. We tested the method using synthetic signals and prove it to deliver a reliable coupling measurement in 96.3% of the cases and reconstruct respiration free signals with a median correlation coefficient of 0.992. Afterwards, we applied our method to signals recorded during paced respiration and during natural breathing. We demonstrated that coupling is dependent on respiratory frequency and that it maximizes at 0.3 Hz. Furthermore, the HRV parameters measured during paced respiration tend to level among subjects after decoupling. The intersubject variability of HRV parameter is also decreased after the separation process. During natural breathing, coupling is notoriously lower to non-existing and decoupling has little impact on HRV. We conclude that the method proposed here can be used to investigate the diagnostic value of respiration independent HRV parameters.
To measure blood flow distributions within the lungs at bedside, Electrical Impedance Tomography measurements based on conductive indicator signals have been recently proposed. The first passage of the indicator signal through the lungs is exploited, but needs to be separated from a superimposed slow drift signal. Two fitting approaches are presented in this paper to accomplish this separation task. The accuracy of estimated first pass signal features is investigated on a synthetic data base. Both algorithms alter the shape of the indicator signal similarly. The algorithms are finally tested on real data from a preclinical porcine study.
M. Kircher, R. Hattiangdi, R. Menges, and O. Dössel. Influence of background lung tissue conductivity on the cardiosynchronous EIT signal components: a sensitivity study.. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, vol. 2019, pp. 1547-1550, 2019
Electrical impedance tomography is an accepted and validated tool to analyze and support mechanical ventilation at the bedside. In the future it could furthermore clinically provide information of the pulmonary perfusion and other blood volume changes within the thorax by exploiting a cardiosynchronous EIT component. In the presented study, the spatial forward sensitivity against different background lung tissue distributions was analyzed. Spheres with a 10% change of the background conductivity were introduced in the lungs and in the heart. The cranio-caudal distribution of sensitivity had a bell shape and was similar between all simulated scenarios, varying only in magnitude. If the background tissue conductivity within the lungs was chosen to be the one of deflated tissue, the overall sensitivity was 46% smaller compared to the overall sensitivity against inflated lung tissue conductivity. Within the heart region, the sensitivity was increased for fully deflated lung tissue conductivity (23% relative to the sensitivity in the lungs) compared to a homogeneous distribution of inflated lung tissue conductivity (10% relative to the sensitivity in the lungs).
M. Kircher, R. Menges, G. Lenis, and O. Dössel. Respiratory influence on HRV parameters analyzed during controlled respiration, spontaneous respiration and apnoe. In Current Directions in Biomedical Engineering, vol. 3(2) , pp. Abstract, 2017
The heart rate variability (HRV) is a measure which is commonly used to assess sympathetic and parasympathetic auto-nomic function. It is well known, that respiration can have a strong influence on HRV. Especially, a phenomenon called Respiratory Sinus Arrythmia (RSA) modulates the RR intervals and is a major contributor to the HRV. The interpreta-tion of common HRV parameters can be ambiguous due to different respiration rates and patterns. To assess this ambi-guity, the coupling of RSA on HRV was quantified and the HRV parameters were compared during different respirato-ry states.A pilot study with five healthy subjects was performed. A three lead ECG was acquired and the respiration was estimat-ed by measuring the aeration of the lungs using the PulmoVista 500 by Dräger. This device uses Electrical Impedance Tomography (EIT) to monitor impedance changes due to the changing amount of air within the lungs during respira-tion. The subjects were asked to breath at controlled respiration rates of 8, 15 and 24 breaths per minute as well as spon-taneously for 1 min each. In addition, to analyze HRV during apnoic phases without any respiration, the subjects were asked to hold their breath for 40s at end-inspiration and end-expiration. After preprocessing of the ECG and the respiration signal, the coupling between the measured respiration and the RR intervals was quantified using the Granger causality. If significant coupling was present, the HRV was separated from its respiratory influence using an ARMAX model. The measured respiration hereby formed the exogeneous input to the filter. Finally, common HRV parameters were calculated for the original and the decoupled RR intervals.We showed, that coupling strength depends on respiratory rates, which might complicate HRV interpretation. Moreo-ver, the coupling is decreased during spontaneous breathing in comparison to controlled respiration. Additionally we found, that HRV parameters during apnoic phases differ from decoupled HRV parameters during spontaneous or con-trolled respiration.
M. Kircher, G. Lenis, and O. Dössel. Separating the effect of respiration from the Heart Rate Variability for cases of constant harmonic breathing. In Current Directions in Biomedical Engineering, vol. 1(1) , pp. 46-49, 2015
Heart Rate Variability studies are a known measure for the autonomous control of the heart rate. In special situations, its interpretation can be ambiguous, since the respiration has a major influence on the heart rate variability. For this reason it has often been proposed to measure Heart Rate Variability, while the subjects are breathing at a constant respiration rate. That way the spectral influence of the respiration is known. In this work we propose to remove this constant respiratory influence from the heart rate and the Heart Rate Variability parameters to gain respiration free autonomous controlled heart rate signal. The spectral respiratory component in the heart rate signal is detected and characterized. Subsequently the respiratory effect on Heart Rate Variability is removed using spectral filtering approaches, such as the Notch filter or the Raised Cosine filter. As a result new decoupled Heart Variability parameters are gained, which could lead to new additional interpretations of the autonomous control of the heart rate.
Electrical Impedance Tomography (EIT) is a clini- cally used tool for bed-side monitoring of ventilation. Previous work also showed a high potential for lung perfusion moni- toring with indicator-enhanced EIT. However, many research questions have yet to be answered before it can be broadly ap- plied in clinical everyday life. The goal of this work is to eval- uate a new method to improve EIT perfusion measurements. Pulmonary hemodynamic transfer functions were estimated using regularized deconvolution with Tikhonov regularization to estimate spatial perfusion parameters. The final comparison between EIT images and PET scans showed a median corre- lation of 0.897 for the images which were reconstructed using the regularized deconvolution. In comparison the previously used maximum slope method led to a median correlation of 0.868.
The indicator dilution method (IDM) is one approach to measure pulmonary perfusion using Electrical Impedance Tomography (EIT). To be able to calculate perfu- sion parameters and to increase robustnes, it is necessary to approximate and then to separate the components of the mea- sured signals. The component referring to the passage of the injected bolus through the pixels can be modeled as a gamma variate function, its parameters are often determined using nonlinear optimization algorithms. In this paper, we introduce a linear approach that enables higher robustnes and faster com- putation, and compare the linear and nonlinear fitting approach on data of an animal study.
Lung ventilation and perfusion analyses using chest imaging methods require a correct segmentation of the lung to offer anatomical landmarks for the physiological data. An automatic segmentation approach simplifies and accelerates the analysis. However, the segmentation of the lungs has shown to be difficult if collapsed areas are present that tend to share similar gray values with surrounding non-pulmonary tissue. Our goal was to develop an automatic segmentation algorithm that is able to approximate dorsal lung boundaries even if alveolar collapse is present in the dependent lung areas adjacent to the pleura. Computed tomography data acquired in five supine pigs with injured lungs were used for this purpose. First, healthy lung tissue was segmented using a standard 3D region growing algorithm. Further, the bones in the chest wall surrounding the lungs were segmented to find the contact points of ribs and pleura. Artificial boundaries of the dorsal lung were set by spline interpolation through these contact points. Segmentation masks of the entire lung including the collapsed regions were created by combining the splines with the segmentation masks of the healthy lung tissue through multiple morphological operations. The automatically segmented images were then evaluated by comparing them to manual segmentations and determining the Dice similarity coefficients (DSC) as a similarity measure. The developed method was able to accurately segment the lungs including the collapsed regions (DSCs over 0.96).
Radiofrequency ablation is the gold standard for treating cardiac arrhythmias. However, the success rate of this procedure depends on numerous parameters. Wet lab experiments provide the opportunity to investigate cardiac electrophysiology under reproducible conditions. To evaluate the electrophysiological changes of ablated myocardium in these studies it is necessary to consider the three-dimensional (3D) geometry of the lesions. For this purpose, we investigated the usage of different magnetic resonance imaging (MRI) sequences as well as an image processing procedure to analyze in-vitro preparations. To differentiate signal intensities between nonablated and ablated tissue we evaluated FISP (fast imaging with steady-state precession; delivering dominantly T1-weighted images) and RARE (rapid acquisition with relaxation enhancement; delivering dominantly T2-weighted images). After image processing, the ablated tissue was segmented in each image slice forming a 3D volume. The geometry of the lesion was modeled by the boundary of this volume. It was generally feasible to distinguish between healthy myocardium and ablated tissue as well as to determine lesion transmurality. The analysis of the reconstructed lesion geometries from FISP and RARE MRI showed a high agreement, however T2-weighted sequences showed larger lesion volumes as well as higher variations in segmentation compared to T1- mapping. FISP with higher quality may be used to reconstruct the 3D geometry of the ablation lesions.
Electrical impedance tomography (EIT) can trace ventilation and perfusion related changes in electrical properties of lung tissue. So far, the ability of EIT to assess lung perfusion has been examined experimentally by electron-beam and single-photon-emission computerized tomography (CT) in healthy or globally injured lungs [1,2].
M. Kircher. Towards an Efficient Gas Exchange Monitoring with Electrical Impedance Tomography. Dissertation. 2020
In many patients suffering from severely impaired gas exchange of the lungs, regional pulmonary ventilation and perfusion are not aligned. Especially, if patients are suffering from the acute respiratory distress syndrome, very heterogeneous distributions of ventilation and perfusion are observed, and patients need to be artificially ventilated and monitored in an intensive care unit, in order to ensure sufficient gas exchange. In severely injured lungs, it is very challenging to find an optimal trade off between recruiting collapsed regions by applying high pressures and volumes, while protecting the lung from further damage caused by the externally applied pressure. In order to ensure lung protective ventilation and to optimize and to support clinical decision making, a growing need for bedside monitoring of regional lung ventilation, as well as regional perfusion, has been reported. Electrical Impedance Tomography (EIT) is a non-invasive, radiation-free and portable system, which has raised interest especially among physicians treating critically ill patients in ICUs. It provides high temporal sampling and a functional spatial resolution, which allows to visualize and monitor dynamic (patho-) physiological processes. Medical EIT research has mainly focused on estimating spatial ventilation distributions, and commercially available systems have proven that EIT is a valuable extension for clinical decision making during mechanical ventilation. Estimating pulmonary perfusion with EIT nevertheless has not been established yet and might represent the missing link to enable the analysis of pulmonary gas exchange at the bedside. Though some publications have shown the principle feasibility of indicator-enhanced EIT to estimate spatial distributions of pulmonary blood flow, the methods need to be optimized and validated against gold-standards of pulmonary perfusion monitoring. Additionally, further research is needed to understand the underlying physiological information of EIT perfusion estimations. With this thesis, we aim to contribute to the question, whether EIT can be applied clinically to provide spatial information of pulmonary blood flow alongside regional ventilation to potentially assess pulmonary gas exchange at the bedside. Spatial distributions of perfusion were estimated by injecting a conductive saline indicator bolus, to trace the passage of the indicator during its progression through the vascular system of the lungs. We developed and compared different dynamic EIT reconstruction methods as well as perfusion parameter estimations, to be able to robustly assess pulmonary blood flow. The estimated regional EIT perfusion distributions were validated against gold-standard lung perfusion measurement techniques. A first validation has been conducted using data of an experimental animal study, where multidetector Computed Tomography was used as comparative lung perfusionmeasure. On top, a comprehensive preclinical animal study has been conducted to investigate pulmonary perfusion with indicator-enhanced EIT and Positron Emission Tomography during multiple different experimental states. Besides a thorough method comparison, we aimed to investigate the clinical applicability of the indicator-enhanced EIT perfusion measurement by above all analyzing the minimal indicator concentration, which allows robust perfusion estimations and presents no harm to the patient. Besides the experimental validation studies, we conducted two in-silico investigations to firstly evaluate the sensitivity of EIT to the passage of a conductive indicator through the lungs in front of severely heterogeneous pulmonary backgrounds. Secondly, we studied the physiological contributors to the reconstructed EIT perfusion image to find basic limitations of the method. We concluded, that pulmonary perfusion estimation based on indicator-enhanced EIT shows great potential to be applied in clinical practice, since we were able to validate it against two established perfusion measurement techniques and provided valuable information about the physiological contributors to the estimated EIT perfusion distributions.