imbalances in regional lung ventilation: a validation study on electrical impedance tomography
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IMBALANCES IN REGIONAL LUNG VENTILATION: A VALIDATION
STUDY ON ELECTRICAL IMPEDANCE TOMOGRAPHY
SUBJECT CATEGORY (DESCRIPTOR NUMBER): 2 (ALTERNATIVES: 145, 14, 9)WORD COUNT: 3622
AUTHORS:
VICTORINO, JOSUÉ A. 1
BORGES, JOÃO B. 1
OKAMOTO, VALDELIS N.1
MATOS, GUSTAVO F. J. 1
TUCCI, MAURO R. 1
CARAMEZ, MARIA P. R. 2
TANAKA, HARKI 1
SUAREZ SIPMANN, FERNANDO 3
SANTOS, DURVAL C. B. 4
BARBAS, CARMEN S. V. 1
CARVALHO, C. R. R. 1
AMATO, MARCELO B. P. 1
1. Respiratory ICU – Hospital das Clínicas – Pulmonary Department – University of São Paulo, Brazil
2. General ICU – Hospital das Clínicas – Emergency Clinics Division – University of São Paulo, Brazil
3. Department of Intensive Care. Fundación Jiménez Díaz, Madrid, Spain
4. Radiology Department – Hospital das Clínicas – University of São Paulo, Brazil
This article has an online data supplement, which is accessible from this issue's table of content online at www.atsjournals.org
FUNDED: FAPESP - Fundação de Amparo à Pesquisa do Estado de São Paulo (without any conflict of interest in the study outcome)
CORRESPONDENCE SHOULD BE ADDRESSED TO:
MARCELO AMATO, M.D.,Laboratório de Pneumologia - LIM09 Faculdade de Medicina da USPAv Dr Arnaldo, 455 sala 2206 (2nd floor)CEP: 01246-903 São Paulo, SP, BrazilEmail: amato@unisys.com.brPhone: 55-11-30667361 / FAX: 55-11-30612492
AJRCCM Articles in Press. Published on December 23, 2003 as doi:10.1164/rccm.200301-133OC
Copyright (C) 2003 by the American Thoracic Society.
1
ABSTRACT (WORD COUNT: 200)
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 potentially capable of
monitoring those imbalances. The aim of this study was to validate EIT 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). Conclusion: impedance tomography can reliably assess ventilation
distribution during mechanical ventilation.
KEY WORDS: Artificial Respiration; Physiologic Monitoring; Validation Studies; Adult
Respiratory Distress Syndrome; Respiratory Insufficiency.
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Introduction (word count 446)
Patients under artificial ventilation often present heterogeneous lung aeration, with
inadequate distribution of tidal volume (1, 2). Prevalent conditions like 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 collapse of dependent lung zones, but
also hyperdistention of non-dependent 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 like 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
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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 on-line
PEEP titration tool has also been proposed, since 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 above. As EIT does not keep perfect anatomical 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 best use the quantitative pixel information
provided by EIT, nor how reliable it is in critically ill patients with acute lung injury.
We designed the present study to answer the questions above, and to test specifically if
EIT can consistently quantify ventilation imbalances caused by gravitational forces on
the injured lung. We also tested if some minimal anatomical /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).
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Methods: (word count = 924)
Ten adult patients under mechanical ventilation were recruited (table 1), after obtaining
informed consent from patients’ relatives.
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 due to excessive electromagnetic interference.
Therefore, we performed three sets of slow-inflations in the ICU, monitored by EIT
(DAS-01P, Sheffield, UK), followed by one set monitored by CT (GE HighSpeed,
Milwalkee, USA). Back to ICU, 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.
In order to start lung inflations from same approximate resting volume, lung history was
homogenized before each one of the seven slow inflations, by applying CPAP of
40 cmH2O, lasting 20 seconds, followed by disconnection against atmosphere for 15
seconds.
The slow-inflation was initiated by directing a constant flow generator (1 L/min) towards
the endotracheal tube through a three-way stopcock, linked in series to a proximal
pressure/flow sensor. Data was sampled at 100 Hertz. Inflation stopped at 45 cmH2O,
enough to obtain approximately 100 EIT scans (0.8 image/second) or 45 CT scans (0.3
image/second).
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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 Hertz 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 5th 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:
a) standard positioning – equally spaced - the distance between two adjacent
electrodes kept constant along thoracic perimeter. The first electrode was always
placed at sternum.
b) test positioning – electrodes 5 (left armpit) and 13 (right armpit – figure 1) were
displaced upwards (3 cm), closer to anterior axillary line. Inter-electrode distances
were evenly shortened on anterior thoracic surface, and evenly expanded on
posterior surface.
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
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The EIT device injected an alternating current (51 kHz, 2.1 RMS) between sequential
pairs of adjacent electrodes. During each injection pattern, voltage differences between
adjacent pairs of non-injecting 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-second, each one providing information to reconstruct one new relative image.
By using 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 mathematical algorithm called back-projection (46, 47), in
which pixel values were expressed as percent 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. A
customized software automatically extracted pixel information from regions of interest
(ROIs) correspondent to those assigned on CT images (figure 2).
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 regions of interest, according to the topography shown in figure 2.
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A customized software converted regional CT histograms into 3 derived variables:
mean-density, gas/tissue ratio and air-content, according to published formulas (48, 49).
Tidal volume distribution and statistical analysis
Retrospectively, we looked at airway flow tracings, identifying the start of slow-inflation
(error of ± 0.02 s). 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. Since 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 tidal volume across the ROIs. For CT, the percent of tidal
ventilation directed towards a particular ROI was calculated as the increment in air-
content for that ROI, divided by air-content increment for the whole 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 vs. after CT scan), and EIT vs. CT
agreement, according to the principles proposed by Bland and Altman (52).
Additional details are provided in the online data supplement.
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RESULTS
Stability of lung mechanics along the study
Cross correlations among the 7 pressure-time curves obtained for each patient were
calculated. Since patient #2 presented at least one correlation coefficient < 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 data supplement).
Reproducibility
Reproducibility in EIT estimates of tidal volume distribution was assessed by calculating
the within-subject-standard-deviation-between-repeated-measures (SW) (53). For each
ROI, we calculated SW and bias observed between two consecutive measurements,
always under the same electrode positioning. Considering all ROIs and both electrode-
positioning arrangements together, we observed global Sw 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 these results were below our a priori reproducibility cutoff of 9%.
Agreement
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Agreement in estimates of tidal volume distribution according to EIT vs. 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%).
Translating this agreement into images, figures 4 and 5 exemplify typical EIT images -
contrasted with synchronized CT images.
Relative distribution of tidal volume according to EIT and CT
Figure 6 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 6-left). Pooled measurements
across patients suggested a rather homogeneous (≅ 1:1) distribution of ventilation
between right/left fields. However, there were some outliers, exemplified by patient #8
(figure 4), who had a solid mass entirely blocking the right lung, and who obtained an
estimate of ventilation towards the right field = 2% in CT analysis, versus –3% in EIT
analysis. CT and EIT similarly detected all outliers.
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
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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 4
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 7).
Multiple regression analysis (figure 8) further checked two potential errors in EIT
analysis: (a) image distortions, and (b) lack of linear relationship between electrical
properties vs. density (X-ray attenuation) of tissues. We assumed CT based variables as
“gold standard” (independent variables), intentionally plotting the whole 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 1.
Figure 8 shows that air-content in CT presented best coefficient of determination (R2 =
0.92, standard positioning; R2 = 0.93, test positioning, 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 CT mean-density (R2 = 0.57) or with 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 9. 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
1 See complete footnote at page 29.
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changes along time line suddenly increased in dependent zones, synchronously with the
sudden increase in air-content in dependent zones of CT slices.
DISCUSSION
The major findings in this study can be summarized as follows: a) EIT images from
patients under controlled mechanical ventilation were reproducible and presented good
agreement to dynamic CT scanning; b) Electrode array replacement slightly deteriorated
the reproducibility of EIT measurements, and the inter-electrode spacing within the array
affected the agreement with CT; c) Although EIT estimates of right/left imbalances in
regional lung ventilation were more precise (and less dependent on inter-electrode
spacing), gravity related imbalances of regional lung ventilation could be reliably
assessed, even for layers corresponding to one fourth of anteroposterior thoracic
distance, and d) 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, like regional X-ray mean-density or regional
gas-tissue ratio, did not parallel regional changes in impedance as consistently.
An important methodological aspect of this study is linked to the results above: 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
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perturbations all over the image slice – independently of its radial position (50, 51).
Secondly, it allows the estimation of the percentage of tidal volume directed towards 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. And finally, there was a strong rationale supporting this
approach, particularly for our study, as explained bellow. Because clear anatomical
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 non-expandable 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 (since 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 up to
estimate global changes in air-content is not trivial. Recently, Nopp et al. (45) provided a
theoretical framework supporting this convenient relationship, which was explored in this
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study as well as in a recent publication (54). Using an appropriate mathematical 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 (< 100%) - the author
demonstrated that each percent 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 above: different amounts of
compact tissue across different ROIs are expected to cause a poor correlation between
EIT and these two latter CT variables. Figure 8 corroborates this hypothesis.
This stronger association with CT air-content was a key finding in our study. Recently,
Frerichs et al (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). Methodological differences like 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
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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 theoretical 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 non-reproducible pressure-time tracings, the averaging of two EIT
15
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, since technological 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 inter-electrode
spacing along the thoracic perimeter can have impact on EIT analysis (figure 7). Better
electrode-array handling (71) and new mathematical formulations to take into account
thoracic asymmetries are needed for the next years.
Implications of current data
Despite the limitations cited above, 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 PEEP level can produce large
imbalances in regional ventilation along the gravity axis, usually by the same order of
magnitude observed in the present study (27, 36).
16
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 9). 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 tidal volume 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.
17
ACKNOWLEDGMENTS:
We are grateful to the EIT Study Group (especially to Prof. Raul Gonzalez and the
team of the Polytechnic Institute and to Dra. Joyce Bevilacqua from the Applied
Mathematic Institute - University of São Paulo), for their valuable input, criticisms and
discussions during the experiments and data analysis.
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25
TABLE AND FIGURE LEGENDS:
Table 1: Patient characteristics at entry
M = male; F = female; COPD = chronic obstructive pulmonary disease; AIDS = acquired
immunodefiency syndrome; PCP = Pneumocystis carini pneumonia; VT = tidal volume;
CST = respiratory system compliance (average slope of the inflation P-V curve, from zero
to 30 cmH2O)
Exclusion criteria: contraindications for sedation, paralysis or hypercapnia, and presence
of bronchopleural fistula.
Figure 1: Sketch of thoracic plane and theoretical effects of different electrode
positioning. During 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), mid-electrode 5 is frequently placed over the skin
close the posterior lung (LLL illustrates an atelectatic left lower lobe), and not at mid-lung
height. Since the EIT imaging algorithm assumes that electrode 5 is at mid-thoracic-
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
26
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 mid-lung height. Electrodes 1-5 have now a shorter
inter-electrode distance than electrodes 5-9 (where subcutaneous tissue is abundant).
We hypothesized that such positioning would avoid the over-representation of LLL.
Figure 2: Schematic regions of interest (ROI) on CT and EIT. Each one 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 a special software splitting the original circle with 788
pixels into subsets shown. On 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 antero-posterior
thoracic diameter, were drawn. Similarly, the crossing between skin contour and the
horizontal line at mid thoracic 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.
Figure 3: Bland-Altman plots of the differences in regional distribution of tidal volume,
estimated by EIT and CT. (for brevity, only 6 representative lung regions, and only the
standard electrode-positioning is presented). The overall span of Y-axis represents our a
priori limits of agreement. Representation: dotted lines - limits of agreement of the
observed sample; gray line - mean of observed differences; downward triangles -
27
measurements taken before CT exam; upwards 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.
Figures 4 and 5: EIT and CT images obtained at start (left) and at end (right) of slow-
inflation maneuver. Both images were obtained at the same thoracic plane (5th
intercostal space). 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.
Figure 6: Box-plot representing distributions of tidal volume estimated by EIT (white
boxes) and CT (gray boxes) in 9 patients, when using standard electrode-positioning.
Boxes indicate 25% and 75% percentiles, with median line inside. Error bars represent
5% and 95% percentiles. Left panel points out ventilation imbalances between left and
right thoracic areas. Right panel points out imbalances between upper and lower parts of
the thorax.
*: P = 0.04 using asymptotic approximation for Wilcoxon Signed-rank test.
†: P = 0.02 using asymptotic approximation for Wilcoxon Signed-rank test.
Figure 7: Box-plot representing distributions of tidal volume estimated by EIT (white
boxes) and CT (gray boxes). Electrode-positioning was standard in the top panel and
28
test in the bottom. Boxes represent 25% and 75% percentiles, with 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.
†: P = 0.028 using asymptotic approximation for Wilcoxon Signed-rank test.
Figure 8: 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,
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 9600 data points per graph. R2 represents
within-subject coefficient of determination.
Figure 9: 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.
29
FOOTNOTES:
1: EIT image distortions tend to produce plots with different slopes for each region.
Consider an EIT slice with adjacent regions A and B. Consider also X-axis
representing true changes in air-content for regions A or B (measured by CT) versus
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.
30
Table 1
Patient Age Gender Diagnosis APACHE IIPaO2/FiO2
PEEPVT
( mL )CST
( mL/cmH2O )
Days on Mech.Vent.
1 61 M COPD, right lobectomy, sepsis
18 289 5 540 61 3
2 44 M stroke, alcohol abuse, sepsis
35 222 15 480 35 8
3 52 M Hemothorax, sepsis, pneumonia
8 114 14 460 59 8
4 43 F COPD, AIDS, PCP 15 176 15 500 60 3
5 36 M AIDS, miliary tuberculosis, PCP
12 270 15 290 17 10
6 43 M Hystoplasmosis, tuberculosis
13 188 16 250 56 8
7 39 M Pulmonary carcinoma, sepsis
22 192 18 400 17 9
8 36 M Non-Hodgkin lymphoma, sepsis
11 237 20 350 47 4
9 60 F Congestive heart failure, lung edema
22 240 15 500 68 8
10 31 M Systemic lupus, miliary tuberculosis
19 227 13 370 22 20
31
Figure 1:
1 2
3
6
4
7
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89
LLL LLL
1 2
3
4
5
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CT EIT
LLL
1 2
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Standard positioning
13
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32
Figure 2:
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33
Figure 3:
RIGHT LUNG Layer 2UPPER LUNG
LOWER LUNG Layer 4LEFT LUNG
Mean VT Distribution (EIT and CT)
0.50
0.25
0
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36
Figure 6:
0.0
0.2
0.4
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0.8
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37
Figure 7:
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38
Figure 8:
-0.05 0.00 0.05 0.10 0.15 0.20 0.25-10000
0
10000
20000
30000
40000
50000
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40
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60
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39
Figure 9:
0 10 20 30 40 50 60
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Inflation Time (seconds)
0
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" ON LINE DATA SUPPLEMENT "
IMBALANCES IN REGIONAL LUNG VENTILATION: A VALIDATION
STUDY ON ELECTRICAL IMPEDANCE TOMOGRAPHY
AUTHORS:
VICTORINO, JOSUÉ A. 1
BORGES, JOÃO B. 1
OKAMOTO, VALDELIS N.1
MATOS, GUSTAVO F. J. 1
TUCCI, MAURO R. 1
CARAMEZ, MARIA P. R. 2
TANAKA, HARKI 1
SUAREZ SIPMANN, FERNANDO 3
SANTOS, DURVAL C. B. 3
BARBAS, CARMEN S. V. 1
CARVALHO, C. R. R. 1
AMATO, MARCELO B. P. 1
1
METHODS:
This study took place at the Respiratory and Medical ICU’s of Hospital das Clínicas,
University of São Paulo, Brazil. Patients were transported to the Radiology Department
for CT scanning. Data was collected from July to December 2001.
PATIENT SELECTION
The study protocol was approved by the hospital ethics committee.
Hemodynamically stable patients, under controlled mechanical ventilation were eligible
for study. Patients were kept supine, under sedation and paralysis during the study
period. They were monitored with ECG, pulse oxymetry and non-invasive blood
pressure. An independent team of physicians was responsible for patient care, with
autonomy to withdraw patients from the study.
MONITORING AND EQUIPMENT
Disposable flow/presssure sensors were connected to proximal airways, between the Y-
piece of the ventilator circuit and the endotracheal tube (CO2SMO-Plus - Novametrix
Medical Systems, Wallingford, CT). The CO2SMO-Plus monitor was connected to a
Notebook through a serial RS 232 output (DELL PII, 266mHz), where a special program
recorded proximal pressure and flow tracings at 100 Hz.
A customized flow-generator (Intermed – São Paulo, Brazil) drove the slow lung
inflation. It provided adjustable flow rates from 0.2 to 10 L/min, with 1% precision (for
intra-airways pressure up to 60 cmH2O). The flow-generator was previously calibrated at
2
1L/minute, through the displacement of reference volumes observed at a dry spirometer
(Vitatrace® Pró-doctor; Rio de Janeiro).
For EIT images, we used a 16-electrode Sheffield Applied Potential Tomograph (DAS-
01P Portable Data Acquisition System, IBEES, Sheffield, UK).
During transportation to the CT room, patients were ventilated with a Servo-Siemens
300 C ® ventilator (Siemens-Elema AB, Sweden).
For CT images, we used a GE HighSpeed equipment (GE, Milwalkee, WI), storing data
in an optic disk for subsequent analysis.
PROTOCOL DESIGN
Because it was impossible to obtain simultaneous EIT and CT images due to excessive
electromagnetic interference (signals from EIT got corrupted, with systematic failure in
reciprocity tests), we compared both technologies in sequential order (EIT→CT→EIT),
with repeated measures for EIT (pre and post CT) trying to control for eventual carry
over effects. Each EIT measure was composed by 3 sequential EIT acquisitions during 3
independent slow-inflations, each one preceded by a homogenizing lung maneuver. In
between EIT acquisitions, patients were mechanically ventilated according to baseline
parameters described below, for approximately 5-10 minutes.
For the first and second EIT acquisitions, two different electrode-positioning
arrangements (standard and test) were selected in random order. All electrodes were
discarded and replaced in between. An additional slow-inflation was repeated at the
same positioning of the second acquisition, without replacement o electrodes. In all
trials, electrode arrays were placed exactly at the same cross-sectional plane of the
3
thorax, but with different inter-electrode spacing according to the randomized positioning
(figure 1). Reference electrodes 1 and 9 were kept in the same spots for both
arrangements (anterior and posterior midline, respectively). Electrode 5 was always at
the left side of the thorax. The thoracic perimeter was previously assessed, and the
target skin positions for the electrodes were marked with ink. Those marks were later
used to guide CT scanning.
SPECIAL PROCEDURES AT THE ICU (BEFORE AND AFTER CT SCANNING):
At baseline, all patients were submitted to volume controlled ventilation with VT = 8
mL/kg, respiratory rate of 14 breaths/minute, PEEP = 10 cmH2O, at 100% oxygen. The
constant flow generator was also fed by 100% oxygen. Upper airways and trachea were
previously aspirated. Leak tests at a pause pressure of 25 cmH2O were performed to
adjust internal cuff pressure of endotracheal tube.
Patients were placed in supine position and 16 ECG electrodes (Meditrace 200
electrode, foam adhesive gel 3.6 cm 100/pch 1000/cs Danlee Medical Products, Inc)
were placed on the thorax at the level of the 5th inter-costal space at midclavicular line,
defining a transverse plane crossing the thorax 2 cm below the carina. An additional
ground electrode, for common mode feedback, was placed on the inferior abdomen.
The EIT system was based on a personal computer (Pentium III – 600 MHz), receiving
real-time voltage information from electrodes. Images were reconstructed off-line. At its
maximum speed, a complete voltage set (formed by the average of 10 rotating cycles of
voltage readings) is collected in approximately 1 second. In parallel, a second computer
(notebook, DELL PII, 266mHz) acquired pressure/flow signals at 100 Hz, through serial
4
connection to the CO2SMO-Plus device. Before data acquisition, we ran a customized
software for synchronizing the internal clocks of both computers (error < 0.005 second).
After homogenizing lung history, the slow-inflation was started by manual actuation of a
3-way stopcock, directing the constant flow towards the lung and through the proximal
pressure/flow sensor. After training, we always started the first EIT scanning 1.0 to 1.5
seconds after starting the slow-inflation. Pressure/flow data acquisition always started a
few seconds before, in order to later precise the starting time of inflation (see below).
EIT electrodes were then removed and patients were transported to the CT room.
SPECIAL PROCEDURES AT THE CT ROOM:
Patients were kept in supine position inside the gantry, with a laser beam simulating the
X-ray emission. The table was positioned to guarantee that the CT slice was taken at the
same cross-sectional plane defined by the electrode marks. Before starting data
acquisition, we ran a customized software for synchronizing the internal clocks of the
notebook (collecting proximal pressure/flow) and the CT workstation within 0.005
second. Lung conditions were homogenized as before. After training, we were able to
start the first scanning 1.0 to 1.5 seconds after the very start of the slow-inflation. The
tube current *time product was set at 120 mAs and beam energy at 100 Kva.
IMAGING ANALYSIS – EIT:
Off-line, the two-dimensional EIT image reconstruction algorithm of the Sheffield device
projects the estimates of the regional changes in thoracic impedance over a 32 x 32
matrix. A central circular region with 788 pixels (diameter = 32 pixels) represents the
thorax (figure 1). The image output assigns one value per pixel to represent the relative
5
change (% change) in impedance for that pixel from the reference frame to the current
one. We pre-defined 12 ROIs for this study, represented on figure 2. Each one of the 12
ROIs embraced a portion of the chest wall plus part of the lung. The portions were
selected automatically by a special software splitting the original circle into the following
subsets: total (788 pixels), right and left (394 pixels each), upper and lower (394 pixels
each), layers 1 to 4 (154, 240, 240, 154, pixels respectively), and the four quadrants
(197 pixels each).
A Labview 5.1.1 routine (Labview 5.1.1 – National Instruments software) was
customized to calculate the integral of pixel value over the 12 ROIs.
IMAGING ANALYSIS – CT SCAN:
A quantitative analysis of the tomographic tissue densities was performed according to a
two-step procedure. Firstly, the Osiris Medical Imaging Software version 3.6 (University
Hospital of Geneva - available on the Web: www.expasy.ch/UIN) was used to generate
histograms of density distribution for manually determined regions of interest (figure 2).
The Osiris software produces histograms in which the frequency distribution is displayed
over 2000 arbitrary CT compartments (from –1000 HU to +1000 HU, each step being 1
HU), and the number of voxels included in each compartment is presented in absolute
numbers. Subsequently, all data gathered from the histograms were transferred and
analyzed by a customized software (Labview 5.1.1 – National Instruments software),
programmed to extract quantitative information from regions of interest.
Briefly, the quantitative analysis is based on a quasi-linear relationship between x-ray
attenuation in a given tissue volume – i.e. the voxel, considered as a CT volume unit -
and the physical density of that lung volume (i.e. the mass/volume ratio) (E1-3).
6
The tissue attenuation on x-ray is expressed in CT numbers, or Hounsfield Units (HUs).
This CT number is obtained, in any given voxel, through determining the percentage of
radiation absorbed in a given lung volume. The attenuation scale arbitrarily assigns a
value of 0 H.U for water and a value of –1,000 HU for air (no absorption). This scaling
results in values close to +1,000 HU for bone tissues. By approximating tissue density to
water density (with a specific weight = 1), the relationship between tissue composition
and CT numbers, in any lung region of interest (ROI), may be expressed as:
Volumegas / (Volumegas + Volumetissue) = mean CT numberobserved /(CT numbergas – CT
numberwater)
If the equation above is re-arranged, it is possible to assess, for any given lung voxel,
where the total volume is known, the gas volume inside, the tissue volume inside, and
the gas/tissue ratio (E3). For example, a voxel of –1,000 HU is uniquely composed by
gas, a voxel of 0 H.U. is uniquely composed by water (or “tissue”), and a voxel of –500
H.U. is approximately 50% composed by gas and 50% by water (or tissue). Therefore,
the amount of air inside each voxel may simply be calculated as:
Air-contentVOXEL = (- CT number / 1000) x (Voxel volume)
During slow-inflation - as only one fixed slice through time was assessed - the changes
in air-content refer to the volume of air entering a 1 cm-thick slice. For reference, we
usually observe the entrance of approximately 100 mL of air in a typical slice (at the
thoracic level used in this study) during vital capacity maneuvers in volunteers.
7
The overall accuracy of these calculations was previously validated by a bench study
with a super-syringe with known internal volume (100 mL), and later in a sponge
experiment (intended to represent a complex lung-like structure) during stepwise
changes in water content. For the syringe, the amount of air computed by the addition of
all adjacent slices (at a pitch = 1) was 98 mL (2% error). For the sponge, after the
injection of 500 mL water in its interior, a 476 mL reduction was observed in the
calculated air-content (4.8% error).
SYNCHRONIZING CT AND EIT IMAGES
As described above, the notebook acquiring proximal pressure/flow signals was
synchronized with the EIT personal computer, and later, with the CT workstation (error <
0.005 second). Retrospectively, we looked at airway flow tracings (100 Hz), identifying
the start of slow-inflation with sharp flow spikes (maximum error of ± 0.02 second). Off-
line, we referenced the very start of EIT or CT scans relative to this time origin. Since we
had a higher image rate in the EIT acquisition, we linearly interpolated EIT image data
(for all ROIs) to the same points in time where we had CT scans, getting 30-45
“synchronized” images per inflation.
REGIONAL VENTILATION ACCORDING TO CT AND EIT IMAGES
Since the data collected during the first synchronized EIT scan was considered as the
voltage reference set, all pixels in this initial image had zero value, and subsequent
images represented impedance variations relatively to this reference. Therefore, after
obtaining the last synchronized frame during slow inflation, the regional distribution of
8
tidal volume was simply defined as the ratio between the integral of pixel values over a
certain ROI, divided by the integral of pixel values over the whole slice.
For CT images, regional distribution of ventilation for a particular ROI was calculated
through the comparison of the last (FINAL) to the first synchronized scan (INITIAL), using
the formula below:
( Air-content ROI FINAL - Air-content ROI INITIAL )
( Air-content ALL PIXELS FINAL - Air-content ALL PIXELS INITIAL )
STATISTICAL ANALYSIS
Before assessing the reproducibility of EIT measurement, and since the slow-inflations
were performed at different time points (up to 2 hours difference), the stability of lung
mechanics was tested through the cross-correlation of the 7 pressure-time curves
obtained for each patient (each one with 30-60 data points - figure E1. The presence of
at least one coefficient of correlation below 0.9 was considered as an indication of large
changes in lung conditions, and in this case the whole study case was removed from the
analysis.
EIT ERROR AND REPRODUCIBILITY
For each pair of measurements, at each electrode positioning (standard and test) and
for each lung region (figure 2), the within-subject standard deviation between repeated
measures (Sw) of regional ventilation was calculated according to the method described
9
by Bland and Altman (E4). The same calculations were performed for the special cases
without electrode replacement.
In order to obtain an overall Sw (which represents a global error detected during
repeated measures, for all regions and for both electrode-positioning arrangements), we
considered all measurements after including the regions, patient-region interactions, and
electrode positioning as factors in the repeated measurements ANOVA model. The
square root of the residual variance was considered as SW.
In order to establish an a priori and clinically meaningful value for the reproducibility
required in our experiments, we reasoned the following:
When splitting the lung according to its regions (see Figure 2), we frequently observed a
silent half-lung, or a silent quadrant/layer, suggesting a complete block of regional
ventilation (regional ventilation = 0%) directed to one half or one fourth of the lung.
Considering that the expected regional ventilation for those regions should be 50% and
25%, respectively (supposing homogeneous lung ventilation), and also considering that
the chances of such an extreme result being observed just by chance had to be lower
than 5%, a maximum Sw was calculated to be accepted in repeated measurements (i.e.
the reproducibility limits) (E4):
For regions representing half lung section (right, left, upper and lower ROIs):
Sw = 50.0% / (2–2 x 1.96) = 18.0% (E4)
For regions representing quadrants and layers (layers 1-4, URQ, ULQ, LRQ, and LLQ):
Sw = 25.0% / (2–2 x 1.96) = 9.0%.
10
The inter-observer agreement was not assessed in this study, since most of the
measurements were performed by computer calculation (quantitative image analysis),
with very few subjective decisions.
In order to check if some bias was introduced by the interposed CT scans between the
repeated EIT measurements, we checked if the inclusion of an additional factor (the
pre/post within factor) in the ANOVA model significantly decreased the residual
variance. This procedure attested a non-significant bias (P > 0.20).
EIT AGREEMENT AND CORRELATION TO CT
We used the same rationale to define, a priori, the maximum limits of agreement
between the two methods: in order to reliably detect a complete blockage of regional
ventilation, the sampled bias ± 2 SD should not cross the ±25% boundaries for an area
corresponding to one fourth of the lung; or ±50% for any half lung area.
During multiple linear regression (integral of pixel values plotted against CT air-content,
CT gas/tissue ratio or CT mean-densities), adjustments for inter-subjects differences
were obtained through the inclusion of dummy variables on the model according to the
procedure described by Bland and Altman (E5). Therefore, the reported R2 values
correspond to within-subject determination coefficients.
The SPSS software, version 9.0, was used for all the tests.
11
REFERENCES for the WEB supplement:
E1. Drummond GB. Computed tomography and pulmonary measurements. Br J
Anaesth 1998; 80:665-71.
E2. Hounsfield GN. Computerized transverse axial scanning (tomography). 1.
Description of system. Br J Radiol 1973; 46:1016-22.
E3. Gattinoni L, Caironi P, Pelosi P, Goodman LR. What has computed tomography
taught us about the Acute Respiratory Distress Syndrome? Am J Respir Crit Care
Med 2001; 164:1701-11.
E4. Bland JM, Altman DG. Measurement error. BMJ 1996; 312:1654.
E5. Bland JM, Altman DG. Calculating correlation coefficients with repeated
observations: Part 1 - Correlation within subjects. BMJ 1995; 310:446.
12
Figure E1 (Web repository): Consecutive slow-inflation recordings (converted into
pressure-volume curves) obtained in two patients. Each curve lasted ± 2 minutes. Three
recordings (black filled symbols) were obtained before the recording at CT room (thick
gray line) and 3 recordings afterwards (open symbols). Patient #7 presented
reproducible patterns of lung inflation (cross correlation coefficients > 0.99) while patient
#2 presented very dissimilar behaviors across inflations (cross correlation coefficients
< 0.9). He was, therefore, subsequently excluded. Although discarded from our analysis,
the dynamics of lung inflation observed in this case was very illustrative of the spatial
resolution of EIT (see the animations #1 and #2).
Figure E2 (Web repository): Scattered plots of patient #6, illustrating the linear
regression analysis for impedance variations during slow-inflation as dependent
variable, projected over the air-content changes within CT slice. Electrode positioning
was standard. Each colored plot represents the behavior of a different ROI during both
trials (pre and post the CT scan). The superposed plots and slopes indicate that there
was very little image distortion. R2 represents the overall coefficient of determination.
Animation #1 and Animation #2: Simultaneous pressure-volume recordings vs. EIT
images (sequence #1), and synchronized CT vs. EIT images (sequence #2). The image
sequence suggests that the right lung was blocked during a significant part of lung
inflation, probably due to persistent secretions in the airways, despite our previous
aspiration attempt. This blockage was overcome at the end of inspiration after varying
periods of time for each slow-inflation.
13
Figure E1:
PATIENT #7
AIRWAY PRESSURE (cmH2O)
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PATIENT #4
AIRWAY PRESSURE (cmH2O)
0 10 20 30 40 50
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PATIENT #2PATIENT #7
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