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

Benjamin Hentze1,2, Thomas Muders2, Christoph Hoog Antink1,3

  • 1Medical Information Technology, RWTH Aachen University, Pauwelsstr. 20, 52074 Aachen, Germany.

Physiological Measurement
|June 24, 2021
PubMed
Summary

Electrical impedance tomography (EIT) lung perfusion imaging is improved by a new gamma decomposition (GD) algorithm. This method reduces bias from partial volume effects, enabling clearer visualization of lung perfusion in intensive care settings.

Keywords:
clusteringelectrical impedance tomographyfunctional imagingindicator dilutionphysiological modelingpulmonary perfusionsource separation

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Area of Science:

  • Medical Imaging
  • Physiological Monitoring
  • Critical Care Medicine

Background:

  • Electrical impedance tomography (EIT) is a promising tool for lung perfusion imaging in intensive care.
  • Current methods using indicator-based signals (IBS) are limited by partial volume effects (PVE) due to low spatial resolution.
  • Accurate lung perfusion imaging is crucial for adjusting ventilation therapy and understanding regional ventilation/perfusion (V/Q) ratios.

Purpose of the Study:

  • To address the partial volume effect (PVE) in EIT lung perfusion imaging.
  • To develop and validate a novel algorithm for extracting lung perfusion images from indicator-based signals (IBS).
  • To improve the accuracy and reduce regional bias in EIT-based lung perfusion assessments.

Main Methods:

  • Re-framed lung perfusion image extraction as a source separation problem.
  • Developed a model-based algorithm named gamma decomposition (GD).
  • GD transforms IBS into a parameter space for separating heart and lung signals via spatiotemporal clustering, reconstructing lung signals for unambiguous image extraction.

Main Results:

  • Evaluated the GD algorithm on EIT data from a prospective animal trial (eight pigs).
  • Demonstrated the algorithm's ability to perform lung perfusion imaging across various stages of regional impairment.
  • Observed that source signal parameters may reflect physiological properties of the cardiopulmonary system.

Conclusions:

  • The gamma decomposition (GD) algorithm offers an efficient solution for EIT lung perfusion imaging.
  • GD effectively mitigates partial volume effects, reducing bias in perfusion images.
  • This advancement facilitates more accurate regional ventilation/perfusion (V/Q) ratio imaging in critical care.