Toward Morphological Thoracic EIT: Major Signal Sources Correspond to Respective Organ Locations in CT

Ferrario, Damien; Grychtol, Bartłomiej; Adler, Andy; Solà, Josep; Böhm, Stephan H.; Bodenstein, Marc

doi:10.1109/tbme.2012.2209116

Cited by 46 publications

(44 citation statements)

References 23 publications

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“…Although the ROIs have equal sizes they might contain different amounts of lung tissue. Especially the amount of lung tissue in the ventral ROI might differ from the other ROIs [16,17]. The ROI analysis did not exclude the central (mediastinum, heart) region of the lung.…”

Section: Resultsmentioning

confidence: 99%

Determination of respiratory gas flow by electrical impedance tomography in an animal model of mechanical ventilation

et al. 2014

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BackgroundA recent method determines regional gas flow of the lung by electrical impedance tomography (EIT). The aim of this study is to show the applicability of this method in a porcine model of mechanical ventilation in healthy and diseased lungs. Our primary hypothesis is that global gas flow measured by EIT can be correlated with spirometry. Our secondary hypothesis is that regional analysis of respiratory gas flow delivers physiologically meaningful results.MethodsIn two sets of experiments n = 7 healthy pigs and n = 6 pigs before and after induction of lavage lung injury were investigated. EIT of the lung and spirometry were registered synchronously during ongoing mechanical ventilation. In-vivo aeration of the lung was analysed in four regions-of-interest (ROI) by EIT: 1) global, 2) ventral (non-dependent), 3) middle and 4) dorsal (dependent) ROI. Respiratory gas flow was calculated by the first derivative of the regional aeration curve. Four phases of the respiratory cycle were discriminated. They delivered peak and late inspiratory and expiratory gas flow (PIF, LIF, PEF, LEF) characterizing early or late inspiration or expiration.ResultsLinear regression analysis of EIT and spirometry in healthy pigs revealed a very good correlation measuring peak flow and a good correlation detecting late flow. PIFEIT = 0.702 · PIFspiro + 117.4, r2 = 0.809; PEFEIT = 0.690 · PEFspiro-124.2, r2 = 0.760; LIFEIT = 0.909 · LIFspiro + 27.32, r2 = 0.572 and LEFEIT = 0.858 · LEFspiro-10.94, r2 = 0.647. EIT derived absolute gas flow was generally smaller than data from spirometry. Regional gas flow was distributed heterogeneously during different phases of the respiratory cycle. But, the regional distribution of gas flow stayed stable during different ventilator settings. Moderate lung injury changed the regional pattern of gas flow.ConclusionsWe conclude that the presented method is able to determine global respiratory gas flow of the lung in different phases of the respiratory cycle. Additionally, it delivers meaningful insight into regional pulmonary characteristics, i.e. the regional ability of the lung to take up and to release air.

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Section: Resultsmentioning

confidence: 99%

Determination of respiratory gas flow by electrical impedance tomography in an animal model of mechanical ventilation

et al. 2014

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“…We also purposefully used a circular (cylindrical in 3D) forward model for all algorithms, as this is what assumes, although using accurate body shape (e.g. derived from CT data) is known to decrease artefacts and improve positional accuracy [28], [65]. The effects of using a circular model do not affect our analysis of tidal volume changes, since V T is a scalar not affected by the distribution of pixel values within the ROI used for its calculation.…”

Section: Discussionmentioning

confidence: 99%

“…Most such studies have not used anatomical models for EIT reconstruction (typically using circles on a pixel grid) making comparison with morphologically accurate images problematic. Also, the inherent low resolution of EIT creates large partial volume effects [28], while the scan rate of EIT is much higher (50 frames/s for some devices) than those of the reference radiological techniques. The most important drawback of such studies, however, is that anatomical validation is primarily sensitive to morphological accuracy, and thus less sensitive to the functional performance of EIT imaging.…”

Section: Introductionmentioning

confidence: 99%

Functional Validation and Comparison Framework for EIT Lung Imaging

et al. 2014

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IntroductionElectrical impedance tomography (EIT) is an emerging clinical tool for monitoring ventilation distribution in mechanically ventilated patients, for which many image reconstruction algorithms have been suggested. We propose an experimental framework to assess such algorithms with respect to their ability to correctly represent well-defined physiological changes. We defined a set of clinically relevant ventilation conditions and induced them experimentally in 8 pigs by controlling three ventilator settings (tidal volume, positive end-expiratory pressure and the fraction of inspired oxygen). In this way, large and discrete shifts in global and regional lung air content were elicited.MethodsWe use the framework to compare twelve 2D EIT reconstruction algorithms, including backprojection (the original and still most frequently used algorithm), GREIT (a more recent consensus algorithm for lung imaging), truncated singular value decomposition (TSVD), several variants of the one-step Gauss-Newton approach and two iterative algorithms. We consider the effects of using a 3D finite element model, assuming non-uniform background conductivity, noise modeling, reconstructing for electrode movement, total variation (TV) reconstruction, robust error norms, smoothing priors, and using difference vs. normalized difference data.Results and ConclusionsOur results indicate that, while variation in appearance of images reconstructed from the same data is not negligible, clinically relevant parameters do not vary considerably among the advanced algorithms. Among the analysed algorithms, several advanced algorithms perform well, while some others are significantly worse. Given its vintage and ad-hoc formulation backprojection works surprisingly well, supporting the validity of previous studies in lung EIT.

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“…Ferrario et al proposed an algorithm for automatic detection of heart and lung regions in the EIT images. The results were compared to the corresponding regions in CT images and a good match was found [10]. However, to assess the morphological correctness of the result, they defined precision as precision = EIT lung ∩ CT lung EIT lung (5) where EIT lung represents the regions of lung identified in EIT image and CT lung the regions of lung segmented in CT image.…”

Section: Discussionmentioning

confidence: 99%

Individual thorax geometry reduces position and size differences in reconstructed images of electrical impedance tomography

Zhao

Frerichs²,

Pulletz

et al. 2014

Journal of X-Ray Science and Technology

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BACKGROUND: Due to the ill-posed problem, the electrical impedance within the thorax cannot be exactly reconstructed. OBJECTIVE: The aim of our study was to prove that reconstruction with individual thorax geometry improved the quality of EIT (electrical impedance tomography) images. METHODS: Seven mechanically ventilated patients with acute respiratory distress syndrome were examined by EIT. The thorax contours were determined from routine computed tomography (CT) images based on automatic threshold filtering. EIT raw data was reconstructed offline with (1) back-projection with circular forward model; (2) GREIT reconstruction method with circular forward model and (3) GREIT with individual thorax geometry. The resulting EIT images were compared to rescaled CT images. The distance between the lung contour and the thorax contour was calculated for each method and the differences to that in CT were denoted as position differences. Shape differences was defined as the ratio of thorax (or lungs) size in EIT and that in rescaled CT. RESULTS: Method (3) has the smallest position differences (6.6 ± 2.8, 5.3 ± 3.3, 2.3 ± 1.4 in pixel, for each reconstruction method respectively; mean ± SD). The thorax and lungs sizes in the transformed CT images were 514 ± 73 and 177 ± 39. Shape differences of thorax were 1.81 ± 0.26, 1.81 ± 0.26, 1.10 ± 0.12 and that of lungs were 1.69 ± 0.45, 1.52 ± 0.45, 1.34 ± 0.35 for each method respectively. CONCLUSION: The reconstructed images using the GREIT method with individual thorax geometry were more realistic. Improvement of EIT image quality may foster the acceptance of EIT in routine clinical use.

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Toward Morphological Thoracic EIT: Major Signal Sources Correspond to Respective Organ Locations in CT

Cited by 46 publications

References 23 publications

Determination of respiratory gas flow by electrical impedance tomography in an animal model of mechanical ventilation

Determination of respiratory gas flow by electrical impedance tomography in an animal model of mechanical ventilation

Functional Validation and Comparison Framework for EIT Lung Imaging

Individual thorax geometry reduces position and size differences in reconstructed images of electrical impedance tomography

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