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Abstract
Abstract The indicator dilution method (IDM) is one approach to measure pulmonary perfusion using Electrical Impedance Tomography (EIT). To be able to calculate perfusion parameters and to increase robustnes, it is necessary to approximate and then to separate the components of the measured 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 computation, and compare the linear and nonlinear fitting approach on data of an animal study.
Highlights
Pulmonary diseases are among the most common causes of death worldwide
We present a fit based on linear least squares (LLS) and compare it to the nonlinear approach based on data of a porcine study [6]
The nonlinear fit is a modification of the method described in [2], the linear fit was developed during this work
Summary
Pulmonary diseases are among the most common causes of death worldwide. A reliable diagnostic method, e.g. based on imaging techniques, is the first step in a successful treatment. EIT allows functional imaging of the lungs and offers - in contrast to MRI, CT or PET - the possibility for diagnostics at bedside, combined with a high temporal resolution [1]. One method to measure pulmonary perfusion using EIT is the IDM, where a saline solution (NaCl) is injected during apnea. Due to its significantly higher conductivity compared to air or blood, the measured conductivity increases in a pixel of the reconstructed image when the injected bolus appears. The observed signal can be considered as a gamma variate function, superimposed by other signal components [3][4].
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