A model-based source separation algorithm for lung perfusion imaging using electrical impedance tomography

Physiological Measurement, vol. 42, pp. 084001

Abstract

Abstract Objective . Electrical impedance tomography (EIT) for lung perfusion imaging is attracting considerable interest in intensive care, as it might open up entirely new ways to adjust ventilation therapy. A promising technique is bolus injection of a conductive indicator to the central venous catheter, which yields the indicator-based signal (IBS). Lung perfusion images are then typically obtained from the IBS using the maximum slope technique. However, the low spatial resolution of EIT results in a partial volume effect (PVE), which requires further processing to avoid regional bias. Approach . In this work, we repose the extraction of lung perfusion images from the IBS as a source separation problem to account for the PVE. We then propose a model-based algorithm, called gamma decomposition (GD), to derive an efficient solution. The GD algorithm uses a signal model to transform the IBS into a parameter space where the source signals of heart and lung are separable by clustering in space and time. Subsequently, it reconstructs lung model signals from which lung perfusion images are unambiguously extracted. Main results . We evaluate the GD algorithm on EIT data of a prospective animal trial with eight pigs. The results show that it enables lung perfusion imaging using EIT at different stages of regional impairment. Furthermore, parameters of the source signals seem to represent physiological properties of the cardio-pulmonary system. Significance . This work represents an important advance in IBS processing that will likely reduce bias of EIT perfusion images and thus eventually enable imaging of regional ventilation/perfusion (V/Q) ratio.

Authors 8

1. Benjamin Hentze corresponding Aachen Medical Information Technology (MedIT)

   RWTH Aachen University · University of Bonn

   Affiliation as printed

   RWTH Aachen University

   University of Bonn

   Department of Anaesthesiology and Intensive Care Medicine, University of Bonn, Venusberg-Campus 1, 53127 Bonn, Germany

   Medical Information Technology, RWTH Aachen University, Pauwelsstr. 20, 52074 Aachen, Germany

2. Thomas Muders

   University of Bonn

   Affiliation as printed

   University of Bonn

   Department of Anaesthesiology and Intensive Care Medicine, University of Bonn, Venusberg-Campus 1, 53127 Bonn, Germany

3. Christoph Hoog Antink Aachen Medical Information Technology (MedIT)

   RWTH Aachen University · Technische Universität Darmstadt

   Affiliation as printed

   RWTH Aachen University

   TU Darmstadt

   Biomedical Engineering, TU Darmstadt, Darmstadt, Germany

   Medical Information Technology, RWTH Aachen University, Pauwelsstr. 20, 52074 Aachen, Germany

4. Christian Putensen

   University of Bonn

   Affiliation as printed

   University of Bonn

   Department of Anaesthesiology and Intensive Care Medicine, University of Bonn, Venusberg-Campus 1, 53127 Bonn, Germany

5. Anders Larsson

   Uppsala University

   Affiliation as printed

   Uppsala University

   Department of Surgical Sciences, Uppsala University, Uppsala, Sweden

6. Göran Hedenstierna

   Uppsala University

   Affiliation as printed

   Uppsala University

   Department of Medical Sciences, Uppsala University, Uppsala, Sweden

7. Marian Walter Aachen Medical Information Technology (MedIT)

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

   Medical Information Technology, RWTH Aachen University, Pauwelsstr. 20, 52074 Aachen, Germany

8. Steffen K. Leonhardt Aachen Medical Information Technology (MedIT)

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

   Medical Information Technology, RWTH Aachen University, Pauwelsstr. 20, 52074 Aachen, Germany