GREIT: a unified approach to 2D linear EIT reconstruction of lung images

Andy Adler, John H. Arnold, Richard Bayford, Andrea Borsic, Brian Brown, Paul Dixon, Theo J.C. Faes, Inéz Frerichs, Bartłomiej Grychtol

    Research output: Contribution to journalArticle

    288 Citations (Scopus)

    Abstract

    Electrical impedance tomography (EIT) is an attractive method for clinically monitoring patients during mechanical ventilation, because it can provide a non-invasive continuous image of pulmonary impedance which indicates the distribution of ventilation. However, most clinical and physiological research in lung EIT is done using older and proprietary algorithms; this is an obstacle to interpretation of EIT images because the reconstructed images are not well characterized. To address this issue, we develop a consensus linear reconstruction algorithm for lung EIT, called GREIT (Graz consensus Reconstruction algorithm for EIT). This paper describes the unified approach to linear image reconstruction developed for GREIT. The framework for the linear reconstruction algorithm consists of (1) detailed finite element models of a representative adult and neonatal thorax, (2) consensus on the performance figures of merit for EIT image reconstruction and (3) a systematic approach to optimize a linear reconstruction matrix to desired performance measures. Consensus figures of merit, in order of importance, are (a) uniform amplitude response, (b) small and uniform position error, (c) small ringing artefacts, (d) uniform resolution, (e) limited shape deformation and (f) high resolution. Such figures of merit must be attained while maintaining small noise amplification and small sensitivity to electrode and boundary movement. This approach represents the consensus of a large and representative group of experts in EIT algorithm design and clinical applications for pulmonary monitoring. All software and data to implement and test the algorithm have been made available under an open source license which allows free research and commercial use.
    LanguageEnglish
    PagesS35-S55
    JournalPhysiological Measurement
    Volume30
    Issue number6
    DOIs
    Publication statusPublished - Jun 2009

    Fingerprint

    Acoustic impedance
    Computer-Assisted Image Processing
    Electric Impedance
    Tomography
    Lung
    Image reconstruction
    Patient monitoring
    Physiologic Monitoring
    Licensure
    Ventilation
    Amplification
    Artificial Respiration
    Research
    Artifacts
    Electrodes
    Thorax
    Software
    Monitoring

    Keywords

    • electrical impedance tomography
    • image reconstruction
    • lung physiology

    Cite this

    Adler, A., Arnold, J. H., Bayford, R., Borsic, A., Brown, B., Dixon, P., ... Grychtol, B. (2009). GREIT: a unified approach to 2D linear EIT reconstruction of lung images. Physiological Measurement, 30(6), S35-S55. https://doi.org/10.1088/0967-3334/30/6/S03
    Adler, Andy ; Arnold, John H. ; Bayford, Richard ; Borsic, Andrea ; Brown, Brian ; Dixon, Paul ; Faes, Theo J.C. ; Frerichs, Inéz ; Grychtol, Bartłomiej. / GREIT: a unified approach to 2D linear EIT reconstruction of lung images. In: Physiological Measurement. 2009 ; Vol. 30, No. 6. pp. S35-S55.
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    abstract = "Electrical impedance tomography (EIT) is an attractive method for clinically monitoring patients during mechanical ventilation, because it can provide a non-invasive continuous image of pulmonary impedance which indicates the distribution of ventilation. However, most clinical and physiological research in lung EIT is done using older and proprietary algorithms; this is an obstacle to interpretation of EIT images because the reconstructed images are not well characterized. To address this issue, we develop a consensus linear reconstruction algorithm for lung EIT, called GREIT (Graz consensus Reconstruction algorithm for EIT). This paper describes the unified approach to linear image reconstruction developed for GREIT. The framework for the linear reconstruction algorithm consists of (1) detailed finite element models of a representative adult and neonatal thorax, (2) consensus on the performance figures of merit for EIT image reconstruction and (3) a systematic approach to optimize a linear reconstruction matrix to desired performance measures. Consensus figures of merit, in order of importance, are (a) uniform amplitude response, (b) small and uniform position error, (c) small ringing artefacts, (d) uniform resolution, (e) limited shape deformation and (f) high resolution. Such figures of merit must be attained while maintaining small noise amplification and small sensitivity to electrode and boundary movement. This approach represents the consensus of a large and representative group of experts in EIT algorithm design and clinical applications for pulmonary monitoring. All software and data to implement and test the algorithm have been made available under an open source license which allows free research and commercial use.",
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    Adler, A, Arnold, JH, Bayford, R, Borsic, A, Brown, B, Dixon, P, Faes, TJC, Frerichs, I & Grychtol, B 2009, 'GREIT: a unified approach to 2D linear EIT reconstruction of lung images' Physiological Measurement, vol. 30, no. 6, pp. S35-S55. https://doi.org/10.1088/0967-3334/30/6/S03

    GREIT: a unified approach to 2D linear EIT reconstruction of lung images. / Adler, Andy; Arnold, John H.; Bayford, Richard; Borsic, Andrea; Brown, Brian; Dixon, Paul; Faes, Theo J.C.; Frerichs, Inéz; Grychtol, Bartłomiej.

    In: Physiological Measurement, Vol. 30, No. 6, 06.2009, p. S35-S55.

    Research output: Contribution to journalArticle

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    AU - Adler, Andy

    AU - Arnold, John H.

    AU - Bayford, Richard

    AU - Borsic, Andrea

    AU - Brown, Brian

    AU - Dixon, Paul

    AU - Faes, Theo J.C.

    AU - Frerichs, Inéz

    AU - Grychtol, Bartłomiej

    N1 - Strathprints' policy is to record up to 8 authors per publication, plus any additional authors based at the University of Strathclyde. More authors may be listed on the official publication than appear in the Strathprints' record.

    PY - 2009/6

    Y1 - 2009/6

    N2 - Electrical impedance tomography (EIT) is an attractive method for clinically monitoring patients during mechanical ventilation, because it can provide a non-invasive continuous image of pulmonary impedance which indicates the distribution of ventilation. However, most clinical and physiological research in lung EIT is done using older and proprietary algorithms; this is an obstacle to interpretation of EIT images because the reconstructed images are not well characterized. To address this issue, we develop a consensus linear reconstruction algorithm for lung EIT, called GREIT (Graz consensus Reconstruction algorithm for EIT). This paper describes the unified approach to linear image reconstruction developed for GREIT. The framework for the linear reconstruction algorithm consists of (1) detailed finite element models of a representative adult and neonatal thorax, (2) consensus on the performance figures of merit for EIT image reconstruction and (3) a systematic approach to optimize a linear reconstruction matrix to desired performance measures. Consensus figures of merit, in order of importance, are (a) uniform amplitude response, (b) small and uniform position error, (c) small ringing artefacts, (d) uniform resolution, (e) limited shape deformation and (f) high resolution. Such figures of merit must be attained while maintaining small noise amplification and small sensitivity to electrode and boundary movement. This approach represents the consensus of a large and representative group of experts in EIT algorithm design and clinical applications for pulmonary monitoring. All software and data to implement and test the algorithm have been made available under an open source license which allows free research and commercial use.

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    Adler A, Arnold JH, Bayford R, Borsic A, Brown B, Dixon P et al. GREIT: a unified approach to 2D linear EIT reconstruction of lung images. Physiological Measurement. 2009 Jun;30(6):S35-S55. https://doi.org/10.1088/0967-3334/30/6/S03