revisãoeit TIE

C

Electrical impedance tomograph
yEduardo L.V. Costaa, Raul Gonzalez Limab and Marcelo B.P. Amatoa
aRespiratory Intensive Care Unit, University of SaoPaulo School of Medicine and bDepartment ofMechanical Engineering, Escola Politecnica, Universityof Sao Paulo, Sao Paulo, Brazil

Correspondence to Marcelo B.P. Amato, MD, PhD,Respiratory Intensive Care Unit, University of SaoPaulo School of Medicine, Av Dr Arnaldo 455, Room2206 (2nd floor), 01246-903 Sao Paulo, BrazilTel: +55 11 3061 7361; fax: +55 11 3061 2492;e-mail: [email protected]

Current Opinion in Critical Care 2009,15:18–24

Purpose of review

Electrical impedance tomography (EIT) is a noninvasive, radiation-free monitoring tool

that allows real-time imaging of ventilation. The purpose of this article is to discuss the

fundamentals of EIT and to review the use of EIT in critical care patients.

Recent findings

In addition to its established role in describing the distribution of alveolar ventilation, EIT

has been shown to be a useful tool to detect lung collapse and monitor lung recruitment,

both regionally and on a global basis. EIT has also been used to diagnose with high

sensitivity incident pneumothoraces during mechanical ventilation. Additionally, with

injection of hypertonic saline as a contrast agent, it is possible to estimate ventilation/

perfusion distributions.

Summary

EIT is cheap, noninvasive and allows continuous monitoring of ventilation. It is gaining

acceptance as a valuable monitoring tool for the care of critical patients.

Keywords

electrical impedance tomography, imaging, lungs, monitoring

Curr Opin Crit Care 15:18–24� 2009 Wolters Kluwer Health | Lippincott Williams & Wilkins1070-5295

IntroductionElectrical impedance tomography (EIT) is a noninvasive,

radiation-free monitoring tool that allows real-time ima-

ging of ventilation [1–3]. EIT is the only bedside method

that allows repeated, noninvasive measurements of

regional changes in lung volumes [4,5]. For this reason,

EIT has been used as a monitoring tool in a variety of

applications in critical care medicine, including monitor-

ing of ventilation distribution [3,6], assessment of lung

hyperdistension [7] and collapse [8�,9�], detection of

pneumothorax [10�,11], among others. In this article,

we will discuss the fundamentals of EIT and review

the use of EIT in critical care patients in the light of

recent literature.

How electrical impedance tomography worksEIT uses injection of high frequency and low amplitude

electrical currents, typically through 16 or 32 electrodes

around the thorax, to obtain images of a cross section of

the lungs (Fig. 1) [1,10�]. These currents travel through

the thorax following pathways that vary according to

chest wall shape and thoracic distribution of impedi-

tivities. The resulting electric potentials on the surface

of the chest wall are measured and used to obtain the

electric impedance distribution within the thorax using a

reconstruction algorithm that solves an ill-posed non-

linear problem. Ill-posedness means that the solution

opyright © Lippincott Williams & Wilkins. Unautho

1070-5295 � 2009 Wolters Kluwer Health | Lippincott Williams & Wilkins

for intrathoracic impedances may not be unique or

may be unstable: small errors in voltage measurements

may lead to fairly different solutions. This problem is

aggravated by the small number of measurements made

at the body surface, because of the present technological

limitation on minimum electrode size (and thus maximal

electrode number). In order to overcome the ill-posed

nature of impedance estimation, most EIT imaging

algorithms make use of additional assumptions, known

as regularizations, such as smoothness of the intrathoracic

impedance distribution [12,13]. These regularizations

help the estimation algorithm to decide between com-

peting solutions, producing an image that is a reasonable

estimation of the true impedance distribution within the

thorax, at the expense of degraded spatial resolution or

attenuation of maximum perturbations.

Absolute versus difference imagesThoracic shape can contribute as much as internal thor-

acic impedances to the measured voltages at the chest

wall surface [13]. Consequently, the reconstruction of the

absolute impedance distribution, albeit feasible, requires

knowledge of the shape of the thorax. Difference images,

described by Brown [13], can be generated without

precise knowledge of the thoracic geometry. Their recon-

struction is based on relative changes in impedance in

relation to a reference, assuming that the shape of the

thorax is nearly rounded and did not change in between.

rized reproduction of this article is prohibited.

DOI:10.1097/MCC.0b013e3283220e8c

mailto:[email protected]

http://dx.doi.org/10.1097/MCC.0b013e3283220e8c

C

Electrical impedance tomography Costa et al. 19

Figure 1 Disposition of electrodes around the thorax and elec-

trical current injection

Computerized tomography (CT) of the thorax of a patient with schema-tically drawn electrodes and electrical current pathways through thethorax. One pair of electrodes injects electrical current at a time whereasthe remaining electrodes read the voltages produced as a result ofelectrical current passing through the thorax. The injecting pair isalternated sequentially so that after a full cycle, all possible adjacentelectrodes serve as injectors. Each full cycle results in an image and 50images are produced each second.

This relative or differential approach cancels out most

errors related to wrong assumptions about the true thor-

acic shape (the same error is equally present in both

images) and has proven its validity in the last years.

The use of normalized voltage measurements further

improves the robustness of such solution, which ignores

the absolute values of impedance at the beginning of the

measurements. Typically, at a frequency of 10 kHz, the

electrical impedance of the chest tissues is in the order of

2–4 Vm, and the average impedance of the lung is around

10 Vm [14]. During respiration, whereas lung impedance

changes up to 300% (from 7.2 to 23.6 Vm in one report)

[14], chest wall impedance remains relatively constant.

Most currently available EIT devices in clinical practice

and most publications in the field use the relative

approach and thus calculate difference impedance

images in relation to a reference. The output image of

such algorithms is usually a 32� 32 or a 64� 64 array

(which might be later interpolated) from which each

element corresponds to a pixel on the image and contains

the change in impedance in relation to a reference frame,

expressed as a percentage. The baseline impedance

cannot be recovered. Thus, breaths producing a change

in lung impedance from 5 to 10 Vm, or from 10 to 20 Vm

will produce exactly the same relative image. As we will

see later, this limitation is not worrisome as far as clinical

applications are concerned, because there is a well

described and linear relationship between the amount

opyright © Lippincott Williams & Wilkins. Unauth

of air entering the voxel and the percentage change in

lung impedance [15,16].

One drawback of the use of difference images is that only

regions of the thorax that change their impedance over

time are represented in EIT images. Consequently,

preexisting consolidated areas of the lung (e.g., pneumo-

nia or atelectasis), pleural effusions or large bullae are not

represented in difference EIT images. For this reason,

absolute images have the potential to bring additional

and important information and there is continuing

research to improve the quality of absolute images.

Although some progress has been made, with promising

results presented by Hahn et al. [11], the quality of the

images is still not good enough for meaningful clinical

use. Further development in this area, using estimations

of electrode position and taking thoracic shape into

account will enable precise absolute image reconstruc-

tion.

Spatial and temporal resolutionSpatial resolution of EIT depends on the accuracy and

noise of the measurements, the number of electrodes,

and the regularization used. For this reason, spatial resol-

ution varies from one EIT device to another, and even

within a single device depending on the settings

employed. In bench tests, the resolution is usually

optimized because less regularization is required. At

the bedside, however, noise and wrong assumptions

about thoracic shape require stronger regularizations

and resolution is compromised. On average, in 16-elec-

trode systems, resolution is 12% of the thoracic diameter

for regions in the periphery of the lung and 20% for

central regions. In 32-electrode systems, this resolution

can be improved to 6–10% of the thoracic diameter

(Turri F, personal communication). In a typical adult

patient, this resolution corresponds to approximately

1.5–3 cm in the cross-sectional plane. The spatial resol-

ution in the craniocaudal direction is lower, the slice

thickness amounting to approximately 7–10 cm. Such

thick slices can be useful for studying the lung behavior

during mechanical ventilation, when the detection of

heterogeneities along the gravity axis is the main target

[17,18].

Although it is possible to improve spatial resolution, for

example, by increasing the number of electrodes or by

improving hardware performance, it is unlikely that EIT

will ever reach the resolution of CT or MRI [19]. On the

contrary, modern EIT devices are characterized by high

temporal resolution, with some generating 50 images/s. It

is thus possible to follow closely the time pattern of

regional inflation and deflation of the lung. For example,

it is possible to show that some areas start to inflate after

others, reflecting either tidal recruitment [20�] or local

orized reproduction of this article is prohibited.

C

20 Respiratory system

auto-PEEP. Additionally, by use of brief periods of apnea

or by filtering out ventilation [21�], high temporal resol-

ution allows for monitoring of changes in intrathoracic

impedance caused by perfusion.

Clinical applicationsInitial EIT applications on critical care medicine focused

mainly on ventilation and its distribution; now other

applications are being studied such as detection of pneu-

mothorax, assessment of lung recruitment and collapse,

and lung perfusion.

Assessment of lung recruitment and lung collapse

Careful titration of PEEP is of utmost importance for the

success of ventilatory strategies based on the open lung

approach. Global lung parameters, such as pressure-

volume curves [22,23] or respiratory system compliance

[24] fail to represent what is happening to the lung on a

regional basis [25]. Collapsed and overdistended lung

compartments commonly coexist, and a method capable

of assessing both, simultaneously, would be invaluable as

a tool to titrate PEEP. Our group described an EIT-based

method for estimating alveolar collapse at the bedside,

pointing out its regional distribution. On an experimental

model in pigs, we found a good correlation between EIT

and CT estimates of lung collapse during decremental


Figure 2 Estimation of collapse and overdistension

Computerized tomography (CT) of the thorax of a patient with right inferiohyperdistension (left) and lung collapse (right) in a patient with ARDS. The lright, at a PEEP of 3 cmH2O. In these images, CT slice thickness was 1 cm

PEEP trials after a maximal lung recruitment maneuver

[26]. Combining data obtained from EIT and respiratory

mechanics, it is also possible to estimate the amount of

overdistension during a PEEP trial, although validation

of such estimates represents a challenge due to the lack of

a gold standard to compare with (Fig. 2) [27]. Meier et al.[9�] recently used EIT to monitor regional tidal volume

during a PEEP titration maneuver in a model of surfac-

tant depletion in pigs. Looking at changes in regional

tidal volumes that occurred with changes in PEEP, they

were able to detect the initiation of regional lung collapse

and of regional lung recruitment before global changes

occurred in lung mechanics. Additionally, they showed

good correlation of ventilation estimated by EIT and CT,

confirming the results of Victorino et al. [3]. Together,

these results bring an exciting possibility of bedside

titration of PEEP based on regional lung mechanics.

In a similar report, Luepschen et al. [28�] showed that the

center of gravity of ventilation images moves dorsally

during lung recruitment and ventrally during lung col-

lapse. This EIT-based parameter brought additional

information to a fuzzy controller of ventilation that opti-

mized lung recruitment based on oxygenation, venti-

latory parameters and hemodynamics. Other authors also

used the center of gravity of ventilation to assess lung

recruitment [29]. In 16 newborn piglets with acute lung


r lobe pneumonia (top figures) and EIT maps (bottom figures) of lungeft-hand images were obtained at PEEP of 23 cmH2O and those on the

, and EIT slice thickness was approximately 7 cm.

C


injury induced by whole lung lavage, Frerichs et al.showed that EIT allows visualization of the effects of

acute lung injury, lung recruitment, surfactant adminis-

tration, and mechanical ventilation strategy. They found

that lung injury displaces the ventilation ventrally, and

lung recruitment restores the center of ventilation to the

normal position. After surfactant administration, venti-

lation shifts ventrally over 10–60 min, but remains in the

preinjury position if surfactant administration is followed

by a recruitment maneuver.

Other methods to estimate lung recruitment using EIT

have been proposed. In a recent report [20�], the authors

compared EIT measures to dynamic CT in 18 pigs

divided into three groups (control, direct, and indirect

lung injury). EIT allowed real-time monitoring of

regional ventilation distribution. During an interposed

slow inflation, regional recruitment was detected by using

the time delay between start of inspiration and start of

regional inflation (ventilation delay index). Hinz et al.[30�] monitored 20 mechanically ventilated patients

during with acute lung injury/acute respiratory distress

syndrome (ARDS) during tidal breathing and showed

that the behavior of regional impedance over time was

heterogeneous and significantly different from that of the

whole lung. The findings suggested the occurrence of

hyperdistension and tidal recruitment in different regions

of the lung (Fig. 2).

Although a careful titration of PEEP is important, lung

conditions frequently change, and a selected PEEP

might not suffice to keep the lung open at all times,

especially if transient depressurization of the lung occurs.

Depressurization is particularly common when suction-

ing of the airways is required for clearance of secretions.

To assess the derecruitment caused by closed-system

suctioning, Wolf et al. [31�] studied six children with

ARDS, on pressure controlled ventilation and continu-

ously monitored with EIT. They showed that lung

volumes decreased on average by 5.3 ml/kg after three

suctioning maneuvers. Unexpectedly, they showed that

the most dorsal regions of the lung were the least affected

by derecruitment; as the authors used difference EIT

images, they could only speculate that this region was

atelectatic even before the suctioning maneuver. Another

possibility might be suggested in this context, which is

the presence of air trapping in dependent lung regions. In

pigs monitored with EIT after surfactant depletion,

Lindgren et al. [8�] found different results. They showed

that endotracheal suctioning induced collapse of the lung

and decreased regional lung compliance during open-

system suctioning but not during closed-system suction-

ing. The collapse was predominantly in the dorsal

regions, but recruitment was reestablished in less than

10 min by simple reconnection to the ventilator. Never-

theless, it is important to bear in mind that the model of


surfactant depletion using whole lung lavage leads to

lungs prone to collapse, but highly recruitable. There-

fore, these results need careful scrutiny before being

extrapolated to inflamed, edematous lungs of ARDS

patients, on whom depressurization can cause long-term

effects and should be avoided.

On a different report, the same authors used EIT to

assess lung collapse during bronchoscopic suctioning

in patients mechanically ventilated with acute lung

injury [32�]. They elegantly showed that bronchoscopy

initially leads to localized auto-PEEP due to the reduced

transverse area available for airflow, and that suctioning

leads to a decrease in lung aeration and compliance even

when a closed suctioning system is used. They obtained

similar findings both in volume-controlled and pressure-

controlled ventilation. In mechanically ventilated

patients being submitted to bronchoscopic procedures,

EIT might prove to be a useful tool to quantify the

amount of collapse and guide postsuctioning recruit-

ment.

Detection of pneumothorax and pleural effusion

Pneumothorax is a relatively common complication in

critical care patients under mechanical ventilation or

submitted to invasive procedures such as central venous

line placement or thoracocentesis. Costa et al. [10�]

created an algorithm for the detection of pneumotho-

races using EIT. The algorithm was created in a first

set of 10 pigs, and subsequently tested in 29 pigs. EIT

showed a sensitivity of 100% (CI 93–100%) to detect

pneumothoraces as small as 20 ml (Fig. 3). The major

limitation of this study was the need for a baseline

measurement before the occurrence of the pneumo-

thorax, limiting the application to monitoring of situa-

tions with high risk of developing pneumothorax, such

as central venous line placements or mechanical ventila-

tion with high alveolar pressures.

Another group of investigators [11] studied the combined

use of dynamic and absolute images for the diagnosis of

pneumothorax and pleural effusion. They studied five

pigs and showed reproducible results with the develop-

ment of pneumothorax consisting of regional increase in

absolute impedance and decreased ventilation. Pleural

effusions, being more conductive than the lung, pro-

duced a regional decrease in impedance associated with

decreased ventilation. They further acquired images in

four patients and showed that EIT absolute images were

compatible with CT images.

The use of EIT as a sensitive monitoring tool for the

detection of pneumothorax and pleural effusions

[10�,11,33] is appealing but probably will not be applied

in clinical practice until systematic studies in patients

are performed.


C


Figure 3 Detection of pneumothorax

Computerized tomography (CT), ventilationmap, and aeration change map obtained atbaseline and after the induction of a 100 mlpneumothorax in a pig with partial atelectasisof the lungs.

Correct placement of endotracheal tube

Steinmann et al. [34�] studied 40 patients requiring one-

lung ventilation for surgical procedures. EIT monitoring

started before intubation and continued throughout the

protocol. All clinical decisions were based on fiberoptic

bronchoscopy and EIT investigators were blinded to

bronchoscopy findings. EIT correctly identified left

and right one-lung ventilation, but could not identify

misplacement of the endobronchial cuff, suggesting that

EIT cannot fully replace bronchoscopy as a guide to one-

lung ventilation. Although not designed to address this

question, this study suggests that EIT can be used to

diagnose selective intubation or endotracheal tube dis-

placement during conventional two-lung ventilation.

Perfusion and ventilation/perfusion ratios

The relationship between ventilation and perfusion

(VA/Q) is important in the pathophysiology of a number

of clinical conditions, such as pulmonary thromboembol-

ism, advanced chronic obstructive pulmonary disease and

ARDS. Additionally, in the ICU, VA/Q relationships

can change quite fast following changes that occur in

cardiac output and alveolar ventilation [35�]. Better

understanding of the VA/Q relationships could potentially

help guide treatment decisions and optimize gas

exchange. It is possible to use EIT to study lung per-

fusion through intravenous injection of hypertonic saline,

which serves as a contrast agent for EIT images (because

of its extremely low impeditivity), together with a breath


hold maneuver [36�,37]. Using this technique, we con-

ducted a study (Borges JB, unpublished data) to compute

regional VA/Q ratios in a pig model of ARDS and com-

pared them to VA/Q ratios computed with single photon

emission tomography. Ventilation was calculated using

the concept of functional images, as previously described

[2]. Regional perfusion estimations were based on the

time-signal intensity curve (or first-pass contrast curve)

observed in each pixel after the bolus of hypertonic

saline. A gamma curve, which models the indicator

washin, was fitted to sequential impedance measure-

ments of each pixel during the passage of the ‘contrast’.

We found that it is possible to derive meaningful VA/Q

maps from EIT data (Fig. 4) (Borges JB, unpublished

data).

Recently Deibele et al. [21�] used a combination of

techniques including signal filtering and principal com-

ponents analysis to separate the respiratory from the

cardiac signal in real time without the need for breath

holding. The authors were able to decompose the EIT

signal and to produce images from the cardiac oscillations.

This technique has the advantage over the hypertonic

saline technique in that it allows continuous monitoring

of cardiac-related oscillations. The amplitude of cardiac

related oscillations seen in the EIT signals, however, has

not been shown to correspond to the amount of local

perfusion, and further studies are necessary to explore its

precise physiological meaning [38].


C


Figure 4 Ventilation/perfusion maps

Computed tomography, ventilation/perfusion (VA/Q) map and distribution of ventilation and blood flow derived from electrical impedance tomographydata. On the color scale for the VA/Q maps, red indicates shunt, green indicates VA/Q¼1, and white indicates dead space ventilation. Shunt is shownby the closed circle at VA/Q¼0. Panels a, b and c are from a mechanically ventilated pig with normal lungs and panels d, e and f are from the sameanimal after induction of atelectasis of the left lung. CT, computed tomography. Adapted with permission from (Borges JB, unpublished data).

ConclusionEIT is gradually gaining acceptance as a valuable

monitoring tool for the care of critical patients. It is cheap,

noninvasive and, up to now, has been shown to reliably

track changes in regional ventilation, describe regional

ventilation distribution and regional lung mechanics,

detect pneumothoraces, and monitor lung recruitment

and derecruitment. Other applications such as monitoring

of lung perfusion and of ventilation/perfusion distri-

bution are feasible but still require further studies.

AcknowledgementsFinancial support by grants from ‘Fundacao de Amparo a Pesquisa doEstado de Sao Paulo (FAPESP)’ – Sao Paulo State Research SupportFoundation and ‘Financiadora de Estudos e Projetos (FINEP)’ –Studies and Projects Financial Support Provider.

References and recommended readingPapers of particular interest, published within the annual period of review, havebeen highlighted as:� of special interest�� of outstanding interest

Additional references related to this topic can also be found in the CurrentWorld Literature section in this issue (p. 71).

1 Barber DC, Brown BH. Applied potential tomography. Journal of Physics E:Scientific Instruments 1984; 17:723–733.

2 Frerichs I, Hahn G, Schiffmann H, et al. Monitoring regional lung ventilation byfunctional electrical impedance tomography during assisted ventilation. Ann NY Acad Sci 1999; 873:493–505.

3 Victorino JA, Borges JB, Okamoto VN, et al. Imbalances in regional lungventilation: a validation study on electrical impedance tomography. Am JRespir Crit Care Med 2004; 169:791–800.


4 Frerichs I, Hinz J, Herrmann P, et al. Detection of local lung air content byelectrical impedance tomography compared with electron beam CT. J ApplPhysiol 2002; 93:660–666.

5 Wolf GK, Arnold JH. Noninvasive assessment of lung volume: respiratoryinductance plethysmography and electrical impedance tomography. Crit CareMed 2005; 33:S163–S169.

6 Frerichs I, Dargaville PA, Dudykevych T, Rimensberger PC. Electrical im-pedance tomography: a method for monitoring regional lung aeration and tidalvolume distribution? Intensive Care Med 2003; 29:2312–2316.

7 Adler A, Shinozuka N, Berthiaume Y, et al. Electrical impedance tomographycan monitor dynamic hyperinflation in dogs. J Appl Physiol 1998; 84:726–732.

8

�Lindgren S, Odenstedt H, Olegard C, et al. Regional lung derecruitment afterendotracheal suction during volume- or pressure-controlled ventilation: astudy using electric impedance tomography. Intensive Care Med 2007;33:172–180.

This study demonstrates that lung derecruitment occurs after open-system en-dotracheal suctioning.

9

�Meier T, Luepschen H, Karsten J, et al. Assessment of regional lung recruit-ment and derecruitment during a PEEP trial based on electrical impedancetomography. Intensive Care Med 2008; 34:543–550.

During a PEEP titration maneuver, the authors were able to identify the start of lungcollapse using electrical impedance tomography to image regional changes in thelungs.

10

�Costa ELV, Chaves CN, Gomes S, et al. Real-time detection of pneumothoraxusing electrical impedance tomography. Crit Care Med 2008; 36:1230–1238.

This study shows that it is possible to detect small volumes of pneumothorax usingelectrical impedance tomography.

11 Hahn G, Just A, Dudykevych T, et al. Imaging pathologic pulmonary air andfluid accumulation by functional and absolute EIT. Physiol Meas 2006;27:S187–S198.

12 Bayford RH. Bioimpedance tomography (electrical impedance tomography).Annu Rev Biomed Eng 2006; 8:63–91.

13 Brown BH. Electrical impedance tomography (EIT): a review. J Med EngTechnol 2003; 27:97–108.

14 Harris ND, Suggett AJ, Barber DC, Brown BH. Applications of appliedpotential tomography (APT) in respiratory medicine. Clin Phys Physiol Meas1987; 8 (Suppl A):155–165.


C


15 Nopp P, Harris ND, Zhao TX, Brown BH. Model for the dielectric properties ofhuman lung tissue against frequency and air content. Med Biol Eng Comput1997; 35:695–702.

16 Adler A, Amyot R, Guardo R, et al. Monitoring changes in lung air and liquidvolumes with electrical impedance tomography. J Appl Physiol 1997;83:1762–1767.

17 Gattinoni L, Pelosi P, Vitale G, et al. Body position changes redistribute lungcomputed-tomographic density in patients with acute respiratory failure.Anesthesiology 1991; 74:15–23.

18 Borges JB, Okamoto VN, Matos GFJ, et al. Reversibility of lung collapse andhypoxemia in early acute respiratory distress syndrome. Am J Respir Crit CareMed 2006; 174:268–278.

19 Seagar AD, Barber DC, Brown BH. Theoretical limits to sensitivity andresolution in impedance imaging. Clin Phys Physiol Meas 1987; 8 (Suppl A):13–31.

20

�Wrigge H, Zinserling J, Muders T, et al. Electrical impedance tomographycompared with thoracic computed tomography during a slow inflation man-euver in experimental models of lung injury. Crit Care Med 2008; 36:903–909.

This study shows that an index of regional ventilation delay based on electricalimpedance tomography can be used to monitor lung recruitment.

21

�Deibele JM, Luepschen H, Leonhardt S. Dynamic separation of pulmonary andcardiac changes in electrical impedance tomography. Physiol Meas 2008;29:S1–S14.

This study describes technical aspects of a method capable of separatingpulmonary and cardiac changes in the electrical impedance signal, and appliesthe method to two healthy volunteers.

22 Villar J, Kacmarek RM, Perez-Mendez L, Aguirre-Jaime A. A high positive end-expiratory pressure, low tidal volume ventilatory strategy improves outcome inpersistent acute respiratory distress syndrome: a randomized, controlled trial.Crit Care Med 2006; 34:1311–1318.

23 Amato MB, Barbas CS, Medeiros DM, et al. Effect of a protective-ventilationstrategy on mortality in the acute respiratory distress syndrome. N Engl J Med1998; 338:347–354.

24 Suarez-Sipmann F, Bohm SH, Tusman G, et al. Use of dynamic compliancefor open lung positive end-expiratory pressure titration in an experimentalstudy. Crit Care Med 2007; 35:214–221.

25 Hinz J, Moerer O, Neumann P, et al. Regional pulmonary pressure volumecurves in mechanically ventilated patients with acute respiratory failuremeasured by electrical impedance tomography. Acta Anaesthesiol Scand2006; 50:331–339.

26 Beraldo MA, Reske A, Borges JB, et al. PEEP titration by EIT (electricimpedance tomography): correlation with multislice CT. Am J Respir CritCare Med 2006; 173:A64.


27 Borges JB, Costa ELV, Beraldo MA, et al. A bedside real-time monitor todetect airspace collapse in patients with ALI/ARDS. Am J Respir Crit CareMed 2006; 173:A377.

28

�Luepschen H, Meier T, Grossherr M, et al. Protective ventilation usingelectrical impedance tomography. Physiol Meas 2007; 28:S247–S260.

This study describes the integration of the center of gravity of ventilation imagesinto a fuzzy controller of mechanical ventilation.

29 Frerichs I, Dargaville PA, van Genderingen H, et al. Lung volume recruitmentafter surfactant administration modifies spatial distribution of ventilation. Am JRespir Crit Care Med 2006; 174:772–779.

30

�Hinz J, Gehoff A, Moerer O, et al. Regional filling characteristics of the lungs inmechanically ventilated patients with acute lung injury. Eur J Anaesthesiol2007; 24:414–424.

This study analyzes regional lung mechanics and shows that tidal recruitment andoverdistension coexist during mechanically patients.

31

�Wolf GK, Grychtol B, Frerichs I, et al. Regional lung volume changes inchildren with acute respiratory distress syndrome during a derecruitmentmaneuver. Crit Care Med 2007; 35:1972–1978.

This study identified lung derecruitment after endotracheal suctioning in children.

32

�Lindgren S, Odenstedt H, Erlandsson K, et al. Bronchoscopic suctioning maycause lung collapse: a lung model and clinical evaluation. Acta AnaesthesiolScand 2008; 52:209–218.

Study demonstrating lung collapse during bronchoscopy.

33 Beraldo MA, Costa ELV, Gomes S, et al. Detection of pleural effusion at thebedside by EIT. Am J Respir Crit Care Med 2007; 173:A791.

34

�Steinmann D, Stahl CA, Minner J, et al. Electrical impedance tomography toconfirm correct placement of double-lumen tube: a feasibility study. Br JAnaesth 2008; 101:411–418.

This study shows that electrical impedance tomography allows for detection ofmainstem bronchus intubation, but cannot fully replace bronchoscopy as a guideto one lung ventilation.

35

�Wagner PD. The multiple inert gas elimination technique (MIGET). IntensiveCare Med 2008; 34:994–1001.

Excellent review of the MIGET technique.

36

�Tanaka H, Ortega NRS, Galizia MS, et al. Fuzzy modeling of electricalimpedance tomography images of the lungs. Clinics 2008; 63:363–370.

Study describing a fuzzy modeling technique for heart and lung segmentationusing electrical impedance tomography.

37 Frerichs I, Hinz J, Herrmann P, et al. Regional lung perfusion as determined byelectrical impedance tomography in comparison with electron beam CTimaging. IEEE Trans Med Imaging 2002; 21:646–652.

38 Smit HJ, Vonk Noordegraaf A, Marcus JT, et al. Determinants of pulmonaryperfusion measured by electrical impedance tomography. Eur J Appl Physiol2004; 92:45–49.


Documents

revisãoeit TIE