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Published ahead of print on December 23, 2003, doi:10.1164/rccm.200301-133OC
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American Journal of Respiratory and Critical Care Medicine Vol 169. pp. 791-800, (2004)
© 2004 American Thoracic Society

Original Article

Imbalances in Regional Lung Ventilation

A Validation Study on Electrical Impedance Tomography

Josué A. Victorino, João B. Borges, Valdelis N. Okamoto, Gustavo F. J. Matos, Mauro R. Tucci, Maria P. R. Caramez, Harki Tanaka, Fernando Suarez Sipmann, Durval C. B. Santos, Carmen S. V. Barbas, Carlos R. R. Carvalho and Marcelo B. P. Amato

Respiratory ICU, Hospital das Clínicas, Pulmonary Department; General ICU, Hospital das Clínicas, Emergency Clinics Division; Radiology Department, Hospital das Clínicas, University of São Paulo, São Paulo, Brazil; and Department of Intensive Care, Fundación Jiménez Díaz, Madrid, Spain

Correspondence and requests for reprints should be addressed to Marcelo Amato, M.D., Laboratório de Pneumologia, LIM09, Faculdade de Medicina da USP, Av Dr Arnaldo, 455 sala 2206 (2nd floor), CEP: 01246–903, São Paulo, SP, Brazil. E-mail: amato{at}unisys.com.br


   ABSTRACT
TOP
 ABSTRACT
METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Imbalances in regional lung ventilation, with gravity-dependent collapse and overdistention of nondependent zones, are likely associated to ventilator-induced lung injury. Electric impedance tomography is a new imaging technique that is potentially capable of monitoring those imbalances. The aim of this study was to validate electrical impedance tomography measurements of ventilation distribution, by comparison with dynamic computerized tomography in a heterogeneous population of critically ill patients under mechanical ventilation. Multiple scans with both devices were collected during slow-inflation breaths. Six repeated breaths were monitored by impedance tomography, showing acceptable reproducibility. We observed acceptable agreement between both technologies in detecting right–left ventilation imbalances (bias = 0% and limits of agreement = -10 to +10%). Relative distribution of ventilation into regions or layers representing one-fourth of the thoracic section could also be assessed with good precision. Depending on electrode positioning, impedance tomography slightly overestimated ventilation imbalances along gravitational axis. Ventilation was gravitationally dependent in all patients, with some transient blockages in dependent regions synchronously detected by both scanning techniques. Among variables derived from computerized tomography, changes in absolute air content best explained the integral of impedance changes inside regions of interest (r2 >= 0.92). Impedance tomography can reliably assess ventilation distribution during mechanical ventilation.

Key Words: artificial respiration • physiologic monitoring • validation studies • adult respiratory distress syndrome • respiratory insufficiency

Patients under artificial ventilation often present heterogeneous lung aeration, with inadequate distribution of VT (1, 2). Prevalent conditions such as increased lung weight (3), lung compression by the heart (4, 5), abnormalities of chest wall (6, 7), and impaired surfactant function (8) promote not only a collapse of dependent lung zones, but also hyperdistention of nondependent zones (9–11). Such imbalances create zones of stress concentration inside the parenchyma, with increased risks for ventilator-induced lung injury (12).

Although global indexes of lung function like blood gases (13, 14), lung mechanics (15, 16), and plethysmography (17) have been used to track those ventilatory imbalances, they provide limited information. Imaging techniques such as magnetic resonance (18) or computerized tomography (CT) can provide better information about lung heterogeneities (14, 19–21), but they lack the dynamic features and bedside monitoring capabilities needed for intensive care.

Electrical impedance tomography (EIT) has emerged as a new imaging tool for bedside use (22–25). It is a noninvasive and radiation-free technique based on the measurement of electric potentials at the chest wall surface. Within a particular cross-sectional plane, harmless electrical currents are driven across the thorax in a rotating pattern, generating a potential gradient at the surface, which is then transformed into a two-dimensional image of the electric impedance distribution within the thorax.

Recent experimental studies have suggested that EIT images are very sensitive to regional changes in lung aeration (26–32). The dynamic behavior and the qualitative information extracted from EIT images look similar to that reported in dynamic CT studies (2, 33, 34) or in ventilation scintigraphy (31, 35). Its potential use as an online positive end-expiratory pressure titration tool has also been proposed, as EIT apparently provides reliable information about the recruitment/derecruitment of dependent lung regions (27, 28, 36, 37) and thus about the associated risk of ventilator-induced lung injury.

However, the poor spatial resolution of current EIT devices casts doubts on the promises mentioned previously here. As EIT does not keep perfect anatomic correspondence with CT images, we do not know yet whether we can translate the knowledge acquired from CT studies to the EIT universe. Although a recent animal study (38) suggested a good linear relationship between regional impedance changes and density changes (measured in Hounsfield units), we do not know how to use best the quantitative pixel information provided by EIT or how reliable it is in critically ill patients with acute lung injury.

We designed this study to answer the questions mentioned previously here and to test specifically whether EIT can consistently quantify ventilation imbalances caused by gravitational forces on the injured lung. We also tested whether some minimal anatomic/functional agreement with dynamic CT images can be obtained in critically ill patients. Part of this investigation has been previously reported in the form of abstracts (26, 39).


   METHODS
TOP
 ABSTRACT
 METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Ten adult patients under mechanical ventilation were recruited (Table 1) after obtaining informed consent from patients' relatives.


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  		TABLE 1. Patient characteristics at entry

 
Experimental Protocol
Dynamic sequences of EIT and CT scans, repeatedly at the same thoracic plane, during a slow-flow inflation maneuver were compared in supine patients. It was impossible to obtain simultaneous EIT and CT images because of excessive electromagnetic interference. Therefore, we performed three sets of slow inflations in the intensive care unit, monitored by EIT (DAS-01P, Sheffield, UK), followed by one set monitored by CT (GE HighSpeed, Milwaukee, WI). Back to the intensive care unit, three additional slow inflations were again monitored by EIT. By repeating EIT acquisition before and after patient transport to the CT room, we fully tested EIT reproducibility.

To start lung inflations from same approximate resting volume, lung history was homogenized before each one of the seven slow inflations by applying continuous positive airway pressure of 40 cm H2O, lasting 20 seconds, followed by disconnection against atmosphere for 15 seconds.

The slow inflation was initiated by directing a constant flow generator (1 L/minute) toward the endotracheal tube through a three-way stopcock, linked in series to a proximal pressure/flow sensor. Data were sampled at 100 Hz. Inflation stopped at 45 cm H2O, enough to obtain approximately 100 EIT scans (0.8 image/second) or 45 CT scans (0.3 image/second).

We always started the slow inflation 1.0 to 1.5 seconds before starting the first EIT or CT scan. Hardware scanning time was 1.0 second for both devices. Pressure/flow signals were continuously stored (100 Hz sampling) in a personal computer with its internal clock previously synchronized with EIT and CT machine clocks.

Electrode Positioning
For EIT measurements, 16 standard electrocardiograph electrodes were placed around the thorax at the transverse plane crossing the fifth intercostal space at midclavicular line. To check potential interferences of positioning of electrodes on image reconstruction (Figure 1), two different electrode-positioning arrangements were tested, exactly at the same transverse plane: (1) standard positioning—equally spaced—the distance between two adjacent electrodes kept constant along thoracic perimeter; the first electrode was always placed at sternum, and (2) test positioning—electrodes 5 (left armpit) and 13 (right armpit; Figure 1) were displaced upward (3 cm), closer to anterior axillary line. Interelectrode distances were evenly shortened on anterior thoracic surface and evenly expanded on posterior surface.



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  		Figure 1. Sketch of thoracic plane and theoretic effects of different electrode positioning. During electrical impedance tomography (EIT) imaging, impedance changes occurring in real trapezoid domain (left) are projected over a circular EIT domain (right), deforming lung areas. The perimeter of the EIT circle necessarily corresponds to the skin with electrodes. By using standard electrode positioning (top), midelectrode 5 is frequently placed over the skin close the posterior lung (LLL illustrates an atelectatic left lower lobe) and not at midlung height. Because the EIT imaging algorithm assumes that electrode 5 is at midthoracic height, midway between electrodes 1 and 9, there is some shrinking of nondependent lung representation, with expansion of LLL. This is because EIT back projection assumes that every lung tissue above electrode 5 must be projected over the anterior part of the circular EIT representation, whereas every tissue below electrode 5 (LLL) has to occupy the whole posterior part of the circular EIT representation. The test positioning used in this study is illustrated at the bottom. Electrode 5 is displaced ventrally (3 cm), closer to midlung height. Electrodes 1–5 have now a shorter interelectrode distance than electrodes 5–9 (where subcutaneous tissue is abundant). We hypothesized that such positioning would avoid the overrepresentation of LLL.

 
Electrode positioning for the first scan was randomly selected. The second scan was performed under the alternative positioning. For the third, no electrode replacements were made. The previous set of electrodes was completely removed whenever we changed electrode positioning or before transport to CT.

EIT Scans
The EIT device injected an alternating current (51 kHz, 2.1 root mean square [RMS]) between sequential pairs of adjacent electrodes. During each injection pattern, voltage differences between adjacent pairs of noninjecting electrodes were collected. The first scanning cycle worked as reference voltage set, with all image pixels (pixel = minimal element for image reconstruction) assigned to zero. Subsequently, new scanning cycles were collected every 1.2 seconds, each providing information to reconstruct one new relative image. By using a long scanning time (1 second), impedance changes were mostly related to changes in lung aeration, with negligible effects of perfusion waves (40–45). Each image represented the relative change in impedance distribution within the transverse section of the chest, from the first scan (right after starting slow inflation) to current scan. Images were reconstructed through a mathematic algorithm called back projection (46, 47), in which pixel values were expressed as percentage changes of local impedance, not providing any information about absolute values of tissue impedance. In its formulation, the algorithm assumes that voltages were collected from a nearly rounded section of the body, projecting its estimates of impedance changes over a 32 x 32 circular matrix. Customized software automatically extracted pixel information from regions of interest (ROIs) correspondent to those assigned on CT images (Figure 2).



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  		Figure 2. Schematic regions of interest (ROIs) on computerized tomography (CT) and EIT. Each of the 12 ROIs embraced a portion of the chest wall plus part of the lung. On EIT images, the portions were selected automatically by special software splitting the original circle with 788 pixels into subsets shown. On the CT image, the skin border was manually designed, forming the outer boundary of a cross-section of the thorax with approximately 140,000 pixels. Subsequently, uppermost and lowest pixels of contour were taken as references, and four evenly spaced layers, each one corresponding to one fourth of anteroposterior thoracic diameter, were drawn. Similarly, the crossing between skin contour and the horizontal line at midthoracic height defined references to split the thorax in its middle (left and right halves). URQ = upper right quadrant; ULQ = upper left quadrant; LRQ = lower right quadrant; LLQ = lower left quadrant.

 
CT Scans
After a new homogenizing maneuver, sequential CT slices (every 3 seconds, scanning time = 1 second) were taken during slow inflation without interruption and repeatedly at the same cross-sectional plane defined for EIT. The collimation was set at 10 mm.

From each image, we obtained frequency distributions of CT numbers corresponding to manually determined ROIs, according to the topography shown in Figure 2. A customized software converted regional CT histograms into three derived variables: mean density, gas/tissue ratio, and air content, according to published formulas (48, 49).

VT Volume Distribution and Statistical Analysis
Retrospectively, we looked at airway flow tracings, identifying the start of slow inflation (error of ± 0.02 seconds). Using synchronized time information, we referenced EIT or CT scans relative to this time origin. Off-line, we synchronized EIT and CT acquisitions by linearly interpolating EIT image data to the same points in time where we had CT scans, getting 30–45 synchronized images per inflation. Because we used constant-flow generator, lungs were inflated up to equivalent volumes for all matched images.

The relationship between CT and EIT variables was addressed by multiple linear regression. By taking only the first and the last matched images, we calculated the relative distribution of VT across the ROIs. For CT, the percentage of tidal ventilation directed toward a particular ROI was calculated as the increment in air content for that ROI divided by the air content increment for the entire slice. For EIT, we took the last image and calculated the integral of pixel value over that corresponding ROI (50, 51) divided by integral of pixel value over the whole slice. Based on these estimates—presented as dimensionless numbers or percentages—we tested EIT reproducibility (comparing EIT estimates before versus after CT scan) and EIT versus CT agreement according to the principles proposed by Bland and Altman (52). Additional details are provided in the online supplement.


   RESULTS
TOP
 ABSTRACT
 METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Stability of Lung Mechanics along the Study
Cross correlations among the seven pressure–time curves obtained for each patient were calculated. Because patient 2 presented at least one correlation coefficient of less than 0.9, interpreted as a signal of poor stability of lung mechanics across the measurements, he was excluded from subsequent analysis. Although discarded, the dynamics of lung inflation in this case was illustrative of the spatial resolution of EIT (being presented in the animations 1 and 2 in the online supplement).

Reproducibility
Reproducibility in EIT estimates of VT distribution was assessed by calculating the within-subject SD between repeated measures (53). For each ROI, we calculated within-subject SD between repeated measures and bias observed between two consecutive measurements, always under the same electrode positioning. Considering all ROIs and both electrode-positioning arrangements together, we observed a global within-subject SD between repeated measures of 4.9% when electrodes were kept in place and 7.4% when we replaced electrode array after CT (separately considered: 7.0% for standard and 7.7% for test positioning). This demonstrates that replacement of electrodes increased random errors in our measurements. The bias was less than 1% for all situations. All of these results were below our a priori reproducibility cutoff of 9%.

Agreement
Agreement in estimates of VT distribution according to EIT versus CT is presented in Figure 3. Even smaller ROIs presented acceptable agreement (i.e., sample limits of agreement did not exceed the boundaries established a priori) for either electrode positioning. Agreement was better for right–left imbalances than for upper–lower imbalances in ventilation. The worst agreement was observed in layer 1, with standard positioning (bias = +9.4% and sample limits of agreement = -6.4 to 25%).



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  		Figure 3. Bland-Altman plots of the differences in regional distribution of VT estimated by EIT and CT (for brevity, only six representative lung regions, and only the standard electrode positioning is presented). The overall span of Y axis represents our a priori limits of agreement. (Dotted lines) Limits of agreement of the observed sample. (Gray line) Mean of observed differences. (Downward triangles) Measurements taken before CT exam. (Upward triangles) Measurements taken after CT exam. (Circles) The average difference. (Gray square) Patient 9, presenting acute cardiogenic pulmonary edema soon after CT scan, resulting in the largest disagreement.

 
Translating this agreement into images, Figure 4 exemplifies typical EIT images—contrasted with synchronized CT images.




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  		Figure 4. EIT and CT images obtained at start (left) and at end (right) of slow-inflation maneuver in patients 8 (A) and 3 (B). All CT images were obtained at the same thoracic plane (fifth intercostal space), which coincides with the plane of electrodes. The relative EIT images express only variations in impedance. Note that the back-projection algorithm projects impedance changes (bright colors represent increased impedance) onto the same quadrants suffering higher aeration in CT images (regions getting darker shades of gray). Atelectatic zones, the mediastinum, and the pleural effusion zones remain silent. F.R.C.= functional residual capacity.

 
Relative Distribution of VT according to EIT and CT
Figure 5 shows the distribution of ventilation according to the horizontal and vertical axes in CT and EIT images. When considering potential imbalances between right/left fields, EIT and CT exhibited comparable estimates for regional ventilation (bias = 0% and limits of agreement = -10 to 10%, p = 0.31; Figure 5, left). Pooled measurements across patients suggested a rather homogeneous (approximately equal to 1:1) distribution of ventilation between right/left fields. However, there were some outliers, exemplified by patient 8 (Figure 4A), who had a solid mass entirely blocking the right lung and who obtained an estimate of ventilation toward the right field = 2% in CT analysis versus -3% in EIT analysis. CT and EIT similarly detected all outliers.



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  		Figure 5. Box plot representing distributions of VT estimated by EIT (white boxes) and CT (gray boxes) in nine patients when using standard electrode positioning. Boxes indicate 25% and 75% percentiles, with the median line inside. Error bars represent 5% and 95% percentiles. The left panel points out ventilation imbalances between left and right thoracic areas. The right panel points out imbalances between upper and lower parts of the thorax. *p = 0.04 using asymptotic approximation for Wilcoxon Signed-rank test. {dagger}p = 0.02 using asymptotic approximation for Wilcoxon Signed-rank test.

 
Likewise, both techniques detected equivalent imbalances when the upper and lower parts of the thorax were considered (upper/lower ratio = 82%/18% and 75%/25% for EIT and CT, respectively), also with a good case-by-case match. The overall inhomogeneity between the upper/lower fields was marginally larger with EIT (considering the standard electrode positioning) than with CT (p = 0.04).

Similarly to CT, EIT detected a large vertical gradient of regional ventilation across the four superimposed layers in all patients. The standard positioning of electrodes caused a slight overestimation of regional ventilation to layer 1, underestimating the ventilation to layer 3. The test positioning partially corrected this distortion (Figure 6).



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  		Figure 6. Box plot representing distributions of VT estimated by EIT (white boxes) and CT (gray boxes). Electrode positioning was standard in the top and test in the bottom. Boxes represent 25 and 75 percentiles, with the median line inside. Error bars represent 5 and 95 percentiles. There is an overall trend for progressively lower ventilations from ROI 1 to ROI 4, either in EIT (p = 0.001, Friedman test) or in CT (p = 0.003). The test positioning of electrodes resulted in better match with CT. Significant differences between CT and EIT estimates were detected only for the standard positioning. *p = 0.018 using asymptotic approximation for Wilcoxon Signed-rank test; {dagger}p = 0.028 using asymptotic approximation for Wilcoxon signed-rank test.

 
Multiple regression analysis (Figure 7) further checked two potential errors in EIT analysis: (1) image distortions and (2) a lack of linear relationship between electrical properties versus density (X-ray attenuation) of tissues. We assumed CT based variables as "gold standard" (independent variables), intentionally plotting the entire data sequence for all regions together, in the same X–Y plane. We reasoned that both potential errors were expected to compromise the overall coefficient of determination. This is because EIT image distortions tend to produce plots with different slopes for each region. For instance, consider an EIT slice with adjacent regions, A and B. Consider also the x axis representing true changes in air content for regions A or B (measured by CT) versus the y axis representing the measured changes in impedance. If part of a true impedance change in region A was wrongly projected over region B (characterizing an image distortion), the slope of plot A would decrease, whereas the slope of plot B would increase in the same system of coordinates. The result would be a poor r2.



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  		Figure 7. Scattered plots illustrating adjusted multiple regression for local impedance changes during slow inflation (standard electrode positioning) when projected over synchronized changes in CT images. Dependent variable in each plot represents integral of pixel values over a certain ROI in EIT, calculated for each image in the inflation sequence. Independent variables are, respectively, regional air content (top), regional gas/tissue ratio (middle), and regional mean density (bottom), all calculated from corresponding ROIs in CT images. Plots for all ROIs, patients, and trials (pre-or post-CT) are superposed, with approximately 9,600 data points per graph. r2 represents within-subject coefficient of determination.

 
Figure 7 shows that air content in CT presented best coefficient of determination (r2 = 0.92, standard positioning; r2 = 0.93, test positioning, data not shown) to predict regional impedance changes. Linear plots for each region were consistently observed, with very similar slopes across regions and patients. The same was not true for the relationships with the CT mean density (r2 = 0.57) or with the gas/tissue ratio (r2 = 0.56), where different slopes for each region compromised the overall coefficient of determination.

A common phenomenon observed in our patients was illustrated in Figure 8. Dependent lung zones presented transient blockage of regional ventilation at the beginning of slow inflation, although there was no visible collapse on CT at the start of slow inflation. After varied periods of time, this blockage was overcome, and the slope of impedance changes along time line suddenly increased in dependent zones, synchronously with the sudden increase in air content in dependent zones of CT slices.



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  		Figure 8. Temporal sequence of EIT and CT estimates during slow lung inflation. The arrow indicates the moment when the lower half of the thorax started to ventilate, almost 30 seconds later than the upper thorax.

 

   DISCUSSION
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
The major findings in this study can be summarized as follows: (1) EIT images from patients under controlled mechanical ventilation were reproducible and presented good agreement to dynamic CT scanning. (2) Electrode array replacement slightly deteriorated the reproducibility of EIT measurements and the interelectrode spacing within the array affected the agreement with CT. (3) Although EIT estimates of right/left imbalances in regional lung ventilation were more precise (and less dependent on interelectrode spacing), gravity-related imbalances of regional lung ventilation could be reliably assessed, even for layers corresponding to one-fourth of anteroposterior thoracic distance; and (4) Regional impedance changes in the EIT slice were best explained by the corresponding changes in air content detected in the CT slice (explaining 92–93% of its variance). Other CT-derived variables, such as regional X-ray mean density or regional gas–tissue ratio, did not parallel regional changes in impedance as consistently.

An important methodologic aspect of this study is linked to the results discussed previously here: We used the integral of pixel values over each ROI—instead of simple pixel average—to represent the regional changes in impedance. There are several advantages with this approach. First, some bench tests using back-projection reconstruction have demonstrated the superior consistency of this parameter to quantify impedance perturbations all over the image slice—independently of its radial position (50, 51). Second, it allows the estimation of the percentage of VT directed toward a particular ROI by simply calculating a normalized ratio (i.e., the integral over the ROI divided by the integral over the whole slice). This approach obviously decreases the between-patient variability. Finally, there was a strong rationale supporting this approach, particularly for our study, as explained later here. Because clear anatomic marks were absent in EIT images, we adopted a reproducible procedure for ROI delineation, independently of investigator or individual anatomy: We embraced structures suffering aeration together with structures that were not (e.g., the chest wall; Figure 2). Thus, the amount of nonexpandable tissue (with fixed localized impedance) necessarily attenuated the mean impedance change inside each ROI—in the same manner that they attenuated mean density changes on CT. However, the same attenuation is not expected to occur in the integral of pixel values. Varied amounts of compact tissue do not affect calculations for air content in CT analysis (because their calculations are not based on average values, but ultimately on the sum of pixel values), and similar results must be expected for the integral of EIT pixel values.

When estimating air content for each pixel in CT, we calculate the absolute amount of air contained in the voxel (voxel = minimum volume element to construct the image). The idea that the sum of these estimates produces a reliable number expressing air content inside the whole slice is intuitive. However, the understanding of how pixel values in EIT—expressed as percent changes in impedance—can be summed to estimate global changes in air content is not trivial. Recently, Nopp and colleagues (45) provided a theoretic framework supporting this convenient relationship, which was explored in this study as well as in a recent publication (54). Using an appropriate mathematic model for the alveolar structure and boundary conditions—like an almost invariant interstitial space along inspiration (i.e., constant tissue volume within the slice), projected over the same image pixels, and suffering moderate impedance changes (less than 100%)—the author demonstrated that each percentage change in pixel impedance should parallel absolute increments in air content for that corresponding parenchymal region, no matter the initial value for absolute resistivity in that region.

It follows that the integral of pixel value in EIT should parallel changes in air content, as calculated in CT slices. However, the same rationale does not stand for gas/tissue ratio (%) or CT mean densities (Hounsfield units), as suggested previously here: Different amounts of compact tissue across different ROIs are expected to cause a poor correlation between EIT and these two latter CT variables. Figure 7 corroborates this hypothesis.

This stronger association with CT air content was a key finding in our study. Recently, Frerichs and colleagues (38) reported acceptable correlations between local impedance changes versus local changes in CT mean densities (in Hounsfield units). However, even using a less noisy EIT device in a controlled environment (they used normal pigs with convenient rounded thoracic geometry instead of patients with diseased lungs and trapezoid thoracic shapes), they reported lower coefficients of determination (ranging from 0.56 to 0.86). Methodologic differences such as their subjective ROI demarcations and the use of pooled regression, instead of a more appropriate within-subject regression (55), make any comparison difficult. However, altogether, those findings suggest that the choice for better parameters quantifying aeration in CT or EIT is essential for fair comparisons between both technologies or also to extract the most reliable information from EIT.

Limitations of This Study
Unlike the gold standard two-dimensional CT slice, with a homogenous thickness of 1 cm, EIT slice represents a less precise thickness of tissue, which is radius dependent (56). Part of the electrical current commonly flows through planes above and below the electrode plane, and the central part of the image is especially susceptible to these out of plane influences, theoretically up to 10 cm above or below. Therefore, an ideal comparison study should examine EIT against a thicker CT slicing (10–20 cm), requiring more radiation and multislice tomography.

Nevertheless, the high within-subject coefficient of determination obtained with our dynamic single-slice approach (r2 >= 0.92) suggests that even CT multislicing might not provide much additional information. One possible explanation for this finding is that in spite of theoretic assumptions, the amount of out of plane current may be negligible in the human thorax. Another important consideration is that the lung may be relatively homogeneous along the craniocaudal axis, behaving like a liquid body in patients under mechanical ventilation (57). By assuming this isogravitational behavior, out of plane changes would be similar to in-plane ones, minimally affecting our analysis (58).

Another limitation of our study might be related to the fact that EIT and CT acquisitions were not simultaneous and that the lung might behave slightly differently during each slow inflation (59). We tried to minimize this problem, contemplating procedures like the exclusion of nonreproducible pressure–time tracings, the averaging of two EIT acquisitions (before and after CT) for agreement analysis, and the use of intense homogenizing maneuvers before each slow inflation. Nevertheless, this intrinsic limitation eventually precluded us from obtaining better agreement with CT slices.

Our final concern is that the presented results are only valid for the specific device tested here and for ROIs not smaller than one-fourth of the thoracic cross-sectional area. These issues are linked, as technologic improvements such as new image reconstruction algorithms (60–67), larger number of electrodes (68), or higher precision in current injection or voltage readings could all decrease errors in EIT imaging, improving spatial resolution (69, 70). In fact, our reproducibility analysis suggests that we are close to the resolution limits of the tested device and that any further decrease in ROI size would impair reproducibility. As shown in this study, small differences in interelectrode spacing along the thoracic perimeter can have an impact on EIT analysis (Figure 6). Better electrode-array handling (71) and new mathematic formulations to take into account thoracic asymmetries are needed for the next years.

Implications of Current Data
Despite the limitations cited previously here, we think that the reported performance of EIT was good enough for certain clinical applications, especially bedside adjustments of mechanical ventilation with immediate feedback. A similar EIT device could easily detect selective intubation, large pneumothorax, or lobar atelectasis. Additionally, as already reported by our group and others, subtle changes in the positive end-expiratory pressure level can produce large imbalances in regional ventilation along the gravity axis, usually by the same order of magnitude observed in this study (27, 36).

Despite the low spatial resolution of current EIT devices, the high temporal resolution of EIT looks promising. In our study, technical limitations forced us to use slow-motion inflation of the lung, which in turn allowed us to detect transient and usually imperceptible phenomena occurring during normal tidal breaths. For instance, dependent zones in most patients presented complete blockage of ventilation during significant part of inspiration (Figure 8). Suddenly, 20–30 seconds later, some regional ventilation could be precisely and simultaneously detected by EIT and CT—without detectable perturbation in simultaneous pressure–time tracings. The clinical relevance of such "inflation delays" is a matter for future studies, but faster temporal resolutions in new EIT devices would allow us to monitor such phenomena without any especial maneuver. In the context of evidences suggesting deleterious effects of tidal recruitment (72, 73), such sensitive detection at bedside is encouraging (26).

In conclusion, even at its current stage of development, EIT can reliably assess imbalances in distribution of VT in critically ill patients. When comparing regional ventilation across different thoracic regions, the quantitative information provided by EIT carries good proportionality to changes in air content—as calculated by dynamic CT scanning—but not with CT gas/tissue ratio or CT mean densities.


   Acknowledgments
 
The authors thank the EIT Study Group (especially Professor Raul Gonzalez and the team of the Polytechnic Institute and Dr. Joyce Bevilacqua from the Applied Mathematics Institute, University of São Paulo) for their valuable input, criticisms, and discussions during the experiments and data analysis.


   FOOTNOTES
 
Supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo.

This article has an online supplement, which is accessible from this issue's table of contents online at www.atsjournals.org

Conflict of Interest Statement: J.A.V. has no declared conflict of interest; J.B.B. has no declared conflict of interest; V.N.O. has no declared conflict of interest; G.F.J.M. has no declared conflict of interest; M.R.T. has no declared conflict of interest; M.P.R.C. has no declared conflict of interest; H.T. has no declared conflict of interest; F.S.S. has no declared conflict of interest; D.C.B.S. has no declared conflict of interest; C.S.V.B. has no declared conflict of interest; C.R.C.C. has no declared conflict of interest; M.P.B.A. has no declared conflict of interest.

Received in original form January 30, 2003; accepted in final form December 18, 2003


   REFERENCES
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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