Abstract:

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.

Abstract:

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.

Abstract:

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.

Abstract:

Pulmonary diseases belong to the most common causes of death worldwide according to the World Health Organization [1]. The Electrical Impedance Tomography (EIT) is already a reliable tool to monitor the distribution of the aeration inside the lungs. However, the possibility to also measure pulmonary perfusion using EIT would provide further important information to the physicians. Therefore, the indicator dilution method has shown promising results in former studies [2], [3], [4], where an injected saline solution acts as a contrast agent for EIT due to its relatively high conductivity. This thesis was about further investigating if an approximation and separation of the com- ponents observed in the resulting signals, i.e. showing the passage of the injected saline solution and a drift over the entire signal, might enable to better calculate pulmonary perfu- sion parameters than just using raw, unmodified EIT indicator signals. Three main approaches were implemented and evaluated using porcine data from a big ani- mal study. The accuracy and robustness of the components approximations was determined by calculating the correlation and the root mean square error (RMS) between the sum of the approximated components in each pixel and the respective origin signals. Furthermore, relative blood flow (rBF) was calculated using the component that is assumed to refer to pulmonary perfusion, and compared against Positron Emission Tomography (PET) scans to validate if the performed approximations have been made reasonable. Although no significant improvement in calculating rBF was achieved compared to the case that simply raw EIT indicator signals instead of approximated components are used, the results even though showed a high consistency to the values received from PET. The good results allowed to further investigate and explain the reasons for the observed behavior of indicator signals in EIT. Moreover, the separation of the different indicator dilution compo- nents enables the estimation of additional hidden information about the state of the regional perfusion such as blood transit times.