Novel three-dimensional electrical impedance tomography for noninvasive detection of lung perfusion

Introduction

Pulmonary ventilation and perfusion greatly influence the oxygen-carbon dioxide exchange capacity. Ventilation-perfusion mismatch can lead to hypoxemia, indicating failure of pulmonary gas exchange (1). Therefore, assessing pulmonary ventilation and perfusion is important for our understanding of pulmonary function in physiological and pathological conditions. Classical imaging methods for pulmonary ventilation and perfusion assessment include radiocontrast-based computed tomography (CT) (2), single-photon emission computed tomography (SPECT) (3), positron emission tomography (PET) (4), etc. Magnetic resonance imaging (MRI)-based methods can also evaluate lung function, and some of these methods even avoid using contrast agents (5). However, the above methods have limited the widespread practice of pulmonary ventilation and perfusion assessment due to the need for contrast agents, longer examination times, or higher prices. Apart from MRI, none of these diagnostic imaging techniques are completely radiation-free. Thus, comprehensive, dynamic, and bedside-accessible assessment techniques are urgently needed.

Electrical impedance tomography (EIT) is a technique that has the potential function of measuring pulmonary ventilation and perfusion in a two-dimensional (2D) plane (6). The EIT technique operates by applying weak alternating currents and measuring the response voltages via electrodes positioned around the chest wall. Alterations to the measured voltages, induced mainly by the change of air and blood contents in the lung, can be converted to electrical impedance images through an image reconstruction algorithm. During respiration, the electrical impedance is mainly influenced by the ventilation process and, to a lesser extent, by pulmonary perfusion. Therefore, a signal extraction algorithm is necessary to separate the perfusion signals. EIT has been used in pulmonary ventilation and perfusion assessment in many clinical conditions. Longhini et al. (7) assessed the ventilation characteristics of success and failure of spontaneous breathing trials in patients at risk of extubation failure with the help of EIT ventilation parameters. Wang et al. (8) found that prolonged prone ventilation improved ventilation-perfusion matching and oxygenation in patients with acute respiratory distress syndrome (ARDS) via EIT assessment. Ma et al. (9) utilized EIT to assess regional lung function improvement in patients with chronic obstructive pulmonary disease (COPD) undergoing pulmonary rehabilitation. Indeed, the veracity of EIT ventilation images has been widely validated, whereas the methodology for assessing perfusion is still questioned. The currently established perfusion imaging method relies on the bolus injection of hypertonic saline through pulmonary circulation and breath holding (10,11). For patients with cardiac disease, this may lead to concerns about fluid overload. In addition, breath-holding for 20–30 seconds is difficult and unacceptable for most patients. Finally, 2D-EIT images show only one projection of ventilation and perfusion and may miss local functional abnormalities due to specific lesions.

Herein, we reported a novel three-dimensional EIT (3D-EIT) system for noninvasive monitoring of lung perfusion deficiency and reperfusion with a pulsatility signal-based algorithm. With the help of 3D-EIT technology, we acquired real-time and continuous whole-lung ventilation and perfusion images of subjects in normal and pathological states without bolus hypertonic saline injection and breath holding. More importantly, the purpose of this work is to validate the reliability, authenticity, and clinical consistency of this technique using three models: severe pulmonary bullae, Swan-Ganz catheterization, and acute pulmonary embolism. To our knowledge, this is the first time that EIT technology has been utilized for real-time noninvasive 3D lung perfusion imaging.

Methods

Ethics and enrollment

This preliminary study was conducted at Zhongshan Hospital and Shanghai Pulmonary Hospital (Shanghai, China). This study was approved by the Ethics Committee of Zhongshan Hospital Fudan University (No. 402) and the Ethics Committee of Shanghai Pulmonary Hospital (No. K24-631Y). All subjects signed a written informed consent. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. As shown in Figure 1, we included several different groups of patients with well-characterized pulmonary perfusion defects, including irreversible perfusion defects, reversible artificial perfusion defects, and reversible clinical perfusion defects.

Figure 1 Schematic diagram of patient enrollment. 3D-EIT, three-dimensional electrical impedance tomography.

3D-EIT examinations

The novel 3D-EIT system (Infivision 1900 Impedance Imaging System) was provided by Infivision Medical Imaging Technology Co., Ltd. (Beijing, China). This 3D-EIT system mainly includes an imaging computer, an EIT data acquisition module, equipment cables and electrode belts. The indications and contraindications for the use of 3D-EIT examination are detailed in Table 1. Unlike the 2D-EIT system, 3D-EIT uses two electrode belts, one placed at the infra-axillary level and the other placed at the xiphoid level for signal acquisition of the whole lung. Each electrode belt contains 16 electrodes. When the patient was tested, either in the supine or seated position, the experimenter fully exposed the subject’s chest and back and then bound the two electrode belts to the positions mentioned above and aligned the upper and lower electrode positions with each other. For better signal quality, the length of the electrode belt should accommodate the size of the subject’s thorax, and the conductive gel was applied to the skin-electrode contact area to enhance conductivity and skin fitting. After confirming that the signal quality was satisfactory through the imaging computer, the experimenter instructed the subject to breathe calmly, and the ventilation and perfusion signals were acquired for 3 to 5 minutes, depending on the real-time signal quality variations. Lung ventilation and perfusion images can be viewed in real-time on the imaging computer or exported for further analysis. Figure 2 shows the subject undergoing the 3D-EIT examination.

Figure 2 Illustration of a subject undergoing 3D-EIT test. (A) shows the patient with two chest belts and the real-time monitoring system. Clinicians can check the user interface (B) to obtain real-time ventilation and perfusion images and impedance waveforms. 3D-EIT, three-dimensional electrical impedance tomography.

Statistical analysis of EIT

Detailed image reconstruction methods of 3D-EIT ventilation and perfusion images have been described and published (12). Briefly, the measured EIT voltages were simultaneously influenced by ventilatory and cardiac activities. The ventilation-related signals were extracted by a 200-order low-pass finite impulse response (FIR) filter, the cut-off frequency of which was 2.5 times the respiratory rate. The cardiac-related signals were extracted by a band-pass filter, the lower and upper cut-off frequencies of which were 0.8 and 3.5 times the heart rate, respectively. Ventilation and perfusion images were reconstructed from the extracted ventilation- and cardiac-related signals, respectively, using a three-dimensional time-difference imaging algorithm (12). This technology can reconstruct ventilation and perfusion images in real-time and continuously without requiring patients to hold their breath and undergo bolus hypertonic saline injection. The EIT data processing procedure is briefly shown in Figure 3. The definitions of regions of interest (ROIs) and quantitative metrics are shown in the Appendix 1 and Table S1 in the supplementary data file.

Figure 3 The flowchart of EIT signal processing. EIT impedance signals are measured through a double-belt 3D impedance imaging system. Starting with the raw EIT measured signals, a signal separation process is initially executed to separate the ventilation- and perfusion-related signals. Subsequently, these separated signals are individually input into a 3D image reconstruction algorithm to generate 3D ventilation and perfusion images, respectively. 3D, three-dimensional; EIT, electrical impedance tomography.

Validation of EIT reconstructed pulsatility perfusion images—Swan-Ganz catheterization model

In patients with suspected pulmonary hypertension who were about to undergo Swan-Ganz catheterization, we placed and attached 3D-EIT electrode belts and continuously recorded ventilation and perfusion signals before catheterization, provided that contraindications were excluded. The patient was in the supine position during the entire operation. The catheter was passed through the internal jugular vein and through the right heart into the pulmonary artery branches. The baseline EIT signals were recorded for 3 to 5 minutes. Then, the Swan-Ganz catheter balloon was inflated (1.5 mL) until the pulmonary artery wedge pressure (PAWP) waveform appeared, and changes in EIT ventilation and perfusion images were recorded for 3 to 5 minutes. Finally, the Swan-Ganz catheter balloon was released, and changes in EIT ventilation and perfusion images were recorded for 3 to 5 minutes. Chest X-ray images were used to detect the position of the catheter tip. 3D-EIT ventilation and perfusion images at baseline, balloon wedged, and balloon release were reconstructed and subjected to detailed analysis.

Validation of EIT reconstructed pulsatility perfusion images—pulmonary embolism model

For patients with clinical suspicion of acute pulmonary embolism, we performed 3D-EIT at the same time as their admission for computed tomography pulmonary angiogram (CTPA) and/or isotope ventilation-perfusion scan (VQ scan). After the patients received treatment for pulmonary embolism, such as mechanical thrombectomy or intravenous thrombolysis, we performed 3D-EIT concurrently with CTPA or VQ scan to investigate whether perfusion images derived from 3D-EIT were consistent with both vascular recanalization on CTPA and perfusion images on perfusion scan, and its feasibility for pulmonary embolism assessment.

Results

Reconstruction of whole-lung ventilation and perfusion images with 3D-EIT

To demonstrate the feasibility of the novel 3D-EIT system for assessing whole lung ventilation and perfusion conditions, initially, we performed 3D-EIT on a 23-year-old healthy male subject in seated and supine positions. The coronal and transverse 3D-EIT ventilation and perfusion images showed the lung ventilation and perfusion regions well with excellent matching (Figure 4). Ventilation and perfusion signals are indicated in blue and red, respectively, and the higher color brightness represents a stronger signal. In the seated position, the coronal perfusion signal was stronger in the lower lungs compared with the supine position. For example, the right lower segmental perfusion signal in the sitting position accounted for 36.17% of the right lung vs. 32.92% in the supine position. More 3D-EIT transverse images and multi-view images are shown in Figures S1,S2.

Figure 4 The transverse and coronal 3D-EIT images of a healthy subject in the seated or supine position. The whole lung ventilation and perfusion images were successfully reconstructed. 3D-EIT, three-dimensional electrical impedance tomography.

Identification of perfusion defects in patients with pulmonary bullae using 3D-EIT

To confirm whether 3D-EIT could identify focal irreversible lung perfusion defects, we performed 3D-EIT on two patients with long-standing COPD and large pulmonary bullae. The first patient was a 63-year-old male with CT findings of solid lesion in the upper lobe of the left lung. Bedside 3D-EIT examination showed significant hypoperfusion of the left upper lung segment in this patient (21.93% of the left lung) (Figure 5A). The second patient was a 59-year-old male with intractable pain in the left shoulder with hoarseness and shortness of breath on movement. His CT scan revealed a para-mediastinal mass in the left upper lobe of the lung and also suggested emphysema in both lungs and giant pulmonary bullae formation in the right upper lobe (Figure 5B). Bedside 3D-EIT ventilation and perfusion images showed poor ventilation in the upper lobes of both lungs and severely diminished perfusion signal in the right upper lobe (12.82% of the right lung) (Figure 5B). More reconstructed EIT images of these two patients are detailed in Figures S3,S4. The EIT perfusion images of these two patients accurately reflected the location of the pulmonary bullae and were consistent with the clinical reality.

Figure 5 The transverse and coronal 3D-EIT and CT images of patients with massive pulmonary bullae. (A) CT, X-ray, and 3D-EIT images of a patient with massive bullae in the left upper lung. (B) CT, X-ray, and 3D-EIT images of a patient with massive bullae in the right upper lung. Transverse and coronal EIT images demonstrated the corresponding perfusion defect plane on a CT scan or X-ray. The white arrows show the location of pulmonary bullae on 3D-EIT perfusion images. CT, computed tomography; 3D-EIT, three-dimensional electrical impedance tomography.

Identification of pulmonary perfusion loss and recovery during catheterization with 3D-EIT

To demonstrate the feasibility of 3D-EIT for real-time identification of pulmonary perfusion changes during transient vascular obstruction and recanalization, we performed 3D-EIT testing on three patients with suspected pulmonary hypertension who received the Swan-Ganz catheterization. As shown in Figure 6, these patients underwent catheterization after wearing EIT electrode belts. The positions of the catheter tips are indicated by the red circles on the X-ray images. Then, we successively acquired 3D-EIT images before balloon wedging, during wedging, and after balloon release. The 3D-EIT perfusion images of all three patients had changes consistent with the catheterization site and process. At the corresponding locations (left lower lung A8, right lower lung A8, and left lower lung A9, respectively), pulmonary perfusion signals were deficient when the balloons were inflated and wedged in. At the same time, perfusion signals recovered to their original levels after the balloons were deflated, and the filling defect of perfusion images disappeared. Meanwhile, there were no significant changes in the whole pulmonary ventilation signals. This experiment demonstrated the consistency between 3D-EIT perfusion signals and the physiological condition of lung perfusion in the subjects.

Figure 6 3D-EIT ventilation and perfusion images and their radiographic of Swan-Ganz catheterization in three patients. (A) Serial 3D-EIT images obtained from a patient undergoing Swan-Ganz catheterization, showing the wedged balloon position in the left lower lobe (segment A8). (B) Serial 3D-EIT images obtained from a patient undergoing Swan-Ganz catheterization, showing the wedged balloon position in the right lower lobe (segment A8). (C) Serial 3D-EIT images obtained from a patient undergoing Swan-Ganz catheterization, showing the wedged balloon position in the left lower lobe (segment A9). The red circles in the X-ray images show the tips of the catheters, and the white arrows show the perfusion filling defects caused by the wedged balloon. 3D-EIT, three-dimensional electrical impedance tomography.

3D-EIT perfusion images for evaluation of pulmonary embolism therapy

To further illustrate the feasibility of 3D-EIT-based perfusion images and to validate its role in the therapeutic assessment of pulmonary thrombotic disease, we performed pre- and post-treatment EIT detections in patients with acute pulmonary embolism.

The first patient was a 36-year-old male who was admitted to the hospital with a sudden onset of chest tightness and chest pain for 8 days. CTPA showed pulmonary embolism in the apical-posterior segment of the upper lobe and the lingual segment of the left lung, as well as pulmonary artery embolism in the lower lobes of both lungs. Considering the patient’s high thrombus load, percutaneous left pulmonary artery thrombectomy and intraventricular thrombolysis were performed. Postoperative CTPA showed the filling defect of the left pulmonary artery, and its branches were eliminated significantly. Corresponds to 3D-EIT perfusion images, this patient demonstrated a generalized perfusion filling defect in the left lung and the lower lobe of the right lung before the thrombectomy, and then his lung perfusion was recovered mostly, especially in the left upper lung region after the operation (Figure 7A).

Figure 7 The CTPA, nuclide perfusion scan, and 3D-EIT ventilation and perfusion images of patients with acute pulmonary embolism before and after treatments. (A) Pre- and postoperative 3D-EIT and CTPA images in a patient with pulmonary embolism undergoing percutaneous left pulmonary artery thrombectomy with intraventricular thrombolysis. (B) Pre- and post-treatment 3D-EIT and CTPA images in a patient with bilateral pulmonary embolism receiving intravenous thrombolysis. (C) Pre- and post-treatment 3D-EIT, CTPA, and perfusion scan images in a patient with left pulmonary embolism undergoing percutaneous pulmonary thrombolysis. The white or black arrows showed the defects of perfusion in 3D-EIT, CTPA, or nuclide perfusion scan images. CTPA, computed tomography pulmonary angiogram; 3D-EIT, three-dimensional electrical impedance tomography.

The second patient was a 66-year-old elderly female. She was admitted to the hospital for chest tightness for three days and exacerbation for 7 hours. Her D-dimer was greater than 5 mg/mL on admission, and CTPA suggested bilateral multiple pulmonary emboli. Pre-treatment 3D-EIT detection revealed multiple filling defects in perfusion images in both lungs. Correspondingly, the ventilation images of both lungs appeared relatively normal. Four days after intravenous thrombolysis treatment, reexamination of CTPA showed improvement of the filling defects in both sides of the pulmonary artery, and 3D-EIT showed significant recovery of the perfusion images in both lungs (Figure 7B).

The third patient, also a 66-year-old woman, was admitted to the hospital with sudden blackout and palpitations for one day. Her D-dimer on admission was 3.5 mg/mL; therefore, acute pulmonary embolism was suspected. CTPA suggested predominantly left main pulmonary artery embolism. The patient also underwent a VQ scan, which indicated significant attenuation of left lung tracer accumulation on perfusion images. Due to contraindications to intravenous thrombolysis, the physicians performed an interventional pulmonary thrombolysis. Postoperative CTPA suggested improvement of the filling defect in the left pulmonary artery, and a nuclide perfusion scan indicated restoration of left lung perfusion. The patient underwent 3D-EIT before and after treatment, which showed that the left lung perfusion signals improved significantly after treatment, while the whole-lung ventilation distribution did not change significantly (Figure 7C). Detailed ventilation and perfusion images are shown in Figures S5-S7.

Discussion

Our study was the first to use 3D-EIT to reconstruct whole-lung ventilation and perfusion images and demonstrate the feasibility of perfusion images in different clinical situations. Firstly, we showed the ability of 3D-EIT to identify perfusion defects in patients with pulmonary bullae. Giant pulmonary bullae shown on CT presented perfusion defects on EIT perfusion images. The change in perfusion was consistent with a previous report using radionuclide imaging (13). However, EIT demonstrated relatively preserved ventilation in the pulmonary bullae region, which appears discordant with conventional V/Q scan findings. This discrepancy may be attributed to the fundamental technical differences between the two modalities: while V/Q scintigraphy provides absolute measurements of ventilation distribution, current EIT technology primarily detects regional ventilation based on dynamic tidal impedance changes. Secondly, we performed real-time 3D-EIT to monitor the pulmonary ventilation and perfusion in three patients undergoing Swan-Ganz catheterization. Perfusion images of the lung segments corresponding to arterial branches at the site of the balloon showed a filling defect with balloon wedging and recovered with the balloon deflated. This set of changes indicated remarkably well that 3D-EIT perfusion signals correspond to real physiological changes. Finally, we compared 3D-EIT perfusion images with conventional pulmonary artery or perfusion assessment methods such as CTPA and nuclear perfusion scan imaging in acute pulmonary embolism patients before and after mechanical thrombectomy and thrombolysis treatments. Through this pulmonary embolism model, we demonstrated the correspondence between deficits in 3D-EIT perfusion images and the location of the thrombus on CTPA, and also revealed the potential function of 3D-EIT in assessing lung perfusion changes in pulmonary vascular diseases.

Our novel 3D-EIT pulsatility signal-based algorithm technology offers two outstanding advantages. The first advantage is to obtain better physiological or pathological lung perfusion signals rather than using hypertonic saline injection with apnea methodology. The bolus saline injection is a commonly used method to amplify the perfusion signal in conventional EIT (10,14). The premise of this perfusion signal acquisition method is to artificially hold the patient’s breath for 20–30 seconds, which is difficult for most patients to tolerate. Furthermore, the imaging results obtained through this protocol are inherently susceptible to operator-dependent variables due to the substantial technical manipulation required. When combined with the breath-holding requirement, these methodological constraints may limit the ability to consistently and accurately represent true physiological conditions or pathological alterations in pulmonary perfusion dynamics. The need for central venous access for the bolus saline injection method also leads to limited application. The pulsatility-based method is completely non-invasive and has shown good consistency with different clinical situations in this study. The pulmonary perfusion changes in subject tests described above were consistent with clinical estimates, demonstrating the feasibility of our new approach. Further large-sample studies are needed in the future to confirm the authenticity of 3D-EIT signaling and to develop its application in disease assessments. In our previous work, we attempted to integrate clinical parameters with 3D-EIT for the differential diagnosis of chronic thromboembolic pulmonary hypertension (CTEPH), and found that the 3D-EIT diagnostic model demonstrated favorable diagnostic performance (15). The second advantage is that 3D-EIT imaging acquires ventilation and perfusion information of the entire bilateral lung regions through two chest belts and offers the capability of image reconstruction in three dimensions, thereby facilitating the capture of pathological details in any part of the lung. For the pulmonary bullae mentioned above patients, if the 2D-EIT examinations were used, the abnormality could only be detected when the lesion was located within the detection space of the single electrode belt. Therefore, for patients with regional ventilation or perfusion abnormalities, 3D-EIT detection is necessary.

Although there are some methodological limitations, there have been some initial explorations by several researchers in EIT-based pulmonary perfusion studies. Hovnanian et al. (16) found that patients with pulmonary hypertension showed a reduced impedance variation of lung perfusion compared with the normopressoric group, and the impedance variation was associated with the hemodynamic status and disease severity. Proença et al. (17) used a filter-based, non-invasive method for EIT perfusion evaluation and demonstrated that EIT could identify changes in pulmonary pressure of healthy subjects exposed to normobaric hypoxemia. Similar to our study, this study employed a high-pass filtering approach supplemented by electrocardiogram (ECG) gating to extract pulsatile signals, ultimately selecting lung regions based on phase synchronization with cardiac signals. Based on the non-invasive, convenient, and comprehensive features of the novel 3D-EIT, we anticipate that 3D-EIT could play a greater role in the future in the measurement of lung physiologic function as well as in the detection and diagnosis of pulmonary vascular disease. For example, 3D-EIT might be used to calculate global or regional ventilation-perfusion ratio, and the ratios could be calibrated with healthy populations to determine their normal value range in order to evaluate lung function in patients. In addition, an integration of the whole lung perfusion of a cardiac cycle might reflect the stroke volume of the right heart and thus assess the cardiac function. In pathological situations, 3D-EIT can probably be used to identify lung segments perfusion-ventilation mismatch and hence assist in the diagnosis and prognosis evaluation of pulmonary vascular diseases such as acute pulmonary embolism and pulmonary hypertension.

Currently, EIT technology still has certain shortcomings. EIT detects changes in impedance rather than absolute impedance values, which can lead to functionally impaired areas displaying normal signal patterns (the ventilation signal of pulmonary bullae). The lower resolution of the EIT image makes it difficult to distinguish the condition of small blood vessel perfusion. Furthermore, a notable limitation of this study is the lack of direct comparisons with gold-standard methods in many clinical cases—such as confirmation via balloon wedging in Swan-Ganz catheterization for the model, or radionuclide perfusion scans/angiography for pulmonary embolism patients—which somewhat reduces the robustness of our conclusions. Additionally, all primary imaging results are fully presented in the manuscript figures, while quantitative reader analysis was not performed. More future studies need to be done based on large patient cohorts, as well as comparative studies with radionuclide imaging. Although EIT cannot replace the existing diagnostic gold standard due to its poor resolution, it provides a convenient and accessible method of lung ventilation and perfusion function monitoring and follow-up at the bedside.

Conclusions

This proof-of-concept work was completed by a multidisciplinary team, including clinicians and electronic engineers. The novel 3D-EIT successfully reconstructs whole lung ventilation and perfusion images. Our new perfusion imaging methodology using the pulsatility signal-based algorithm can detect whole lung perfusion changes in real-time and non-invasively. It has the potential to provide new physiological information for pulmonary disease diagnosis and lung function monitoring.

Acknowledgments

We appreciate the equipment and technical help provided by the Shanghai International Medical Science and Innovation Center- Excellence Innovation Center for Electromagnetic Medical Diagnosis and Treatment for this study. We gratefully acknowledge the Department of Cardiology of Zhongshan Hospital Affiliated to Fudan University and Shanghai Pulmonary Hospital for providing case resources and analytical assistance in this study.

Footnote

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-275/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-275/prf

Funding: This work was supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project (No. 2024ZD0522700 to L.T.), Fundings of Zhongshan Hospital Fudan University (Nos. ZY2024-001 and ZSLCYJ202319 to Y.S.), Shanghai Three-year Action Plan to Strengthen the Construction of Public Health System (No. GWVI-11.1-18 to Y.S.), National Natural Science Foundation of China (Nos. 82130001 and 82272243 to Y.S.), National Key Research and Development Program of China (No. 2024YFC3044400 to Y.S.), R&D Program of Guangzhou National Laboratory (No. GZNL2024A02003 to Y.S.), The Construction of Multi-Disciplinary Treatment System for Severe Pneumonia (No. W2020-013 to Y.S.), Shanghai Municipal Science and Technology Major Project (No. ZD2021CY001 to Y.S.), Science and Technology Commission of Shanghai Municipality (No. 22Y11900800 to Y.S.) and Data Sharing and Emulation of Clinical Trials (CCS-DASET, SHDC2024CRI043 to Y.S.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-275/coif). H.D., M.G. and Yibing Wang are from Infivision Medical Imaging Technology Co., Ltd. (Beijing, China), which provided research equipment to Zhongshan Hospital. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was approved by the Ethics Committee of Zhongshan Hospital Fudan University (No. 402) and the Ethics Committee of Shanghai Pulmonary Hospital (No. K24-631Y). All subjects signed a written informed consent. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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