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Dynamic EIT technology for real-time non-invasive monitoring of acute pulmonary embolism: a porcine model experiment

Respiratory Research volume 26, Article number: 7 (2025) Cite this article

Abstract

Background

Acute pulmonary embolism represents the third most prevalent cardiovascular pathology, following coronary heart disease and hypertension. Its untreated mortality rate is as high as 20–30%, which represents a significant threat to patient survival. In view of the current lack of real-time monitoring techniques for acute pulmonary embolism, this study primarily investigates the potential of the pulsatility electrical impedance tomography (EIT) technique for the detection and real-time monitoring of acute pulmonary embolism through the collection and imaging of the pulsatile signal of pulmonary blood flow.

Methods

A before-and-after self-control experiment was conducted on anaesthetised domestic pigs (N = 12, 20.75 ± 2.56 kg). The changes in pulmonary perfusion caused by an acute pulmonary embolism (artificially induced) were monitored in real time using the pulsation method. This enabled the extraction of indicators such as Amplitude, Forward (Negative) Slope, and SARC, which were used to assess the local pulmonary blood flow perfusion state. Furthermore, the degree of ventilation/perfusion matching in the lungs was evaluated concurrently with the analysis of the pulmonary ventilation area. Subsequently, a control verification was conducted utilising the conventional invasive hypertonic saline (5 ml 10% NaCl) contrast technique.

Results

The perfusion alterations subsequent to embolism in the pulsatility method are highly concordant with those observed in the hypertonic saline method, as evidenced by the imaging and indicator data. In particular, the perfusion area on the side of the embolism is markedly diminished, and the absolute values of all perfusion indicators are significantly reduced. Among these, Amplitude (P < 0.001) and SARC (P < 0.001) exhibit the most pronounced alterations. Furthermore, the extracted indicators from regional ventilation demonstrated notable discrepancies, the V/Q match% (P < 0.001) and Dead Space% (P < 0.001) exhibited the most pronounced sensitivity to alterations in acute pulmonary embolism. Subsequently, a control verification was conducted utilising the hypertonic saline method, which revealed a high degree of consistency between the two methods in the detection of acute pulmonary embolism (Kappa = 0.75, P < 0.05).

Conclusions

The EIT imaging method, which is based on the analysis of blood flow pulsation, has the potential to reflect in real time the changes in pulmonary blood flow that occur before and after an embolism. This provides a new avenue for the non-invasive real-time monitoring of patients with acute pulmonary embolism in a clinical setting.

Background

An acute pulmonary embolism is a sudden and unpredictable occurrence. It has been documented that the annual incidence of acute pulmonary embolism ranges from 75 to 269 cases per 100,000 individuals, with a mortality rate as high as 28% [1]. Consequently, It is the primary cause of death in clinical emergencies and critically ill patients. If it is not recognised in a timely manner, the optimal window of opportunity for treatment will be missed [2]. In clinical practice, a discrepancy between a deficit in pulmonary perfusion and pulmonary ventilation imaging is frequently the most crucial diagnosis factor for acute pulmonary embolism [1, 2].

Computed tomography pulmonary angiography (CTPA) remains the predominant diagnostic technique for pulmonary embolism in contemporary clinical practice [3]. Despite its high accuracy, this approach has inherent limitations, including the use of ionising radiation, cost, and bulkiness, as well as its inability to continuously monitor acute pulmonary embolism. Consequently, it is not a suitable option for bedside monitoring of critically ill patients.

Electrical impedance tomography (EIT) is a dynamic, safe, non-invasive, and emerging functional imaging technique. The fundamental tenet of this technique is the application of a specific frequency of safe current to an electrode array positioned on the surface of the body at the test site [4]. The resulting voltage change in the electrodes is then measured, thereby enabling the reconstruction of the impedance distribution across the body’s cross-section and the generation of images that illustrate these changes [5]. This technique has attracted considerable interest from research teams due to its a number of advantages, including high temporal resolution, non-invasive and harmless to the human body, portable equipment, and ease of use. At present, EIT technology is being utilized in exploratory research across a range of fields, including the monitoring of stroke, the examination of brain function, the monitoring of pulmonary ventilation status, and the testing of gastric function [6, 7]. It is worthy of note that the application of EIT in pulmonary ventilation status monitoring has gained significant recognition and is now being utilised in clinical practice [6].

Nevertheless, the clinical application of EIT technology in monitoring pulmonary perfusion remains a topic of ongoing investigation. At present, two principal methodologies exist for the monitoring of pulmonary perfusion through the utilisation of EIT technology. Exploratory work on EIT pulmonary perfusion monitoring was carried out by Tan and others, based on the principle that cardiac pulsations lead to pulsatile changes in the pulmonary vasculature [8]. However, due to the extremely weak changes in blood flow impedance, the accuracy requirements for the EIT acquisition system are extremely high, and the reliability of the results needs to be further improved [9]. In this regard, in order to enhance the accuracy of pulmonary perfusion monitoring results, He, H. W. and others initiated the study to utilise a “bullet” injection of a high-conductivity contrast agent (such as a 10% NaCl solution) through the central vein under respiratory depression to temporarily augment the impedance changes in the blood perfusion area [10]. Comparative research findings indicate a strong correlation between the saline method and PET and SPECT in terms of the results obtained in pulmonary perfusion function testing. Furthermore, the saline method has been shown to have superior capabilities in identifying acute pulmonary embolism, which has led to its preliminary acceptance in clinical practice [11, 12].

The team has a long-standing commitment to research on key technologies for EIT data acquisition and imaging. They have recently achieved a breakthrough in high-speed and high-precision EIT imaging data acquisition technology [13]. Building on this, the present study conducted research on a real-time non-invasive pulmonary perfusion status monitoring method based on pulmonary blood flow pulsation. The aim was to explore the feasibility of the pulsation method for acute pulmonary embolism monitoring.

Methods

Objects and instruments

Experimental animals

The experimental animals were 12 female anaesthetised domestic pigs. Their mean weight and mean chest circumference of the subjects were 20.75 ± 2.56 kg and 57.67 ± 3.26 cm, respectively, and they were 2.5-3.0 months old. The study was approved by the Animal Welfare and Ethics Committee of the Air Force Medical University.

EIT imaging instrumentation

In this study, we employed the EC-100 pro High-performance EIT system, which was developed by the research team in collaboration with UTRON Technology Co., Ltd., Hangzhou, China [13]. The system has a maximum acquisition speed of 100 fps and a data acquisition accuracy of better than 0.01‰ within the frequency range of 10 kHz to 250 kHz [13]. The data acquisition in this study was conducted via the opposing excitation and adjacent measurement operating mode, with each data acquisition parameter set to 16 electrodes, a frame rate of 40 fps, an excitation current amplitude of 1.25 mA, and a frequency of 50 kHz.

Animal models and experimental procedures

Anesthesia and instrumentation

The animals were fasted and deprived of water for 12 h, and their body temperature, respiration, and heart rate were confirmed to be within the normal range. The anaesthetic method was as follows: The anaesthetic was induced via intravenous injection of propofol (2 mg kg− 1), zoletil® 50 (2 mg kg− 1), and xylazine (1.5 mg kg− 1) via the ear margin. Tracheal intubation and connection to a respirator (Mindray WATO EX-65) were then performed, and the anaesthetic was maintained with isoflurane gas. The respiratory rate was set at 14 breaths per minute, the tidal volume was 250 ml, the ventilation rate was 3.5 L min− 1, and the airway pressure did not exceed 35 cmH2O. In the postoperative period, a central venous catheter was first introduced into the left internal jugular vein and secured in place with sutures. Subsequently, the introducer sheath (SafeSheath II introducer system, Pressure Products, Xi’an Kunlun Medical Technology Co., Ltd.) was placed into the left external jugular vein. Finally, a 7.5 F Swan-Ganz catheter (Edwards Lifesciences, Xi’an Kunlun Medical Technology Co., The catheter (Ltd.) was introduced into the atrium of the animal via the introducer sheath and allowed to float to a branch of the pulmonary artery, with the pressure waveform at the end of the catheter monitored to ensure the balloon remained in a contracted state. The experiment was conducted under the monitoring of an animal monitor (PHILIPS) and an animal non-invasive sphygmomanometer (MINDRAY) in order to ensure the safety and well-being of the subjects. The ventilator settings were modified throughout the surgical procedure in accordance with the physiological parameters, including the electrocardiogram, blood pressure, heart rate, and oxygen saturation. Once all the experiments had been completed, the animals were euthanised using an overdose of anaesthetic (20 mg kg− 1 propofol). (Please refer to the Supplementary Material 1 for details of the physiological information data.)

Experimental protocol

The animal’s chest and back hair were shaved and subsequently treated with alcohol and scrubs. Subsequently, the electrode tape, comprising 16 electrodes (EH-PET-16-CS, UTRON Technology Co., Ltd., Hangzhou, China), was positioned between the third and fourth ribs of the pig in an equally spaced manner, with the electrodes secured by self-adhesive bandages. Subsequently, the electrodes were then connected to the EIT device, which was used for data acquisition and image monitoring. (Please refer to the Supplementary Material 2, which contains an experimental diagram.)

The following assumptions are made in this study: (1) there is no difference in the position of the lung electrodes among all experimental animals; (2) there is no difference in the metabolic ability of 10% NaCl among all experimental animals; (3) there is no difference in the degree of pulmonary embolism among all experimental animals.

As illustrated in Fig. 1, the image guardianship experiment was divided into three stages:

(1) A controlled experiment was conducted to monitor lung ventilation and perfusion images in the normal state (pre-embolism).

The EIT data were collected continuously for approximately 30 min while all physiological indexes of the animals were maintained at a stable level. This was done in order to observe the impedance and image changes associated with the pulmonary ventilation process. Subsequently, the animals were placed in a state of end-expiratory pause through the administration of a minimal dose of the respiratory inhibitor propofol (2 mg kg− 1) and the regulation of the ventilator. The vascular beat EIT data were initially collected continuously for 10 to 20 cardiac cycles, and then a hypertonic saline contrast agent (5 ml of 10% NaCl solution) was injected via the central venous catheter. Next, the alterations in the electrical impedance of the lungs were continuously collected and observed for a period of 10 to 15 s. The entire procedure was conducted for approximately 40 to 45 s, after which mechanical ventilation was reinitiated.

(2) A controlled experiment was conducted to monitor lung ventilation and perfusion images in the state of acute pulmonary embolism (post-embolism).

Similarly, EIT data were collected continuously for approximately 30 min while all physiological parameters of the animal remained stable. Subsequently, the aerostat gasbag at the end of the catheter was inflated via the aerostat gasbag console of the Swan-Ganz floating catheter until the pulmonary artery pressure waveform on the monitor was no longer discernible. As in stage (1), 10 to 20 consecutive cardiac cycles of vasopulse EIT data were collected during end-expiratory pauses, followed by 10 to 15 s of hypertonic saline EIT data. Subsequently, the aerostat gasbag at the end of the catheter was released, thereby restoring the pulmonary artery pressure waveform to its normal state.

(3) Regional identification of acute pulmonary embolism (post-embolism).

The EIT data were collected continuously for approximately 10 min while all physiological parameters of the animals remained stable. Next, the aerostat gasbag at the end of the catheter was inflated until the pulmonary artery pressure waveform disappeared. Then, 5 ml of 10% NaCl contrast agent was injected through the distal port of the Swan-Ganz floatation catheter during the end-expiratory pause. The lung area where the contrast agent flowed into (i.e., corresponding to the area of the pulmonary artery embolism) was visualised by the EIT images.

Fig. 1
figure 1

Overall experimental flow chart. (A) Healthy phase (before pulmonary embolism occurs); (B) Pulmonary embolism phase (after pulmonary embolism occurs); (C) Pulmonary embolism location verification phase (after pulmonary embolism occurs)

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A new method for extracting pulmonary perfusion parameters based on the pulsatile method of EIT

Data processing and parameter extraction

Data Processing

The data processing procedure is comprised of two principal stages. Initially, a low-pass filter with a cutoff frequency of 5 Hz is employed to filter the boundary voltage signal, thereby obtaining measurement data that reflects the information about the changes in the lung over time. Subsequently, the Grazt consensus reconstruction algorithm for EIT (GREIT) imaging algorithm is utilised for image reconstruction [14]. The Adler group initially proposed this algorithm in 2009, subsequently applying it to pulmonary impedance imaging [14,15,16].

Accordingly, in order to examine the impact of pre- and post-pulmonary embolism alterations on conductivity, we calculated the rate of change of the average reconstructed conductivity across the entire lung based on each EIT image frame. This value represents the overall change in lung conductivity [17]. The formula is calculated as follows and is represented by the following equation:

$$ARC = (\sum\limits_{i = 1}^N {\Delta {\sigma _i}})/N$$
(1)

Where \(\:\varDelta\:{\sigma\:}_{i}\) denotes the rate of change related to the conductivity of the ith pixel in each frame of the EIT image, N denotes the total number of pixels, and ARC is in arbitrary units (a.u).

Following a pulmonary embolism, blood flow is primarily directed to the lung tissue on the non-embolised side. Consequently, a frame of EIT image is divided into two regions of interest (ROI), as illustrated in Fig. 2: the embolised region of interest in the embolised group (PE_ROIPE) and the non-embolised region in the embolised group of interest (PE_ROINPE). Similarly, two regions of interest were defined for the EIT images in the healthy group: the embolic region of interest in the healthy group (Normal_ROIPE) and the non-embolic region of interest in the healthy group (Normal_ROINPE). Subsequently, the ARCs corresponding to the aforementioned four regions of interest were calculated using Eq. (1) for the purpose of extracting pertinent indexes, such as perfusion, at a later stage. The selection of all regions of interest is based on a threshold of 40% of the maximum pixel value.

Parameter extraction

In this study, all beat-to-beat perfusion metrics were extracted from the waveform of the ARC. The extracted perfusion metrics were as follows: amplitude, maximum slope of the ascending segment, maximum slope of the descending segment, and area under the curve. These are illustrated in Fig. 2A. Finally, the mean value of the perfusion metrics of the consecutive M (M ≥ 5) cardiac cycles was obtained for analysis. The relevant formulae for calculating the aforementioned indexes are provided below:

Amplitude is defined as the difference between the crest and trough of the ARC waveform and is used to assess the maximum dilation of the arterial vasculature at blood perfusion pressure. In this context, “crest” denotes the peak of the wave, while “trough” denotes the bottom of the wave. Amplitude of the mth cycle is therefore given by the difference between the ARC crest and ARC trough, and is represented by the symbol (ARCcrest-ARCtrough)m. Finally, M denotes the total number of cardiac cycles. The formula is calculated as follows:

$$Amplitude = {(\sum\limits_{m = 1}^M {(AR{C_{crest}} - AR{C_{trough}}})_m})/M$$
(2)

Forward Slope is defined as the maximum slope of the rising segment of the ARC waveform, i.e. the rate of change of impedance values over time during the second half of the cardiac cycle. It assesses the volume of blood passing through the artery per unit of time (from trough to crest), and f denotes the number of frames. The formula is calculated as follows:

$$Forward\;Slope = (\sum\limits_{m = 1}^M \matrix{((\max {(\Delta ARC/\Delta f)_m}), \hfill \cr f \in (trough,crest + 1)) \hfill \cr} /M$$
(3)

Negative Slope is defined as the maximum slope of the descending segment of the ARC waveform, i.e., the rate of change of impedance values over time during the first half of the cardiac cycle. It assesses the volume of blood passing through the artery per unit of time (from crest to trough). The formula is calculated as follows:

$$Negative\;Slope = (\sum\limits_{m = 1}^M \matrix{((\max {(\Delta ARC/\Delta f)_m}), \hfill \cr f \in (crest,trough)) \hfill \cr} /M$$
(4)

SARC is defined as the area under the curve of the ARC waveform, which is used to assess the magnitude of total blood perfusion. The formula is calculated as follows:

$${S_{ARC}} = (\sum\limits_{m = 1}^M {(\int_{cres{t_m}}^{cres{t_{m + 1}}} {ARC{\kern 1pt} } df)})/M$$
(5)

V/Q indicators and the methods of extraction

Furthermore, ventilation/perfusion matching metrics were extracted through the analysis of lung ventilation regions, with the objective of assessing changes before and after pulmonary embolism.

Lung ventilation was evaluated by initially identifying the trough moments as background frames in the real-time acquisition of lung EIT data across all breaths, followed by EIT imaging for a single breath cycle. Subsequently, the EIT images of lung ventilation for 10 consecutive respiratory cycles were averaged. Ultimately, a threshold value of 25% of the maximum pixel value from the averaged lung ventilation EIT images was selected, with all pixel regions exceeding this value defined as lung ventilation regions, denoted as V [18].

Lung perfusion was evaluated by initially identifying the point at which the wave crest occurred as the background frame in the real-time acquisition of lung EIT data during all end-expiratory pauses, followed by one cardiac cycle of EIT imaging. Subsequently, the lung perfusion EIT images from 10 consecutive cardiac cycles were superimposed and averaged. Subsequently, the threshold value was set at 25% of the maximum pixel value from the averaged lung perfusion EIT images. All pixel regions exceeding this threshold value were defined as lung perfusion regions, denoted as Q [18].

Ultimately, the index regions for the lungs were extracted based on the matching of regional ventilation/perfusion ratios (V/Q) [19]. The V/Q match area, that is to say the area of both ventilation and perfusion, is denoted as RV+P. The Dead Space area, that is to say the area of lung ventilation only, is denoted as RV. The Shunt area, that is to say the area of blood perfusion only, is denoted as RP. The formula is as follows:

$$\eqalign{& {V \mathord{\left/{\vphantom {V Q}} \right.\kern-\nulldelimiterspace} Q}\;Match\% \cr & = {R_{V + P}}/({R_V} + {R_P} + {R_{V + P}}) \times 100\% \cr}$$
(6)
$$\eqalign{& DeadSpace\% \cr & = {R_V}/({R_V} + {R_P} + {R_{V + P}}) \times 100\% \cr}$$
(7)
$$\eqalign{& Shunt\% \cr & = {R_P}/({R_V} + {R_P} + {R_{V + P}}) \times 100\% \cr}$$
(8)

Extraction of lung perfusion indicators based on hypertonic saline method

Figure 2B illustrates a series of lung perfusion-enhanced contrast images obtained through the systematic administration of 5 ml of a 10% NaCl solution into the animal via a central vein during an end-expiratory pause [20]. In this case, as illustrated in Fig. 2B, the onset of the dilution curve was identified as the point of saline entry into the animal, designated as P0. One cardiac cycle following this was used as the point of saline entry into the pulmonary vasculature, marked as P1. The lowest point of global impedance was then taken as the endpoint of the initial saline passage through the pulmonary circulation, labelled as P2 [20].

Ultimately, the dilution curves of the P1-P2 interval were employed for the reconstruction of the EIT image sequence and the extraction of perfusion indexes, utilising the pulmonary impedance data at the moment of P1 as a reference point [20]. On this basis, the corresponding lung perfusion regions were selected and perfusion metrics, including amplitude, maximum slope and waveform area, were extracted based on the method proposed by Nguyen et al. [21]. The extraction of lung perfusion indexes and V/Q indexes was conducted in accordance with the pulsation method, with the objective of estimating the relative distribution of lung perfusion in the animal model of acute pulmonary embolism.

Fig. 2
figure 2

The division of the region of interest (black area) and the extraction of the corresponding region, ARC. (A) Schematic diagram of the extraction of ARC waveform indicators by the pulsation method, where crest indicates the peak, trough indicates the bottom, and crest + 1 indicates the second peak; (B) Schematic diagram of the extraction of ARC waveform indicators by the hypertonic saline method, where P0, P1, and P2 correspond to the perfusion points at different times during the hypertonic saline method

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Data analysis and statistics

The EIT data acquisition and online image reconstruction were implemented by the EC-100 pro system. The offline data analysis and image reconstruction were conducted in the Matlab R2022b (Mathworks, Natick, Massachusetts, USA) environment, based on EIDORS V3.10, with the imaging algorithm GREIT [22,23,24].

The pulmonary embolism models of 12 pigs were all successful, resulting in a total of 36 sets of valid data. These included 12 sets of control monitoring in the healthy state, 12 sets of control monitoring in the pulmonary embolism state, and 12 sets of monitoring in the validation state. The main effects were the PE-induced changes in pulmonary perfusion evaluation indexes and V/Q. The data were subjected to statistical analysis using SPSS 27.0 software. Given that the measured data did not adhere to a normal distribution, a Kruskal-Wallis test was performed, with P < 0.05 was considered statistically significant [25, 26].

Results

EIT imaging analysis of lung ventilation and pulmonary perfusion

A total of 12 groups of random acute pulmonary embolism models were obtained, comprising 8 groups of left embolism data and 4 groups of right embolism data. As illustrated in Fig. 3, the alterations in pulmonary blood flow perfusion corresponding to the healthy state, left embolism state, and right embolism state are presented, respectively, using EIT images (1 frame) at the trough moment.

The macroscopic results of the EIT images demonstrate that the embolism has a considerable impact on the distribution of pulmonary perfusion. Figure 3A-a illustrates the pulmonary blood flow perfusion monitored by the pulsation method in the healthy state. It can be observed that the blood flow perfusion on the left and right sides is adequately displayed, and the perfusion volume on both sides is essentially identical. As illustrated in Fig. 3B-a, the pulsatile method was employed to monitor the pulmonary blood flow perfusion in the embolism state on the left side. It was observed that the blood flow perfusion on the left side was markedly reduced or even absent. Additionally, 5 ml of 10% NaCl contrast agent was injected into the distal port of the Swan-Ganz catheter to determine the embolism area. This revealed that the embolism area was located on the left side (Fig. 3B-c). As illustrated in Fig. 3C-a, the pulsatile method is employed to monitor pulmonary blood flow perfusion in the embolism state on the right side, revealing a significant reduction or even complete absence of perfusion. Similarly, verification with hypertonic saline demonstrated that the embolism area was located on the right side (Fig. 3C-c). Subsequent control and verification with hypertonic saline revealed that the EIT images obtained with hypertonic saline were consistent with the macroscopic results of the pulsation method (Fig. 3A-b, B-b and C-b).

Fig. 3
figure 3

EIT lung perfusion imaging (selecting 1 frame at the moment of arrival at the trough). (A) Healthy state (before embolism); (B) Left-sided embolism state (after left embolism); (C) Right-sided embolism state (after right embolism). (a) Pulsatility method monitoring; (b) Saline method monitoring (saline injection via central venous catheter); (c) Saline verification (saline injection via Swan-Ganz catheter). V: corresponds to the actual Ventral side, R: corresponds to the actual Right side, D: corresponds to the actual Dorsal side, L: corresponds to the actual Left side

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Analysis of pulmonary perfusion indices

The statistics presented in the subsequent table were derived by extracting the perfusion metrics from disparate regions of interest in the healthy state (Normal_ROIPE and Normal_ROINPE) and the embolic state (PE_ROIPE and PE_ROINPE). Table 1 presents the variation of amplitude, maximum positive slope, maximum negative slope, and area under the ARC curve between the different regions of interest in the two states.

To further quantify the changes in perfusion indexes in pulmonary embolism, we conducted a statistical analysis of the perfusion indexes under ROIPE and ROINPE for healthy and embolism states, respectively. The results revealed a notable decline in the absolute values of all perfusion indexes in the embolism state, with the most pronounced reduction observed in ROIPE. Furthermore, it was observed that all perfusion metrics under ROIPE exhibited a statistically significant difference, whereas all perfusion metrics under ROINPE did not. The most significant decreases were observed in the Amplitude and SARC metrics, with Amplitude decreasing from 19.50 ± 1.75 to 8.29 ± 1.08 (P < 0.001) and SARC decreasing from 38.98 ± 0.59 to 10.06 ± 1.81 (P < 0.001).

Subsequently, the same method was employed to extract perfusion indices for different regions of interest in the healthy state (Normal_ROIPE and Normal_ROINPE) and the embolic state (PE_ROIPE and PE_ROINPE) under the hypertonic saline method (Supplementary Material 3). It was observed that the absolute values of all perfusion metrics in the embolic state exhibited a notable decline with embolism. Furthermore, all perfusion metrics under ROIPE demonstrated a statistically significant difference, whereas all perfusion metrics under ROINPE did not. The Amplitude under ROIPE decreased from 80.62 ± 6.49 to 48.01 ± 5.88 (P < 0.001), and the SARC decreased from 81.74 ± 9.04 to 51.18 ± 5.41 (P < 0.001).

Table 1 Results of the Pulsatility Method of evaluating perfusion indicators (in a.u.)
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V/Q analysis of indicators

A combination of pulmonary ventilation was analyzed in order to ascertain the ventilation-blood flow ratios in the healthy state, the state of left-sided pulmonary embolism and the state of right-sided pulmonary embolism. As illustrated in Fig. 4, the first row depicts pulmonary ventilation in distinct states, the second row depicts pulmonary perfusion monitored by the pulsatile method and hypertonic saline method in varying states, and the third row depicts the V/Q-matched images corresponding to the different states.

From the images, it can be observed that there is a significant dysregulation in the V/Q ratio in the presence of pulmonary embolism, when compared to the ratio observed in a healthy state. Figure 4A depicts the healthy state, Fig. 4B depicts the state of left pulmonary embolism, and Fig. 4C depicts the state of right pulmonary embolism. Furthermore, concordance was observed between the pulsatile method and hypertonic saline method in the regions exhibiting dysregulation in both the left and right embolism states. Specifically, dysregulation of the left V/Q was evident in the left embolism state (Fig. 4B), while dysregulation of the right V/Q was observed in the right embolism state (Fig. 4C).

In order to facilitate a more intuitive comparison of the V/Q data between the healthy and embolism groups, we extracted and subjected the data to statistical analysis across all states, as illustrated in Table 2. There was a notable decline in the V/Q match% in the embolism group, from 72.44 ± 1.38 to 35.24 ± 2.48 (P < 0.001). Concurrently, there was an increase in DeadSpace% from 24.73 ± 1.38 to 62.89 ± 2.21 (P < 0.001). Additionally, there was an observed rise in Shunt% from 2.83 ± 0.87 to 1.87 ± 0.46 (P < 0.05). In contrast, the V/Q in the hypertonic saline method exhibited a similar alteration in both the healthy and embolic states as observed in the pulsatile method. This is primarily evidenced by the considerable reduction in the V/Q match% from 78.96 ± 1.78 to 47.86 ± 2.16 (P < 0.001). Concurrently, there was a significant increase in Dead Space% from 7.48 ± 1.54 to 41.10 ± 2.85 (P < 0.001), as well as a significant increase in Shunt% from 13.56 ± 1.46 to 11.04 ± 1.18 (P < 0.05). It can be concluded that the V/Q match% and DeadSpace% are more sensitive to changes in PE.

Fig. 4
figure 4

Images of the process of V/Q matching. (A) healthy state; (B) left embolic state; (C) right embolic state. The first row indicates lung ventilation; the second row indicates lung perfusion; the third row indicates V/Q. R: corresponds to the actual Right side; L: corresponds to the actual Left side

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Table 2 V/Q results of the pulsation method and hypertonic saline method (in a.u.)
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Agreement between the pulsatile method and the hypertonic saline method for evaluating the results of monitoring acute pulmonary embolism

The macroscopic results of the EIT images (Figs. 3 and 4) demonstrated that the pulsation method and the hypertonic saline method exhibit comparable variability in imaging for monitoring acute pulmonary embolism. Furthermore, the extraction of perfusion indexes and V/Q demonstrated comparable results between the pulsatile method and the hypertonic saline method, indicating a similar capacity to differentiate between healthy and embolic states.

To further investigate the concordance between the pulsatile method and the hypertonic saline method in monitoring the results of acute pulmonary embolism, a Kappa concordance test was conducted on the results of the two methods [27]. The test was based on the criteria for diagnosing pulmonary embolism proposed by He et al. [28, 29]. The DeadSpace%≥30% implies a significant increase in dead space, which is considered to be a characteristic manifestation of pulmonary embolism and other pulmonary vascular embolic lesions (Table 2). The table show that the data for all embolised groups for both methods was greater than 30%. Furthermore, we defined the index V/Q match% (the difference between V/Q Match% in the healthy and embolic states). The diagnosis of acute pulmonary embolism was initially identified as positive when V/Q Match%≥30% and DeadSpace%≥30%, and negative when the opposite was true.

In light of the aforementioned evidence, the following results were obtained: In nine cases, both methods yielded positive results for pulmonary embolism. Conversely, in two groups, both methods returned negative results. Additionally, the pulse method alone produced a positive result in one group, while the hypertonic saline method alone yielded a positive result in none. The Kappa test yielded a consistency coefficient (Kappa) of 0.75, P < 0.05. This indicates that the two methods are highly consistent in monitoring acute pulmonary embolism.

Discussion

As the majority of patients with acute pulmonary embolism do not present with specific changes in signs at the onset of the disease, delayed management frequently results in fatality within a few hours [30, 31]. Consequently, prompt detection, diagnosis and treatment are crucial to improve symptoms and outcomes [32]. In this study, we proposed and established a non-invasive evaluation index of pulmonary perfusion status based on vascular beat-to-beat imaging using a high-speed and high-precision EIT imaging system recently established by our team. We then investigated the feasibility of this index for acute pulmonary embolism monitoring through animal model experiments. To the best of our knowledge, this is the inaugural attempt to utilize pulsatility method EIT for bedside non-invasive monitoring and identification of acute pulmonary embolism in an animal study.

This study compared and observed the differences in the changes of pulmonary EIT images and related indicators based on the pulsation method between the healthy group (before embolism) and the embolism group (after embolism). The following conclusions were drawn: (1) In the region of interest for embolism, all EIT perfusion parameters demonstrated notable alterations in comparison to the baseline. Among these parameters, Amplitude and SARC exhibited the most pronounced changes in the embolism group, with a considerable decline. (2) In the embolism group (after pulmonary embolism), all V/Q parameters exhibited significant changes in relation to the baseline. Among these parameters, V/Q match% and DeadSpace% exhibited a notable reduction.

Furthermore, the selection of an appropriate animal model is of paramount importance to ensure the reliability and meaningfulness of experimental results [33]. A prevalent model of acute pulmonary embolism is primarily established in animals through the injection of autologous thrombus via a central venous catheter [34]. However, this model requires the number of thrombi to be controlled by targeting pulmonary artery pressure, and the severity and extent of embolism cannot be well controlled, thus making it difficult to meet the needs of the present study [35]. Accordingly, this study investigated the potential of a Swan-Ganz balloon catheter to serve as a model for pulmonary embolism [36,37,38]. This approach offers the advantage of accurately localising the embolism while enabling the degree of embolism to be controlled by the volume of air injected, thus facilitating the acquisition of more stable experimental data on acute pulmonary embolism. It is important to note, however, that the practicality of this model still requires verification through further experimentation, which represents a limitation of this study [39, 40].

Strengths and consistency analysis

This study was conducted by our team following the development of a high-speed and high-precision data technology, whereby EIT data were collected by a 16-electrode EIT system (EC-100 pro) developed by our group [41, 42]. This system addresses the challenge of acquiring the blood vessel pulsation signal and provides a hardware platform for this study [41, 42].

The pulsatility EIT technique proposed in this study offers two principal advantages: (1) The entire experimental process is subject to continuous monitoring in real time. The pulsed method allows for the real-time monitoring of acute pulmonary embolism, as well as the location of the embolism, which can be used to determine whether or not an embolism is present. (2) The entire experimental process is entirely non-invasive, which is of great significance for real-time non-invasive pulmonary blood flow monitoring of critically ill patients, and also provides a foundation for experimental research into the clinical application of this technique.

Furthermore, in this study, the saline method (invasive) was employed as a control to validate the precision of the pulsatile method (non-invasive) for monitoring pulmonary embolism. The consistency of the results obtained from the two methods was then evaluated using the Kappa test. The results of the two methods were found to be highly consistent in the detection of acute pulmonary embolism. However, one set of data exhibited inconsistency, which may be attributed to inter-individual variations in pigs, resulting in disparate rates of salt metabolism and the subsequent emergence of hypernatremia, which influenced the outcomes of the hypertonic saline method. Additionally, it is noteworthy that given the fundamental differences between the two monitoring techniques for pulmonary perfusion, namely the pulsatility method and the saline method, a direct comparison of extracted parameter data is inherently may be more susceptible to error.

Limitations

It should be noted that the present study is not without limitations. The primary limitations are as follows: (1) During the experiment, 10% NaCl solution was injected into the animals on multiple occasions. Despite the animals being allowed to wait for 30 min following each injection, it is unclear whether this waiting period is sufficient for the animals to metabolize all the NaCl solution. Additionally, the extended waiting period may result in alterations to the physiological parameters of the anesthetized pigs. (2) The hypertonic saline method was employed solely for the purposes of embolization validation and control of lung perfusion indexes, and imaging tools such as CTPA were not utilized for comparative studies. Although this was done because CTPA requires the injection of a contrast agent with a certain amount of ionizing radiation when detecting pulmonary embolism, excessive injection of this contrast agent will affect the state of hematological pulmonary perfusion and thus lead to differences in the monitoring results. (3) Although the model of acute pulmonary embolism established in the present study controlled the location and extent of the actual embolism to a large extent, it is difficult to rule out the effects due to the difference between individual differences such as thickness and size of pulmonary artery vessels, etc.

Conclusions

The results of animal experiments demonstrate that the established EIT method provides preliminary insight into the perfusion difference between healthy and embolic states. This completely non-invasive image monitoring method is anticipated to offer patients a valuable bedside monitoring technology, enabling the real-time monitoring of pulmonary embolism and also providing new insights into the potential applications of EIT technology in the monitoring and early warning of lung diseases.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

SPECT:

Single photon emission computed tomography

EIT:

Electrical impedance tomography

PET:

Positron emission tomography

ROI:

Region of interest

CTPA:

Computed tomography pulmonary angiography

PE:

Pulmonary embolism

ARC:

Average reconstructed conductivity

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Acknowledgements

I would like to express my gratitude to Zuyu Che for their invaluable support in enabling me to pursue my work.

Funding

This work was supported by the Key Research and Development Projects of the Science and Technology Committee (2022YFC2404803); the Key Basic Research Projects of the Basic Strengthening Plan of the Science and Technology Committee (2019-JCJQ-ZD-115-00-02); National Natural Science Foundation of China-General Program (52477237); the National Natural Science Foundation of China under Grant (No.52207008).

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Author notes

  1. Junyao Li, Mingxu Zhu and Yitong Guo contributed equally to this work.

Authors and Affiliations

  1. Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Department of Biomedical Engineering, Air Force Medical University, Xi’an, 710032, China

    Junyao Li, Mingxu Zhu, Yitong Guo, Yu Wang, Benyuan Liu, Weice Wang, Zhenyu Ji & Xuetao Shi

  2. Department of Ultrasound Diagnosis, Tangdu Hospital of Air Force Medical University, Xi’an, 710038, China

    Yitong Guo

  3. Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Air Force Medical University, 569 Xinsi Road, Xi’an, Shaanxi, 710038, China

    Weichen Li

  4. Department of Cardiovascular Surgery, Xijing Hospital, Xi’an, China

    Qing He & Yang Liu

  5. Institute of Medical Research, Northwest Polytechnical University, Xi’an, 710072, China

    Yu Wang

  6. Biomedical Engineering, Department of Engineering, City University of Hong Kong, Kowloon, Hong Kong, China

    Yuxuan Liu

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Contributions

JL, MZ, YG, WL, QH, YW, YL, BL, YL, WW, ZJ and XS contributed substantially to this research and manuscript from conception to final preparation of the article, including the experiment design, data analysis, and the writing of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Zhenyu Ji or Xuetao Shi.

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Li, J., Zhu, M., Guo, Y. et al. Dynamic EIT technology for real-time non-invasive monitoring of acute pulmonary embolism: a porcine model experiment. Respir Res 26, 7 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12931-024-03090-9

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Respiratory Research

ISSN: 1465-993X

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