Literature DB >> 30965314

Comparison of bolus- and filtering-based EIT measures of lung perfusion in an animal model.

Symon Stowe1, Alistair Boyle, Michaël Sage, Wendy See, Jean-Paul Praud, Étienne Fortin-Pellerin, Andy Adler.   

Abstract

OBJECTIVE: Two main functional imaging approaches have been used to measure regional lung perfusion using electrical impedance tomography (EIT): venous injection of a hypertonic saline contrast agent and imaging of its passage through the heart and lungs, and digital filtering of heart-frequency impedance changes over sequences of EIT images. This paper systematically compares filtering-based perfusion estimates and bolus injection methods to determine to which degree they are related. APPROACH: EIT data was recorded on seven mechanically ventilated newborn lambs in which ventilation distribution was varied through changes in posture between prone, supine, left- and right-lateral positions. Perfusion images were calculated using frequency filtering and ensemble averaging during both ventilation and apnoea time segments for each posture to compare against contrast agent-based methods using Jaccard distance score. MAIN
RESULTS: Using bolus-based EIT measures of lung perfusion as the reference frequency filtering techniques performed better than ensemble averaging and both techniques performed equally well across apnoea and ventilation data segments. SIGNIFICANCE: Our results indicate the potential for use of filtering-based EIT measures of heart-frequency activity as a non-invasive proxy for contrast agent injection-based measures of lung perfusion.

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Year:  2019        PMID: 30965314     DOI: 10.1088/1361-6579/ab1794

Source DB:  PubMed          Journal:  Physiol Meas        ISSN: 0967-3334            Impact factor:   2.833


  1 in total

Review 1.  Thoracic Electrical Impedance Tomography-The 2022 Veterinary Consensus Statement.

Authors:  Olivia A Brabant; David P Byrne; Muriel Sacks; Fernando Moreno Martinez; Anthea L Raisis; Joaquin B Araos; Andreas D Waldmann; Johannes P Schramel; Aline Ambrosio; Giselle Hosgood; Christina Braun; Ulrike Auer; Ulrike Bleul; Nicolas Herteman; Cristy J Secombe; Angelika Schoster; Joao Soares; Shannon Beazley; Carolina Meira; Andy Adler; Martina Mosing
Journal:  Front Vet Sci       Date:  2022-07-22
  1 in total

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