A Review on Electrical Impedance Tomography for Pulmonary Perfusion Imaging

A Review on Electrical Impedance Tomography for

Pulmonary Perfusion Imaging

D T Nguyen1, C Jin1, A Thiagalingam2 and A L McEwan1

1 School of Electrical and Information Engineering, The University of Sydney, New

South Wales, Australia2 Department of Cardiology, Westmead Hospital, New South Wales, Australia

E-mail: [email protected]

Abstract.

Although Electrical Impedance Tomography (EIT) for ventilation monitoring is on

the verge of clinical trials, pulmonary perfusion imaging with EIT remains a challenge,

especially in spontaneously breathing subjects. In anticipation of more research on

this subject, we believe a thorough review is called for. In this article, findings related

to the physiological origins and electrical characteristics of this signal are summarised,

highlighting properties that are particularly relevant to EIT. Perfusion impedance

change signal is significantly smaller in amplitude compared with the changes due to

ventilation. Therefore, the hardware used for this purpose must be more sensitive and

more resilient to noise. In past works, some signal or image processing methods have

been required to separate these two signals. Three different techniques are reviewed

in this article, including the ECG-gating method, frequency domain filtering based

methods and a principal component analysis based method. In addition, we review a

number of experimental studies on both human and animal subjects that employed EIT

for perfusion imaging, with promising results in the diagnosis of Pulmonary Embolism

(PE) and pulmonary arterial hypertension as well as other potential applications. In

our opinion, PE is most likely to become the main focus for perfusion EIT in the future,

especially for heavily instrumented patients in the Intensive Care Unit (ICU).

Keywords: electrical impedance tomography, EIT, cardiac changes, pulmonary perfusionSubmitted to: Physiol. Meas.

EIT for Pulmonary Perfusion Imaging 2

1. Introduction

Electrical Impedance Tomography (EIT) is a promising medical imaging modality, which

typically uses a single ring of external electrodes to image the impedance changes

within the body. First developed as a method for medical imaging in 1987 (Barber &

Brown 1986), EIT has been extensively studied and shown to have the ability to detect

both physiologic and pathophysiologic changes of regional pulmonary ventilation. This

application of EIT has been described as being on the verge of clinical application for

pulmonary monitoring with medical device manufacturers offering 2D imaging systems

for trials (Bodenstein et al. 2009).

EIT is a non-invasive, radiation free and portable imaging technique, suitable

for bedside use, especially in the ICU. Nonetheless, to infer the internal impedance

distribution from limited surface measurements is a severely ill-posed inverse problem,

which is still an active research area of applied mathematics (Borcea 2002). The EIT

reconstruction problem can only be solved numerically and is very computationally

expensive. EIT has very low spatial resolution compared with other imaging

techniques such as MRI or CT. It is also prone to noise, artefacts and anatomical

distortion. Furthermore, with current technology, EIT is only capable imaging

difference in impedance and not absolute impedance (Holder 2005). Functional EIT

(fEIT) is a technique that measure the changes in impedance between the current

state and a baseline state. Consequently, two different physiological states can be

misinterpreted to be similar if measured against different baselines. A standard

for interpretation/evaluation of fEIT images has not yet been established (Moerer

et al. 2011) to avoid this problem. Besides, although three dimensional EIT has

recently become possible, most commercial EIT systems are two-dimensional, limiting

the observable changes to one (axial) plane. With its pros and cons, although EIT has

not made it to the bedside, the technique has the potential to become the much needed

portable bedside pulmonary monitoring device for critically ill patients in the ICU.

From a physiological point of view, pulmonary perfusion is as important as lung

ventilation as both elements are necessary to sufficiently describe the regional behaviour

of lung tissues for applications such as the adjustment of ventilator settings (Frerichs

et al. 2009). As the EIT signal of the thorax contains both of the ventilation and

perfusion information, it is possible to independently and concurrently detect regional

changes due to these two processes. However, the significantly smaller amplitude of

the perfusion impedance change signal compared to the respiratory impedance change

signal renders perfusion mapping very difficult.

With the recent success of ventilation EIT, we feel that it is reasonable to

expect more studies in the future dedicated to pulmonary perfusion EIT. This review

attempts to focus specifically on engineering problems that are inherent to pulmonary

perfusion EIT and its clinical applications. Regarding the general principles of EIT,

its instrumentations, procedures and challenges, we refer the readers to the excellent

reviews by Brown (2003), Holder (2005), Bodenstein et al. (2009) and Moerer et al.


(2011).

This review is organised as follows. Section 2 briefly looks at different aspects of the

perfusion signals. Consequently, section 3 summarises technical aspects of EIT that are

applicable to perfusion measurements and focuses on the signal processing techniques for

separating the respiratory- and perfusion-related signals. Section 4 summarises some

of the clinical studies. Finally, section 5 discusses the future of EIT for pulmonary

perfusion imaging in clinical practice and suggests the possible improvements which

may benefit pulmonary perfusion EIT.

2. The pulmonary perfusion EIT signal

Physiologically, the term “pulmonary perfusion” refers to the delivery of arterial blood

to pulmonary capillary bed. In EIT, most studies measure the impedance changes due

to the pulsatile components of pulmonary flow, which in turn, is related to cardiac

activities (Frerichs et al. 2002). The characteristics of this signal is discussed in depth

in section 2.1. The recent work by Borges et al. (2011) proposed the measurement of

another perfusion impedance signal that reflects the regional distribution of blood flow

in the lung. Their findings on this type of perfusion impedance signal is discussed in

section 2.2.

2.1. The pulsatile pulmonary perfusion impedance signal

The relationship between cardiac activities and the pulsatile pulmonary perfusion

impedance changes has been observed to be characterised by the following events. After

ventricular systole, pulmonary arteries are filled with blood, resulting in a decrease in

impedance in the lung because blood has much lower electrical impedance (1.5 Ωm)

than lung tissues (7 Ωm (expiration) and 23 Ωm (inspiration)) (Eyuboglu et al. 1989).

As venous return starts, blood volume in the pulmonary capillary drops, leading

to increase in impedance. Toward the end of diastole, blood volume in pulmonary

capillary is at its minimum while impedance is at its maximum. Following a ventricular

systole, characterised by the R wave of an ECG recording, the increase of blood in the

pulmonary vessels again marks the decrease in impedance. This pulsatile characteristics

of pulmonary perfusion can be seen in Figure 1.

Though cardiac activities are responsible for the general shape of the pulsatile

perfusion impedance changes, the magnitude of these changes was shown to be related to

the size of the pulmonary microvascular bed by Smit et al. (2004). This same study also

concluded that the strength of the perfusion signal obtained by EIT was independent of

the stroke volume (SV) or cardiac output (CO). On the contrary, later results described

by Fagerberg et al. (2009a), showed that the strength of this signal was “significantly

correlated” with a wide range of SV values. Interestingly, the latter authors made

no comments or comparisons between their findings and that of Smit et al. (2004).

The mixed results from these two studies imply more in-depth studies in this area are


necessary.

Vonk Noordegraaf et al. (1998) studied the perfusion EIT signal in four different

groups of subjects: subjects with no cardiac or respiratory history (1) under normal

conditions and (2) while breathing hypoxic gas mixture (12% O2, 5% CO2 and 83% N2),

(3) patients with emphysema and (4) patients with atrioventricular dissociation with

external pacemakers. All four groups showed different perfusion EIT signal profiles (see

Figure 2). The significantly lower amplitude of impedance changes in emphysematous

patients was, reportedly, the result of a loss of alveolar capillaries and structure changes

in muscular pulmonary arteries, both of which were confirmed with morphometrical

analysis of lung tissues acquired at autopsy. Notably, they ruled out the effect of SV

and distensibility of right pulmonary artery based on their analysis on data obtained

from MRI measurements (Vonk Noordegraaf et al. 1998). Furthermore, the lack of the

inflection point (∆Zdia) in the perfusion impedance change measured on patients with

atrioventricular dissociation suggested that the inflection point of the impedance profile

in normal subjects is due to the back flow of blood into the pulmonary artery vein caused

by the high pressure of the left ventricle during systole (Vonk Noordegraaf et al. 1998).

2.2. Perfusion impedance change due to regional blood flow

Borges et al. (2011) proposed a novel method to determine relatively determine regional

blood flow based on impedance change. They reportedly used a bolus of hypertonic

sodium chloride (20 %) as impedance contrast. An algorithm was then performed to

follow the first-pass kinetics of this impedance contrast bolus to quantitatively measure

the relative regional blood flow in the lungs and heart. In an experiment performed

on 6 piglets, the results yielded by this method was compared with SPECT scans

for pulmonary perfusion of the subjects. Regional blood flow measured by EIT using

this novel method, in both healthy and injured lung cases, is highly correlated to that

measured by SPECT.

In the same study, pulsatile pulmonary perfusion impedance EIT was also measured

on the same subjects. The perfusion images obtained with this method did not correlate

well with the SPECT scans, especially in the cases of injured lungs. This could

be because the pulsatile impedance signal is ”strongly influenced” by the size of the

pulmonary microvascular bed (Smit et al. 2004) (Borges et al. 2011) while the regional

flow of blood as measured with the first pass kinetic method is not. Nonetheless,

the pulsatile perfusion impedance change in Borges et al.’s (2011) study is acquired

by averaging over 50 cardiac cycles during a short (20s) apnea while most studies on

pulsatile perfusion impedance change averaged over more than 100 cardiac cycles (see

Section 3.1.1).

Furthermore, the choice of the contrast agent was not justified by Borges et al.

(2011). Frerichs et al. (2002) successfully used a much less concentrated solution of

sodium chloride (5.85%) as the impedance contrast agent. The authors reported that

regional perfusion measurements obtained with this method were comparable with data


obtained with electron beam CT (Frerichs et al. 2002). Therefore, it is questionable

why Borges et al. (2011) used such highly hypertonic solution. Additionally, the total

volume of the 20% NaCl solution was not stated by Borges et al. (2011). The use of

a large quantities of this highly hypertonic solution is likely to be a limitation of the

technique as it may precipitate heart failure.

Although promising, the first-pass kinetic method for regional blood flow

monitoring requires further confirmatory studies. The bulk of previous work was done

measuring the pulsatility of the perfusion impedance signal. Therefore in the remainder

of this review, we focus on the pulsatile impedance change as the perfusion related

signal.

3. Technical aspects of pulmonary perfusion EIT

The large majority of perfusion studies reported using 50 kHz alternating current with 5

mA peak-to-peak amplitude. Leathard et al. (1994) used 20kHz current. More recently,

Borges et al. (2011) used 5-12 mA currents at 125 kHz. Furthermore, in an in vivo

study of pulmonary perfusion and ventilation impedance changes in the human thorax

by Brown et al. (1994), it was found that the mean cardiac-related impedance changes

decreased at higher frequencies and exhibited a relaxation frequency fr of 86.8 kHz.

The respiratory impedance change also decreased in magnitude at higher frequency,

but interestingly, less than the cardiac-related impedance change. Their findings were

supported by Nopp et al.’s (1996) study. Despite the fact that multi-frequency EIT

systems have become available (McEwan et al. 2007), it has never been established

which frequency should be used for this application. It follows that the choices of

frequencies reported in published works have been seemingly arbitrary.

3.1. Signal processing techniques to separate perfusion and ventilation components in

EIT signals

Cardiac-related impedance change magnitude is reported to be as much as 3% while

the impedance of the lung changes as much as 300% between maximal expiration and

maximal inspiration in dogs (Eyuboglu et al. 1989). Since EIT is very sensitive to noise,

the lower magnitude of the cardiac-related impedance changes directly results in a lower

signal-to-noise ratio. In fact, EIT measurements on the chest of spontaneously breathing

and ventilated subjects showed only the respiratory component, as shown in Figure 3.

This motivated the development of signal and image processing techniques to separate

these two components.

The advances and flexibility of new instruments allows the development of

much more sophisticated signal processing techniques for the separation between

ventilation and perfusion signals. In order of ascending sophistication, these techniques

include ECG-gated acquisition with averaging, frequency domain filtering and principle

component analysis (PCA).


3.1.1. ECG-gated acquisition of EIT data This method was first suggested by McArdle

et al. (1988). It is based on two important facts. Firstly, the pulsatile component of

perfusion signal in the lung is in synchrony with cardiac activity and therefore, the

ECG. Secondly, the ventilation changes in impedance occurs at a much lower rate

compared to the ECG. Thus, by recording the changes of impedance during the cardiac

cycle, triggered by a separated ECG module, the lung perfusion signal can be recorded.

Furthermore, it has been shown in a large number of studies that averaging the results

over more than 100 cardiac cycles can reduce the interference of respiration. Eyuboglu

et al. (1989) and Smit et al. (2006) averaged their measurements over 100 cardiac cycles

while Vonk Noordegraaf et al. (1998), Kunst et al. (1998), Smit et al. (2003a) and Smit

et al. (2003b) averaged data over 200 cardiac cycles.

ECG-gated acquisition demands higher frame rates from the EIT instrument to

capture the changes within a cardiac cycle. With the new generation of EIT systems, this

is not a problem. Nonetheless, this technique suffers from several drawbacks. Firstly, the

averaging over a large number of cardiac cycles requires long duration of data acquisition

and therefore may not be suitable for continuous bedside monitoring. Furthermore, as

Deibele et al. (2008) pointed out, this averaging over a large data set could result in

“sudden changes or irregular anomalies cannot be detected.” In addition, with this

method of acquisition, the ventilation component of the signal is lost, which is certainly

not the optimal solution.

3.1.2. Frequency domain filtering based methods These methods are based on the fact

that the heart rate and the respiratory rate are very well separated in frequency. The

Fast Fourier Transform (FFT) of the global signal can provide a good approximation of

the required cut-off frequency for filtering, as shown in Figure 3. Consequently, a wide

range of FIR or IIR filters can be implemented as a high pass filter to extract the cardiac-

related component from the mixed signal. Similarly, the respiratory component of the

signal can be extracted using a low pass filter, possibly with the same cut-off frequency.

This filtration process can be carried out on the voltage signals or the reconstructed

images (Zadehkoochak et al. 1992, Leathard et al. 1994). FIR filters are preferable for

physiological measurements since it preserves the phase component while IIR filters may

distort the phase of the signal.

In spontaneously breathing subjects, the respiratory impedance changes are rarely

sinusoidal, leading to frequency components across a wider range of spectrum than

anticipated. Therefore, it is possible that some of these frequency components can

occur in the heart-rate domain of the spectrum. Consequently, after high pass filtering,

some of these respiratory components can still be observable together with the cardiac-

related signal. To solve this problem, Frerichs et al. (2009) and Grant et al. (2011)

used frequency domain filtering to first separate components in the heart-rate domain

and respiratory rate domain, followed by linear regression fit to identify the regions of

interests (ROIs). Whilst Frerichs et al. (2009) used mechanically ventilated subjects

to ensure no overlapping in the frequency spectrums of the ventilation and perfusion


components of the acquired signal, Grant et al. (2011) successfully separated the two

components in spontaneously breathing subjects by combining the phase component of

the signals with the algorithm suggested by Frerichs et al. (2009) .

3.1.3. Principle component analysis based method A PCA based method for separating

the respiratory and perfusion-related signal was suggested by Deibele et al. (2008). Their

method successfully separated the two signals, allowing continuous beat-to-beat updates

after a 20-second period of initiation. Time domain filtering with template functions

was used to initially separate the cardiac and ventilation signal. The results were then

combined to obtain the best fit with the original signal, computed by performing Least

Mean Square (LMS) between the approximation and the actual signal. In order to

achieve proper separation of the respiratory and cardiac-related signals, the template

functions for time domain filtering were identified by performing PCA on the original

data matrix.

A recent paper by Pikkemaat & Leonhardt (2010) provided a review on each of

the aforementioned techniques and more importantly, compared the results achieved by

each of these approaches in their experiment. Their results showed that PCA and the

frequency domain filtering methods produced highly correlated results. On the other

hand, the correlation between these two methods with the ECG-gating alternative was

reported considerably less. However, the correlations between each of these methods

and a known standard method for perfusion imaging were not computed, rendering the

results from their study inconclusive regarding as to which method is more reliable.

3.2. Perfusion signals and cardiac signals

Once separated from the ventilation impedance change, the perfusion impedance signal

suffers from interferences of the impedance change of the heart. The cardiac impedance

change is comparable in magnitude to the perfusion impedance change. However, the

heart movements, deformation of structures, etc. during a cardiac cycle makes it much

harder to remove this type of interference. In practice, the perfusion signals are usually

taken at the level of the third intercostal space with subjects lying in the supine positions

and arms lifted over their head in order to further the distance between the measurement

plane and the heart.

Nonetheless, the interference from the heart is never completely removed. This is

even more problematic in patients with enlarged hearts. Smit et al. (2003) proposed

and tested three different techniques to record perfusion impedance by defining a ROI

of whole circle (entire image), half circle to exclude the half circle that the heart signal

presents and a ”contour” ROI where the contour is the optimised separation between

the two regions of the image where impedance increases and impedance decreases. The

authors commented that the ”contour” ROI method works best to remove interferences

from the heart impedance. Frerichs et al. (2009) used implemented this idea using

regional linear regression with some success with data measured at the third intercostal


space and at the sixth intercostal space, where the heart lies.

Studies comparing obtained perfusion signal using EIT with other known methods

such as EBCT (Frerichs et al. 2002) and radionuclide scanning (Kunst et al. 1998)

showed well correlated results. However, these two studies analysed their results

differently, Frerichs et al. (2002) only analysed two specific pixel left and right while

Kunst et al. (1998) analysed posterior left and right regions of images obtained at the

third intercostal level. Without a gold standard method for perfusion signal analysis, it

is hard to compare results between EIT perfusion studies and results between EIT and

other imaging modalities.

4. Clinical applications of pulmonary perfusion EIT

The ability to monitor pulmonary perfusion combined with the firmly established

ability of EIT for ventilation magnitude and distribution monitoring, undoubtedly offers

many advantages in the diagnosis and treatment of pulmonary and, to some extent,

cardiovascular diseases. Pulmonary perfusion EIT has been studied for a wide range of

potential clinical applications, including the treatments and/or diagnosis of pulmonary

embolism, acute lung injury, COPD (Chronic Obstructive Pulmonary Diseases) and

PAH (Pulmonary Artery Hypertension). Smit et al. (2005) provided a good review on

the studies on potential clinical applications of EIT of the thorax, including perfusion.

A number of studies have been attempted to detect perfusion defects caused by

pulmonary embolism using EIT. Pulmonary embolism (PE) is one or multiple blockages

of the pulmonary artery or its branches by emboli travelled through the bloodstream.

Acute PE is life-threatening, requiring immediate attention of medical professionals to

begin appropriate treatment such as anticoagulant and thrombolytic therapy (Frerichs

et al. 2002). PE can be diagnosed by combining the patient’s medical history and V/Q

(Ventilation/Perfusion) scans or CT pulmonary angiography. Both of these imaging

techniques expose patients to radiation and are not preferred especially for critically

unwell patients. EIT experiments to detect PEs by McArdle et al. (1988) and Leathard

et al. (1994) showed promising results. Both studies showed that EIT was able to detect

regions of the lung where perfusion was disrupted while ventilation remained normal,

indicating PE. However, the images published in their papers showed large emboli. A

more recent study in pigs, simulated emboli by inflating or deflating a catheter balloon,

showed the with the aid of a contrast agent, NaCl, injected intravenously, perfusion

distribution acquired by EIT was comparable to results obtained from Electron Beam

CT (Frerichs et al. 2002).

Fagerberg et al. (2009b) studied EIT in endotoxinaemic acute lung injury on porcine

models and found that EIT can be used to assess the heterogeneity of regional ventilation

and perfusion, as well as V/Q matching. They concluded that EIT may be valuable in

detecting early pathophysiological changes due to acute lung injury.

Chronic Obstructive Pulmonary Diseases, especially the lung emphysema type, is

characterised by the loss of both alveolar wall and pulmonary small blood vessels. Since


the small vascular bed in the lung is a large reservoir of blood, it is reasonable to

expect that it is largely responsible for the impedance changes due to cardiac activity.

Hence, EIT may be valuable in assisting the diagnosis and treatment of COPDs. A

study by Vonk Noordegraaf et al. (1998) showed that emphysematous patients have

significantly lower cardiac-related impedance changes in the lung than healthy patients.

Furthermore, a study in 7 healthy and 6 emphysematous subjects showed that EIT had

high sensitivity in detecting relaxation in hypoxic pulmonary vasodilation and therefore,

can be useful in determining whether oxygen therapy would be beneficial to a patient

with emphysematous COPD (Smit et al. 2003b).

Pulmonary Artery Hypertension is a rare disease, which mainly effects young adults.

It is characterised by high blood pressure in pulmonary arteries due to the loss of small

pulmonary arteries. The cause of this loss can be many, including intima thickening,

media hypertrophy and thrombosis in small vessels. The disease can be fatal, all the

more because its early symptom is gradual shortness of breath after exercises, which

can be easily mistaken for lack of physical fitness. In a study with 21 patients with

idiopathic PAH and 30 healthy control subjects, EIT on the patients revealed lower

perfusion impedance changes signals compared to the control group (Smit et al. 2006).

This study indicated EIT could potentially be used as non-invasive technique for PAH

diagnosis, which up to date, has only been done invasively (Smit et al. 2006).

Recently, using EIT, Grant et al. (2011) and Carlisle et al. (2010) studied the

perfusion and ventilation distribution and V/Q (ventilation/perfusion) ratio in adults

and preterm infants, respectively. They showed the effect of gravity on both adult

and neonates and more importantly, the difference in the lung function of mature and

immature lungs.

5. Conclusion

In summary, the physiological origin of the perfusion impedance change signal is varied

and not completely understood. Its pulsatile characteristics have been shown to be

related and therefore synchronous with the cardiac activities. The magnitude of

the pulsatile perfusion impedance change is significantly smaller than the ventilation

signals. Therefore various signal processing techniques have been developed to separate

the perfusion signal from the respiratory signal including the ECG-gating acquisition

method, frequency filtering methods and PCA-based method as well as the use of an

impedance contrast agent. Each of these methods have their own advantages however,

they also suffer from different drawbacks. Continuous and concurrent pulmonary

perfusion/ventilation monitoring in spontaneously breathing adult subjects was achieved

in recent studies by Deibele et al. (2008) and Grant et al. (2011). With the recent

advances in digital image and signal processing algorithms and the rapidly growing

processing power of computing units, we can expect to see more developments in

this area in the future. All in all, we have to recognise that up to now, there is no

gold standard for signal separation of pulmonary perfusion that fulfils all requirements


necessary. This circumstance should be taken into account when interpreting the EIT

derived perfusion signals, especially if the results of different studies are compared.

Furthermore, Borges et al. (2011) showed that with the aid of 20% NaCl bolus as

impedance contrast agent, the authors were able to obtain images of the impedance

change due to regional blood flow. Their results were highly correlated with regional

blood flow acquired from SPECT images. Their work promises a new horizon for

perfusion EIT. More confirmatory studies are required to define the clinical relevance

of this type of technique. An alternative impedance contrast agent is also required to

replace 20% NaCl as it may have negative effect on patients. It would also be beneficial

to study closely the nature of ”regional blood flow” impedance signal, how it is related (if

at all) to cardiac activities, pulmonary conditions and most interestingly, its relationship

with the well studied pulsatile perfusion signal.

EIT can be used to monitor pulmonary perfusion and has been studied for clinical

application such as the diagnosis of pulmonary embolism and PAH. It can also be used

to determine the applicability of oxygen therapy for patients with COPD, especially

the lung emphysematous type. Smit et al. (2003a) showed that pulmonary perfusion

measurements obtained with EIT are sufficiently reproducible for clinical practice.

In the future, the most likely application for EIT lung perfusion is probably

the diagnosis of pulmonary embolism, especially for heavily instrumented patients in

the ICU to whom the compactness and bedside suitability of EIT is most beneficial.

Furthermore, compared with current methods for pulmonary embolism detection,

EIT has the advantage of being radiation-free and non-nephrotoxic. However, this

application requires the method to have both high sensitivity and high specificity as

both false positive and false negative diagnosis can lead to fatality.

Other recent advances of EIT such as improved electrodes, 3D EIT, new image

reconstruction methods and current injection patterns have yet to be applied for

pulmonary perfusion study. These techniques can be used to improve the resolution

of perfusion image, which would make this technique more relevant in clinical settings.

Furthermore, studies by Brown et al. (1994) and Nopp et al. (1996) showed that multi-

frequency EIT measurements could potentially improve SNR of the perfusion impedance

signal.

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Figure 1. Synchrony between the change in impedance in the lung impedance and

ECG. Reproduced from Grant et al. (2011).


Figure 2. Pulmonary perfusion signals during cardiac cycle in four different groups

of subjects as a function of time. Reproduced with permission from Vonk Noordegraaf

et al. (1998). A: typical perfusion impedance profile in healthy subjects. B:

typical perfusion profile of healthy subjects while breathing hypoxic gas mixture. C:

typical perfusion impedance profile of patients with emphysematous lung. D: typical

perfusion impedance profile of patients with atrioventricular dissociation with external

pacemakers. The acquisition of signal started at the end of cardiac diastole.


Figure 3. Frequency spectrum of impedance changes in the thorax, obtained

from perform FFT on EIT measurements on a spontaneously breathing subject,

which shows two distinguish frequency peaks at the respiratory rate and heart

rate regions, respectively corresponding to cardiac-related and ventilatory-related

impedance changes. (a) the total impedance signal, (b) the FFT of the signal in

(a). Reproduced with permissions from Grant et al. (2011).

A Review on Electrical Impedance Tomography for Pulmonary Perfusion Imaging

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