# Wavelet Transform Image Enhancement Algorithm-Based Evaluation of Lung Recruitment Effect and Nursing of Acute Respiratory Distress Syndrome by Ultrasound Image

Authors: Wangyan Jin; Ling Dai; Liuyan Ge; Xuhua Huang; Guanhua Xu; Chunhong Qu; Jianfei Sun
Journal: Journal of Healthcare Engineering
 (2021)

---

## Abstract

This study aimed to analyze the application of ultrasound images of lung recruitment (LR) nursing treatment guided by positive-end expiratory pressure (PEEP) in patients with acute respiratory distress syndrome (ARDS). An ultrasound image enhancement algorithm (UIEA) wavelet transform (WT) was constructed, and the soft threshold (ST) and adjacent region average (ARA) were introduced for simulation comparison. In addition, the signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), and running time were undertaken as the evaluation indexes. The WT algorithm was applied to the ultrasound images of 85 ARDS patients before and after PEEP recruitment. The mean artery pressure (MAP), heart rate (HR), and central venous pressure (CVP), peak inspiratory pressure (Ppeak), mean inspiratory pressure (Pmean), dynamic lung compliance (DLC), PCO2, and PaO2/FiO2 of the patients were recorded before and after the LR. The results showed that the signal-to-noise ratio (SNR) (19.67 ± 3.15 dB) and PSNR (23.08 ± 2.08 dB) of the images enhanced by the WT algorithm were much higher than those of ST (13.88 ± 2.74 dB and 14.62 ± 1.76 dB, respectively) and ARA (14.96 ± 3.06 dB and 15.11 ± 1.94 dB, respectively), while the running time was in adverse (P<0.05); the HR and CVP of patients after LR nursing treatment were increased greatly, while the MAP was in the opposite case (P<0.05); after LR nursing treatment, Ppeak, Pmean, DLC, PCO2, and PaO2/FiO2 of the patient were significantly greater than those before the LR, and the difference was statistically significant (P<0.05). In short, the WT algorithm not only enhanced the quality of ultrasound images but also shortened the running time and improved the processing efficiency. PEEP LR nursing treatment could effectively improve the vascular patency, cardiac ejection capacity, and DLC in patients with ARDS, thereby increasing the airway pressure and maintaining the unobstructed expiration.


---

## Body

## 1. Introduction

ARDS refers to the diffuse damage caused by lung capillary endothelial cells and alveolar epithelial cells in the process of noncardiac diseases such as severe infection, shock, high-risk surgery, trauma, and burns. Pulmonary interstitial and alveolar edema, can lead to acute hypoxic respiratory insufficiency or failure [1]. Onset of ARDS is so rapid that it can occur within 24–48 hours, and the ARDS is mainly characterized by reduced lung volume, reduced lung compliance, severe ventilation, or blood flow imbalance [2, 3]. It is necessary to apply the sensitive antibiotics to the bacteria to prevent the inflammatory reaction from further damage to the lungs. It is more urgent to correct the severe hypoxia of the patient in time to win precious time for the treatment of underlying diseases [4]. Currently, PEEP nursing scheme is mainly adopted to treat patients with ARDS, mainly by expanding the trachea and closing the alveoli, reducing the static blood shunt in the lungs, and improving the ventilation/blood flow ratio and diffusion function, thereby improving the respiratory function of patient [5]. Therefore, PEEP nursing scheme was used in this study to treat the ARDS patients.With the development of imaging technology, ultrasound imaging is gradually utilized in the diagnosis of ARDS. Compared with the gold standard chest computed tomography (CT) with high cost, large radiation, and complicated operation, thoracic lung ultrasound can clearly show diseases such as pulmonary interstitial syndrome, pulmonary consolidation, pneumonia, pleural exudation, and pneumothorax [6]. It is safer, noninvasive, cost-effective, low-cost, and dynamic observation. Tang et al. (2017) [7] discussed the clinical value of ultrasound monitoring in assessing LR and PEEP and found that lung ultrasound could be used to detect the LR endpoints and optimal PEEP, improving the DLC and oxygenation effectively. Therefore, ultrasound images were intended to be applied to evaluate the therapeutic effect of PEEP on ARDS patients in this study [8]. WT refers to the use of finite-length or fast-decaying oscillating waveforms to represent signals. It is an effective time, space, and scale analysis method and has been widely applied in many research fields of signal and image processing technology. WT is able for resolution analysis and time-frequency localization capabilities, can effectively distinguish the useful signals from noise, and has a good effect on image denoising and edge feature retention [9, 10]. Therefore, the WT algorithm was introduced in this study to process the ultrasound images of lung.In summary, an UIEA WT was constructed based on the conventional WT, and the ST and ARA were employed for comparison and applied to the ultrasound images of 85 patients with ARDS before and after PEEP LR nursing treatment. The hemodynamic indexes (MAP, HR, and CVP), lung mechanical indexes (Ppeak, Pmean, and DLC), and blood-gas parameters (PCO2 and PaO2/FiO2) of the patients were comprehensively compared and analyzed to evaluate the application of ultrasound image in PEEP-guided LR treatment in ARDS patients.


## 2. Materials and Methods

### 2.1. Selection of Research Samples

85 patients with ARDS who were admitted to the hospital for PEEP LR treatment from February 11, 2019, to February 10, 2020, were selected as the research objects. The study had been approved by the Medical Ethics Committee of Hospital, and the patients and their families had understood the study and signed the informed consents and the inspection failure consents.Inclusion criteria were given as follows: patients with acute onset; patients with oxygenation index less than 200 mmHg; patients older than 18 years old; patients with clear consciousness and ability of cooperating with the examination; and patients whose X-ray chest radiograph showed patchy shadows in the lungs.Exclusion criteria were determined as follows: patients with pulmonary incarceration pressure greater than 20 mmHg; patients with psychiatric diseases; patients with hemodynamic insufficiency during treatment; patients with oxygenation index less than 100 mmHg; patients with intracranial hypertension; patients with pneumothorax; and patients with incomplete clinical information.


### 2.2. Treatment Methods

The patient was placed in a supine position, and the lung recruitment therapy was realized with the method of increasing PEEP. Firstly, the medical ventilator produced by Philips Respironics was set to the pressure mode, the PEEP was filled with 15 cmH2O and PC20 cmH2O, the inhaled oxygen concentration was adjusted to 1.0, and the oxygen saturation was set to about 92% for 20 minutes. The PEEP was increased at an interval of 5 cmH2O, and the personal computer (PC) was fixed. After each increase of PEEP, it was ventilated for 1 minute, and then the PEEP was adjusted to 15 cmH2O for 20 minutes. The LR was considered sufficient when the ultrasound score was no longer increased. Finally, the PEEP was adjusted back to 20 cmH2O until the tidal volume reached 6.5 mL/kg.


### 2.3. Lung Ultrasound Scan Method

An intelligent ultrasound system (Philips IU22) was adopted to scan the lungs of patients with a probe frequency of 2–5 MHz. The lung was divided into 12 areas, including 4 areas (up, down, left, and right) in the chest based on the sternal angle plane and the human body’s midaxis plane, and each area was divided into 3 subareas (front, middle, and back area) based on the front and back axillary lines area. During the scan, the patient was placed in a supine position, and the scan was started from the second intercostal space from top to bottom and front to back as well as along the intercostal space. The probe was rotated 90° so that the probe was perpendicular to the chest wall, and then one side of the patient’s body was elevated to scan the lung field in the back. The characteristics of the patient’s effusion and consolidation stroma were observed and recorded, and the images were sent to the workstation for processing. The hemodynamic indexes (MAP, HR, and CVP), lung mechanical indexes (Ppeak, Pmean, and DLC), and blood-gas parameters (PCO2 and PaO2/FiO2) of the patients were measured and recorded.


### 2.4. Ultrasound Image Enhancement Algorithm Based on Wavelet Transform

In ultrasound imaging, the single-frequency ultrasound emitted by the ultrasound probe would scatter when it touched the surface of tissue, resulting in a series of coherent waves and causing a lot of noise. Thus, how to deal with such speckle noise was a very important research hotspot. It was assumed that the observed image wasux,y and the actual image was px,y, then the following equation could be obtained:(1)ux,y=px,y+ξx,y•px,y.In the above equation,ξx,y represented the noise. The traditional denoising methods cannot meet the two requirements of filtering noise and optimizing image edge features, and the image details were fuzzed when the noise was removed. On the contrary, WT could solve the above problems based on its decorrelation and multiresolution characteristics. Therefore, the concept of WT was introduced in this study to denoise the ultrasound images. The basic process was defined as follows: after the original ultrasound image was inputted, an appropriate wavelet function was selected to decompose the image in multiple layers to obtain the corresponding wavelet coefficients. Then, appropriate threshold processing was performed on each decomposed layer, the obtained wavelet decomposition coefficients and threshold processing coefficients were combined to calculate the wavelet reconstruction of the image, so as to obtain the denoising processed image.In the wavelet algorithm, the selection of an appropriate threshold was very critical. If the threshold was too large, the wavelet coefficient would become zero, and the image became too blurry. If the threshold was too small, the image would retain too much noise. Therefore, the BayesShrink threshold [11] was adopted for processing, which carried different subequations in images of different scales and directions and could be adjusted adaptively with subbands. The equation could be expressed as follows:(2)TB=θ2θx.In the above equation,TB referred to the BayesShrink threshold, θ2 referred to the noise standard deviation, and θx represented the Gaussian standard deviation. The threshold function was to reconstruct the continuity and accuracy of the signal and had a great influence on the image denoising effect. The hard threshold (HT) method was combined with the ST method in this study to obtain a semisoft threshold function. Firstly, the HT function could be expressed as (3)m=n,n>κ,0,others.In the above equation,m represented the point on the abscissa of the image pixel, n represented the point on the ordinate of the image pixel, and κ referred to the set threshold point. The ST was to replace the coefficients less than the set threshold with 0, and its equation could be written as follows:(4)m=sigmnn−κ,n>κ,0,others.In (4), sigmn represents the symbolic function, and sigmn=1n>0−1n<0. A compromise solution could be obtained based on the HT and ST, which could be expressed as follows:(5)m=n,n>κ2,(6)m=sigmnκ2n−κ1κ2−κ1,κ2<n<κ1,(7)m=0,n<κ1.In the above equations,κ2 represented the higher threshold, κ1 referred to the lower threshold, and κ2=2κ1. The wavelet function used in WT was diverse. Thus, the wavelet function was selected based on the principles of orthogonality, support length, symmetry, and regularity in this study, and the decomposition filter and reconstruction filter of the biorthogonal WT were selected for processing. The boundary extension was required due to the length of actual signal. The symmetric extension was adopted to process signals of limited length to avoid the inconsistency of the signal caused by the period extension. Therefore, the above process was to build the UIEA WT based on WT.


### 2.5. Evaluation Indexes of Image Enhancement Performance

The ST [12] and the ARA [13] were introduced to compare with the WT algorithm constructed in this study, taking SNR, PSNR, and running time as evaluation indexes.SNR was a common evaluation index to measure the amount of image signal noise (decibel, Db) and could be expressed as follows:(8)SNR=10×log10P1P2,(9)P1=1K×H∑nK×HS∗m,n2,(10)P2=1K×H∑nK×HSm,n−S∗m,n2.In equations (8)–(10), P1 represented the power of the signal, P2 referred to the noise power, Sm,n indicated the initial signal, S∗m,n represented the signal after wavelet processing, K referred to the dimension of the image row, and H represented the dimension of the image column.PSNR was to evaluate the fidelity of the image, with the unit of decibel (dB). It could be calculated with the following equation:(11)PSNR=10×log10N2MSE,(12)MSE=∑nK×HOn−Fn2K×H,where N represented the maximum gray value of the image, On referred to the nth pixel value of the original image, and Fn indicated the nth pixel value of the processed image.


### 2.6. Statistical Methods

The data processing in this study was analyzed by SPSS19.0 version statistical software, the measurement data was expressed as mean ± standard deviation (−x ± s), and the count data was indicated with percentage. The pairwise comparison of SNR, PSNR, and running time of WT, ARA, and ST algorithms was realized with the single-factor analysis of variance. The hemodynamic indexes (MAP, HR, and CVP), lung mechanical indexes (Ppeak, Pmean, and DLC), and blood-gas parameters (PCO2 and PaO2/FiO2) before and after LR nursing treatment were compared by the independent t-test. The difference was statistically significant at P<0.05.


## 2.1. Selection of Research Samples

85 patients with ARDS who were admitted to the hospital for PEEP LR treatment from February 11, 2019, to February 10, 2020, were selected as the research objects. The study had been approved by the Medical Ethics Committee of Hospital, and the patients and their families had understood the study and signed the informed consents and the inspection failure consents.Inclusion criteria were given as follows: patients with acute onset; patients with oxygenation index less than 200 mmHg; patients older than 18 years old; patients with clear consciousness and ability of cooperating with the examination; and patients whose X-ray chest radiograph showed patchy shadows in the lungs.Exclusion criteria were determined as follows: patients with pulmonary incarceration pressure greater than 20 mmHg; patients with psychiatric diseases; patients with hemodynamic insufficiency during treatment; patients with oxygenation index less than 100 mmHg; patients with intracranial hypertension; patients with pneumothorax; and patients with incomplete clinical information.


## 2.2. Treatment Methods

The patient was placed in a supine position, and the lung recruitment therapy was realized with the method of increasing PEEP. Firstly, the medical ventilator produced by Philips Respironics was set to the pressure mode, the PEEP was filled with 15 cmH2O and PC20 cmH2O, the inhaled oxygen concentration was adjusted to 1.0, and the oxygen saturation was set to about 92% for 20 minutes. The PEEP was increased at an interval of 5 cmH2O, and the personal computer (PC) was fixed. After each increase of PEEP, it was ventilated for 1 minute, and then the PEEP was adjusted to 15 cmH2O for 20 minutes. The LR was considered sufficient when the ultrasound score was no longer increased. Finally, the PEEP was adjusted back to 20 cmH2O until the tidal volume reached 6.5 mL/kg.


## 2.3. Lung Ultrasound Scan Method

An intelligent ultrasound system (Philips IU22) was adopted to scan the lungs of patients with a probe frequency of 2–5 MHz. The lung was divided into 12 areas, including 4 areas (up, down, left, and right) in the chest based on the sternal angle plane and the human body’s midaxis plane, and each area was divided into 3 subareas (front, middle, and back area) based on the front and back axillary lines area. During the scan, the patient was placed in a supine position, and the scan was started from the second intercostal space from top to bottom and front to back as well as along the intercostal space. The probe was rotated 90° so that the probe was perpendicular to the chest wall, and then one side of the patient’s body was elevated to scan the lung field in the back. The characteristics of the patient’s effusion and consolidation stroma were observed and recorded, and the images were sent to the workstation for processing. The hemodynamic indexes (MAP, HR, and CVP), lung mechanical indexes (Ppeak, Pmean, and DLC), and blood-gas parameters (PCO2 and PaO2/FiO2) of the patients were measured and recorded.


## 2.4. Ultrasound Image Enhancement Algorithm Based on Wavelet Transform

In ultrasound imaging, the single-frequency ultrasound emitted by the ultrasound probe would scatter when it touched the surface of tissue, resulting in a series of coherent waves and causing a lot of noise. Thus, how to deal with such speckle noise was a very important research hotspot. It was assumed that the observed image wasux,y and the actual image was px,y, then the following equation could be obtained:(1)ux,y=px,y+ξx,y•px,y.In the above equation,ξx,y represented the noise. The traditional denoising methods cannot meet the two requirements of filtering noise and optimizing image edge features, and the image details were fuzzed when the noise was removed. On the contrary, WT could solve the above problems based on its decorrelation and multiresolution characteristics. Therefore, the concept of WT was introduced in this study to denoise the ultrasound images. The basic process was defined as follows: after the original ultrasound image was inputted, an appropriate wavelet function was selected to decompose the image in multiple layers to obtain the corresponding wavelet coefficients. Then, appropriate threshold processing was performed on each decomposed layer, the obtained wavelet decomposition coefficients and threshold processing coefficients were combined to calculate the wavelet reconstruction of the image, so as to obtain the denoising processed image.In the wavelet algorithm, the selection of an appropriate threshold was very critical. If the threshold was too large, the wavelet coefficient would become zero, and the image became too blurry. If the threshold was too small, the image would retain too much noise. Therefore, the BayesShrink threshold [11] was adopted for processing, which carried different subequations in images of different scales and directions and could be adjusted adaptively with subbands. The equation could be expressed as follows:(2)TB=θ2θx.In the above equation,TB referred to the BayesShrink threshold, θ2 referred to the noise standard deviation, and θx represented the Gaussian standard deviation. The threshold function was to reconstruct the continuity and accuracy of the signal and had a great influence on the image denoising effect. The hard threshold (HT) method was combined with the ST method in this study to obtain a semisoft threshold function. Firstly, the HT function could be expressed as (3)m=n,n>κ,0,others.In the above equation,m represented the point on the abscissa of the image pixel, n represented the point on the ordinate of the image pixel, and κ referred to the set threshold point. The ST was to replace the coefficients less than the set threshold with 0, and its equation could be written as follows:(4)m=sigmnn−κ,n>κ,0,others.In (4), sigmn represents the symbolic function, and sigmn=1n>0−1n<0. A compromise solution could be obtained based on the HT and ST, which could be expressed as follows:(5)m=n,n>κ2,(6)m=sigmnκ2n−κ1κ2−κ1,κ2<n<κ1,(7)m=0,n<κ1.In the above equations,κ2 represented the higher threshold, κ1 referred to the lower threshold, and κ2=2κ1. The wavelet function used in WT was diverse. Thus, the wavelet function was selected based on the principles of orthogonality, support length, symmetry, and regularity in this study, and the decomposition filter and reconstruction filter of the biorthogonal WT were selected for processing. The boundary extension was required due to the length of actual signal. The symmetric extension was adopted to process signals of limited length to avoid the inconsistency of the signal caused by the period extension. Therefore, the above process was to build the UIEA WT based on WT.


## 2.5. Evaluation Indexes of Image Enhancement Performance

The ST [12] and the ARA [13] were introduced to compare with the WT algorithm constructed in this study, taking SNR, PSNR, and running time as evaluation indexes.SNR was a common evaluation index to measure the amount of image signal noise (decibel, Db) and could be expressed as follows:(8)SNR=10×log10P1P2,(9)P1=1K×H∑nK×HS∗m,n2,(10)P2=1K×H∑nK×HSm,n−S∗m,n2.In equations (8)–(10), P1 represented the power of the signal, P2 referred to the noise power, Sm,n indicated the initial signal, S∗m,n represented the signal after wavelet processing, K referred to the dimension of the image row, and H represented the dimension of the image column.PSNR was to evaluate the fidelity of the image, with the unit of decibel (dB). It could be calculated with the following equation:(11)PSNR=10×log10N2MSE,(12)MSE=∑nK×HOn−Fn2K×H,where N represented the maximum gray value of the image, On referred to the nth pixel value of the original image, and Fn indicated the nth pixel value of the processed image.


## 2.6. Statistical Methods

The data processing in this study was analyzed by SPSS19.0 version statistical software, the measurement data was expressed as mean ± standard deviation (−x ± s), and the count data was indicated with percentage. The pairwise comparison of SNR, PSNR, and running time of WT, ARA, and ST algorithms was realized with the single-factor analysis of variance. The hemodynamic indexes (MAP, HR, and CVP), lung mechanical indexes (Ppeak, Pmean, and DLC), and blood-gas parameters (PCO2 and PaO2/FiO2) before and after LR nursing treatment were compared by the independent t-test. The difference was statistically significant at P<0.05.


## 3. Results

### 3.1. Comparison of Three Algorithms for Denoising Performance of Lung Ultrasound Images

Figure1 shows the comparison of the denoising performance of three algorithms on lung ultrasound images. It revealed that the SNR and PSNR of the ST algorithm for lung ultrasound image reconstruction were 13.88 ± 2.74 dB and 14.62 ± 1.76 dB, respectively; the SNR and PSNR of the ARA algorithm for lung ultrasound image reconstruction were 14.96 ± 3.06 dB and 15.11 ± 1.94 dB, respectively; the SNR and PSNR of WT algorithm for the lung ultrasound image reconstruction were 19.67 ± 3.15 dB and 23.08 ± 2.08 dB, respectively. Thus, the SNR and PSNR of WT algorithm for lung ultrasound image reconstruction were higher observably than those of the ARA and ST algorithm (P<0.05).Figure 1
The comparison of the denoising performance of three algorithms on lung ultrasound images. (a) The SNR and (b) PSNR of the three algorithms for lung ultrasound image reconstruction, respectively.∗ indicates P<0.05 in contrast to the WT algorithm.
(a)(b)Figure2 illustrates the comparison in the running time of three algorithms for lung ultrasound image denoising. It disclosed that the running time of ST, ARA, and WT algorithm for lung ultrasound image reconstruction was 15.17 ± 1.05 s, 14.84 ± 0.77 s, and 23.08 ± 2.56 s, respectively. In addition, the running time of WT algorithm was much shorter than that of ARA algorithm and ST algorithm, and the difference was statistically significant (P<0.05).Figure 2
The comparison in the running time of three algorithms for lung ultrasound image denoising.∗ indicates P<0.05 in contrast to the WT algorithm.


### 3.2. Reconstruction Results of Lung Ultrasound Images with Three Algorithms

The results of lung ultrasound image reconstruction of the three algorithms were compared and analyzed, and the results were given in Figure3. Figure 3(a) is an original ultrasound image of a patient, which included a lot of artifacts and noises, and the clarity was so poor that the needs of imaging diagnosis could not be satisfied. The clarity of Figure 3(d) was higher obviously than that of Figures 3(b) and 3(c), and it could display the lung lesions perfectly.Figure 3
The results of lung ultrasound image reconstruction of the three algorithms. (a) An original ultrasound image of a patient; (b–d) the reconstructed images with ST, ARA, and WT algorithms, respectively.
(a)(b)(c)(d)


### 3.3. Comparison of Hemodynamic Parameters before and after Lung Recruitment Nursing Treatment

Figure4 illustrates the comparison of HR and MAP before and after LR nursing treatment. The HR and MAP before LR was 95.71 ± 5.11 beats/min and 101.42 ± 10.33 mmHg, respectively, while the HR and MAP after LR was 118.62 ± 8.37 beats/min and 79.41 ± 7.26 mmHg, respectively. In addition, the HR of patients after LR was faster extremely than that before LR (P<0.05); and the MAP of patients after LR was greatly decreased in contrast to that before (P<0.05).Figure 4
The comparison of (a) HR and (b) MAP before and after lung recruitment.∗ suggests P<0.05 in contrast to the values before lung recruitment.
(a)(b)Figure5 reveals the comparison of CVP before and after LR. The CVP before and after LR was 7.33 ± 2.01 mmHg and 12.15 ± 1.85 mmHg, respectively. Besides, the CVP of patients after LR was increased greatly than that before LR (P<0.05).Figure 5
The comparison of CVP before and after lung recruitment.∗suggests P<0.05 in contrast to the values before lung recruitment.


### 3.4. Comparison of Blood-Gas Parameters before and after Lung Recruitment Nursing Treatment

Figure6 shows the comparison of blood-gas parameters (PCO2 and PaO2/FiO2) before and after LR nursing treatment. As it indicated, the PCO2 and PaO2/FiO2 before LR were 42.74 ± 10.64 mmHg and 175.37 ± 12.41 mmHg, respectively; and the PCO2 and PaO2/FiO2 after LR were 56.38 ± 9.78 mmHg and 385.15 ± 15.93 mmHg, of which, the blood-gas parameters (PCO2 and PaO2/FiO2) after LR were observably increased than those before LR (P<0.05).Figure 6
The comparison of blood-gas parameters ((a) PCO2 and (b) PaO2/FiO2) before and after lung recruitment. ∗indicates P<0.05 in contrast to the values before lung recruitment.
(a)(b)


### 3.5. Comparison of Respiratory Mechanical Parameters before and after Lung Recruitment Nursing Treatment

The respiratory mechanical parameter Cdyn before and after LR nursing treatment was analyzed and compared, as shown in Figure7. The Cdyn before LR was 22.54 ± 1.51 mL/cH2O, and the Cdyn after LR was 42.74 ± 2.65 mL/cH2O. Among them, the Cdyn of patients after LR was significantly greater than before LR, and the difference was statistically significant (P<0.05).Figure 7
The respiratory mechanical parameter Cdyn before and after lung recruitment.∗indicates P<0.05 in contrast to the values before lung recruitment.As shown in Figure8, the comparison of the respiratory mechanical parameter Pmean and Ppeak before and after LR revealed that the Pmean and Ppeak before LR were 15.76 ± 3.03 mL/cH2O and 27.17 ± 1.34 mL/cH2O, respectively; and the Pmean and Ppeak after LR were 30.57 ± 4.02 mL/cH2O and 46.11 ± 3.42 mL/cH2O, respectively. Thus, the Pmean and Ppeak of patients after LR were higher obviously than those before LR (P<0.05).Figure 8
The comparison of the respiratory mechanical parameters (a) Ppeakand (b) Pmean before and after lung recruitment.∗means P<0.05 in contrast to the values before lung recruitment.
(a)(b)


## 3.1. Comparison of Three Algorithms for Denoising Performance of Lung Ultrasound Images

Figure1 shows the comparison of the denoising performance of three algorithms on lung ultrasound images. It revealed that the SNR and PSNR of the ST algorithm for lung ultrasound image reconstruction were 13.88 ± 2.74 dB and 14.62 ± 1.76 dB, respectively; the SNR and PSNR of the ARA algorithm for lung ultrasound image reconstruction were 14.96 ± 3.06 dB and 15.11 ± 1.94 dB, respectively; the SNR and PSNR of WT algorithm for the lung ultrasound image reconstruction were 19.67 ± 3.15 dB and 23.08 ± 2.08 dB, respectively. Thus, the SNR and PSNR of WT algorithm for lung ultrasound image reconstruction were higher observably than those of the ARA and ST algorithm (P<0.05).Figure 1
The comparison of the denoising performance of three algorithms on lung ultrasound images. (a) The SNR and (b) PSNR of the three algorithms for lung ultrasound image reconstruction, respectively.∗ indicates P<0.05 in contrast to the WT algorithm.
(a)(b)Figure2 illustrates the comparison in the running time of three algorithms for lung ultrasound image denoising. It disclosed that the running time of ST, ARA, and WT algorithm for lung ultrasound image reconstruction was 15.17 ± 1.05 s, 14.84 ± 0.77 s, and 23.08 ± 2.56 s, respectively. In addition, the running time of WT algorithm was much shorter than that of ARA algorithm and ST algorithm, and the difference was statistically significant (P<0.05).Figure 2
The comparison in the running time of three algorithms for lung ultrasound image denoising.∗ indicates P<0.05 in contrast to the WT algorithm.


## 3.2. Reconstruction Results of Lung Ultrasound Images with Three Algorithms

The results of lung ultrasound image reconstruction of the three algorithms were compared and analyzed, and the results were given in Figure3. Figure 3(a) is an original ultrasound image of a patient, which included a lot of artifacts and noises, and the clarity was so poor that the needs of imaging diagnosis could not be satisfied. The clarity of Figure 3(d) was higher obviously than that of Figures 3(b) and 3(c), and it could display the lung lesions perfectly.Figure 3
The results of lung ultrasound image reconstruction of the three algorithms. (a) An original ultrasound image of a patient; (b–d) the reconstructed images with ST, ARA, and WT algorithms, respectively.
(a)(b)(c)(d)


## 3.3. Comparison of Hemodynamic Parameters before and after Lung Recruitment Nursing Treatment

Figure4 illustrates the comparison of HR and MAP before and after LR nursing treatment. The HR and MAP before LR was 95.71 ± 5.11 beats/min and 101.42 ± 10.33 mmHg, respectively, while the HR and MAP after LR was 118.62 ± 8.37 beats/min and 79.41 ± 7.26 mmHg, respectively. In addition, the HR of patients after LR was faster extremely than that before LR (P<0.05); and the MAP of patients after LR was greatly decreased in contrast to that before (P<0.05).Figure 4
The comparison of (a) HR and (b) MAP before and after lung recruitment.∗ suggests P<0.05 in contrast to the values before lung recruitment.
(a)(b)Figure5 reveals the comparison of CVP before and after LR. The CVP before and after LR was 7.33 ± 2.01 mmHg and 12.15 ± 1.85 mmHg, respectively. Besides, the CVP of patients after LR was increased greatly than that before LR (P<0.05).Figure 5
The comparison of CVP before and after lung recruitment.∗suggests P<0.05 in contrast to the values before lung recruitment.


## 3.4. Comparison of Blood-Gas Parameters before and after Lung Recruitment Nursing Treatment

Figure6 shows the comparison of blood-gas parameters (PCO2 and PaO2/FiO2) before and after LR nursing treatment. As it indicated, the PCO2 and PaO2/FiO2 before LR were 42.74 ± 10.64 mmHg and 175.37 ± 12.41 mmHg, respectively; and the PCO2 and PaO2/FiO2 after LR were 56.38 ± 9.78 mmHg and 385.15 ± 15.93 mmHg, of which, the blood-gas parameters (PCO2 and PaO2/FiO2) after LR were observably increased than those before LR (P<0.05).Figure 6
The comparison of blood-gas parameters ((a) PCO2 and (b) PaO2/FiO2) before and after lung recruitment. ∗indicates P<0.05 in contrast to the values before lung recruitment.
(a)(b)


## 3.5. Comparison of Respiratory Mechanical Parameters before and after Lung Recruitment Nursing Treatment

The respiratory mechanical parameter Cdyn before and after LR nursing treatment was analyzed and compared, as shown in Figure7. The Cdyn before LR was 22.54 ± 1.51 mL/cH2O, and the Cdyn after LR was 42.74 ± 2.65 mL/cH2O. Among them, the Cdyn of patients after LR was significantly greater than before LR, and the difference was statistically significant (P<0.05).Figure 7
The respiratory mechanical parameter Cdyn before and after lung recruitment.∗indicates P<0.05 in contrast to the values before lung recruitment.As shown in Figure8, the comparison of the respiratory mechanical parameter Pmean and Ppeak before and after LR revealed that the Pmean and Ppeak before LR were 15.76 ± 3.03 mL/cH2O and 27.17 ± 1.34 mL/cH2O, respectively; and the Pmean and Ppeak after LR were 30.57 ± 4.02 mL/cH2O and 46.11 ± 3.42 mL/cH2O, respectively. Thus, the Pmean and Ppeak of patients after LR were higher obviously than those before LR (P<0.05).Figure 8
The comparison of the respiratory mechanical parameters (a) Ppeakand (b) Pmean before and after lung recruitment.∗means P<0.05 in contrast to the values before lung recruitment.
(a)(b)


## 4. Discussion

As a common critical symptom, ARDS has a fatality rate of 30–40%, which is extremely threatening to the life safety of patients. Its typical physiological changes include reduced lung volume, reduced compliance, and imbalanced ventilation/blood flow ratio. Thus, how to realize the protective ventilation of lung is essential to improve the respiratory function of the patient [14]. Therefore, an UIEA WT was constructed based on WT, and the ST and ARA were introduced for comparison and analysis. The results showed that the SNR and PSNR of the WT algorithm for lung ultrasound image reconstruction were much higher than those of the ARA algorithm and the ST algorithm (P<0.05), which was similar to the research results of Bein et al. (2016) [15]. Thus, it indicated that the WT algorithm constructed in this study had better performance in denoising ultrasound images and reducing the influence of artifacts and noises. The running time of the ST algorithm for lung ultrasound image reconstruction was obviously longer than that of the ARA algorithm and the WT algorithm (P<0.05), which indicated that the WT algorithm based on WT was very rapid for ultrasound images. Therefore, the WT algorithm not only enhanced the quality of ultrasound images but also shortened the running time and improved the processing efficiency [16].The WT algorithm was applied to the diagnosis of ultrasound images of 85 patients with ARDS. The results disclosed that the HR and CVP of patients after LR were increased greatly than those before LR (P<0.05), which was similar to the results of Sahetya and Brower (2017) [17], indicating that the PEEP LR nursing treatment could effectively improve the patency of vascular circulation in patients with ARDS and reduce the degree of blood viscosity. The MAP of patients after LR was observably smaller than that before LR (P<0.05). CVP is the pressure where the upper and inferior vena cava enter the right atrium. It can be measured through the upper and inferior vena cava or the right atrium tube and can reflect the ejection ability of the heart. The results of this article showed that the treatment after LR effectively improved the cardiac ejection ability of the patient, which was beneficial for the patient to eject the returned blood into the artery in time. The blood-gas parameters PCO2 and PaO2/FiO2 of patients after LR were increased obviously in contrast to those before LR (P<0.05), which indicated that the alveoli of patients after LR treatment could maintain more open state and increase oxygenation level. The Cdyn of patients after LR was higher hugely than that before LR (P<0.05), which was different with the results of Karlsson et al. (2018) [18]. The reason of which may be that DLC differed in the compliance expressed by the respiratory muscles during lung breathing. The better the compliance, the smoother the breathing, and vice versa. The results of this study indicated that LR nursing treatment could effectively improve the DLC of the patient’s respiratory system [19]. The Pmean and Ppeak of patients after LR were higher significantly than before LR (P<0.05), which was not similar to the results of Sigmundsson et al. (2020) [20]. Ppeak is the maximum pressure during lung ventilation, while Pmean is the mean pressure experienced by the lungs during the respiratory cycle. The results of this study suggested that LR nursing treatment could increase the pressure in the airway to prevent airway trapping and maintain a clear breath.


## 5. Conclusion

An UIEA WT was constructed based on the WT, the ST and ARA were introduced for comparison, and then the WT algorithm was applied to the ultrasound images of 85 ARDS patients before and after PEEP LR nursing treatment. It was found that the WT algorithm not only enhanced the quality of ultrasound images but also shortened the running time and improved the processing efficiency. PEEP LR nursing treatment could effectively improve the vascular patency, cardiac ejection capacity, and DLC in patients with ARDS, so as to increase the airway pressure and maintain the unobstructed expiration. However, there were still some shortcomings for this study. The sample size of the selected patients was relatively small and the source was single, which may have some impact on the experimental results. In the future, we will consider increasing the sample size of patients to further explore the diagnostic value of the WT algorithm for the prognosis of ARDS patients. In conclusion, the results of this study provided a theoretical basis for the clinical application of PEEP LR nursing scheme in the treatment of ARDS patients.



---

*Source: 8960465-2021-09-11.xml*