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.
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.
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.
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.