# E-Health Ultrasonic Diagnostic Monitoring for Analysis of Cardiac Insufficiency and Neuronal Regulation in Patients with Sepsis in Emergency Department under Image Reconstruction Algorithm

Authors: Zhonghua Fang; Mao Zhang
Journal: Computational and Mathematical Methods in Medicine
 (2022)

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## Abstract

An anisotropic diffusion filtering- (ADF-) ultrasound (ADF-U) for ultrasound reconstruction was constructed based on the ADF to explore the diagnostic application of ultrasound imaging based on electronic health (E-health) for cardiac insufficiency and neuronal regulation in patients with sepsis. The 144 patients with sepsis were divided into an experimental group (78 patients with cardiac insufficiency) and a control group (66 patients with normal cardiac function), and another 58 healthy people were included in a blank control. The ultrasound examination was performed on all patients. In addition, new ultrasound image reconstruction and diagnosis were performed based on ADF and E-health, and its reconstruction effects were compared with those of the Bilateral Filter-ultrasonic (BFU) algorithm and the Wavelet Threshold-ultrasonic (WTU) algorithm. The left and right ventricular parameters and neuropeptide levels were detected and recorded. The results show that the running time, average gradient (AG), and peak signal-to-noise ratio (SNR) (PSNR) of the ADF-U algorithm were greater than those of the Bilateral Filter-ultrasonic (BFU) and Wavelet Threshold-ultrasonic (WTU), but the mean square error (MSE) was opposite (P<0.05); the left ventricular end-systolic volume (LVESV) and the vertical distance between the mitral valve E-point to septal separation (EPSS) in the experimental group were higher than those in the control and blank group, while the left ventricular ejection fraction (LVEF), stroke volume (SV), cardiac output (CO), and left ventricular fractional shortening (LVFS) were opposite (P<0.05); the systolic peak velocity of right ventricular free wall tricuspid annulus (Sm) and pulmonary valve blood velocity (PVBV) in the experimental group were lower than those of the control group and blank group (P<0.05); the messenger ribonucleic acid (mRNA) of Proopiomelanocortin (POMC) and Cocain and amphetamine-regulated transcript (CART) was higher than the mRNA IN control group and blank group (P<0.05). In short, the ADF-U algorithm proposed in this study improved the resolution, SNR, and reconstruction efficiency of E-health ultrasound images and provided an effective reference value for the diagnosis of cardiac insufficiency and neuronal adjustment analysis in patients with sepsis in the emergency department.


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## Body

## 1. Introduction

Sepsis is a series of systemic response syndromes caused by infection, which can affect multiple organs, leading to organ damage or failure. The clinical manifestations of sepsis are chills, high fever or low body temperature, cough, sputum, chest pain, and headache, oliguria, consciousness disorder, restlessness, lethargy, apathy, and coma and even shock and progressive multiple organ dysfunction [1, 2]. It usually occurs in patients with severe diseases or trauma, such as severe burns, multiple injuries, postsurgical operations, and severe pneumonia. Besides, it is common in patients with chronic diseases or weakened immune systems, such as diabetes, chronic obstructive bronchitis, leukemia, aplastic anemia, and urinary tract stones [3]. Sepsis is dangerous with high morbidity and mortality. About 3‰ population in the world suffer from sepsis and septic shock every year. The mortality rate of patients with organ failure can even reach 70% [4]. In the sepsis state, the patient’s myocardial contractile function decreases and cardiac output decreases, which leads to heart failure and cardiac insufficiency. If the condition cannot be controlled in time, it may even worsen and develop into organic heart disease. Therefore, it is very important to diagnose the body changes of sepsis as early as possible. Nerve regulation refers to the regulation process of physiological functions achieved under the direct participation of the nervous system. In addition to body fluid regulation and self-regulation, nerve regulation is also the most important regulation method of the human body. The three coordinated to play a regulatory role; the various organs and systems in humans and animals can be coordinated and unified into a whole.Ultrasound imaging is a common imaging examination method in the current auxiliary medical diagnosis. It can image the internal tissues and organs of the human body by using high-frequency sound waves to facilitate the observation of tissue morphology. It is suitable for organs with obvious interfaces such as the liver, gallbladder, kidney, bladder, uterus, ovary, prostate, and other organs [5]. Compared with plain X-ray film and computed tomography (CT), ultrasound imaging has the advantages of instantaneous imaging, simple operation, and no ionizing radiation. Transthoracic echocardiography (TEE) is the main detection method to evaluate the cardiac function of patients, which can directly obtain the cardiac hemodynamic status and show the characteristics of septic cardiomyopathy [6, 7]. However, the transmission and calculation of the image are affected by various objective factors in actual operation, so that the image resolution is reduced, bringing difficulty for a doctor’s diagnosis and judgment. The use of reconstruction algorithms for ultrasound image processing has become a key means to improve the quality of ultrasound imaging [8]. It is a critical issue to improve the effect of denoising while effectively retaining the original image in medical image processing. The ADF-U ultrasound reconstruction algorithm is based on diffusion theory, which is applied to the field of medical images and plays a huge role in image denoising. Diffusion theory refers to the recognition of the pixel value change caused by noise and the edge change caused by the gradient operator through characterizing the pixel value change, and it can weaken the noise gradient change to maintain the effect of removing noise and maintaining the edge [9]. Therefore, an ultrasound image reconstruction algorithm was constructed in this article based on the diffusion theory to explore the cardiac insufficiency and neuronal regulation of patients with sepsis.In summary, a new ultrasound image reconstruction algorithm ADF-U was proposed based on ADF and was applied to E-health ultrasound imaging diagnosis of 144 sepsis patients. By comparing the left and right ventricular parameters and neuropeptide levels of patients in the experimental, control, and blank group, the diagnostic application of the ultrasound imaging based on E-health in cardiac insufficiency and neuronal regulation of patients with sepsis is evaluated comprehensively.


## 2. Materials and Methods

### 2.1. Sample Selection

144 patients (71 males and 43 females) with sepsis who were admitted to the hospital from February 5, 2019, to June 14, 2020, were selected as the research objects. According to theHeart Failure Management Guidelines jointly published by the American Heart Association (AHA), American College of Cardiology (ACC), and American Heart Failure Association (HFSA) in 2017 [10], they were divided into an experimental group (78 sepsis patients with cardiac insufficiency) and a control group (66 sepsis patients with normal heart function). In addition, 58 healthy volunteers who received physical examination during the same period were included in the blank group. The experiment had been approved by the hospital’s medical ethics committee, and the patients and their family members had learned about the experiment and signed the informed consents.The inclusion criteria were defined as follows: patients with more than 2 scores of Sequential Organ Failure Assessment (SOFA), patients older than 18 years old, patients who had not received relevant treatment before the examination, and patients who voluntarily cooperated with the examination.The exclusion criteria were defined as follows: patients with organic heart disease, patients with incomplete basic clinical data, patients with cardiovascular and cerebrovascular diseases, patients with mental diseases, and patients who withdrew from treatment due to personal reasons.


### 2.2. Ultrasonic Imaging Examination

The examination was implemented with ultrasonic diagnostic instrument. Below indicators were measured in the normal standard section of subjects: left ventricular end-diastolic volume (LVEDV), LVESV, LVEF, SV, CO, LVFS, EPSS, early mitral valve diastolic blood velocity (E), advanced mitral valve diastolic blood velocity (A), isovolumic relaxation time (IVRT), E-peak deceleration time (EDT), A-peak duration (AD), left ventricular systolic pulmonary venous blood velocity (LVS), left ventricular early diastolic pulmonary venous blood flow velocity (LVED), Sm, and pulmonary valve annulus blood flow velocity (PVBV).Besides, the brain tissue samples were prepared. The brain neuropeptides of subjects were detected with quantitative polymerase chain reaction (PCR) technology, including the mRNA expression levels of POMC, CART, AgRP, and neuropeptide Y (NPY).


### 2.3. Ultrasonic Image Reconstruction Algorithm Based on Anisotropic Diffusion Filtering

Diffusion theory was extended to the field of medical images by Perona et al., and it had a wide range of applications in image denoising. Therefore, ADF [11] based on this theory was introduced to denoise the ultrasound images. It was assumed that the image was a thermal field; then each pixel could be regarded as a heat flow; then the anisotropic diffusion model could be expressed as follows:
(1)∂Qx,y,t∂t=divcx,y,t∇Qx,y,t=∇c∇Q+cx,y,tΔQ,c∇Q=e−∇Q2K2.In equation (1), ∇ represented the gradient operator, Δ represented the Laplacian operator, K indicated the coefficient of thermal conductivity, t represented the number of iterations, cx,y,t referred to diffusion rate, and Q represented the original ultrasound image. When the diffusion coefficient changed to the direction in which the image heat flow flows, the place where it flowed became smooth. Then, the pixel value corresponding to each pixel in an ultrasound image could be expressed as
(2)Qt+1=Qt+λcNx,y∇NIt+cSx,y∇SIt+cEx,y∇EIt+cWx,y∇WIt.In equation (2), N,S,E,andW, respectively, represented one direction of the image, and I represented the image pixel. Then, the gradient equations in the four directions could be expressed as follows:
(3)∇NIx,y=Ix,y−1−Ix,y,∇SIx,y=Ix,y+1−Ix,y,∇EIx,y=Ix−1,y−Ix,y,∇WIx,y=Ix+1,y−Ix,y.The equations of diffusion coefficients in the four directions were given as follows:(4)cNx,y=e−∇NI2K2,cSx,y=e−∇SI2K2,cEx,y=e−∇EI2K2,cWx,y=e−∇WI2K2.The above-mentioned new ultrasound image reconstruction algorithm was constructed based on ADF, which was set as ADF-U.


### 2.4. Quality Evaluation Indicators of Ultrasound Image

The ultrasound image algorithm BFU [12] based on bilateral filter and the ultrasound image algorithm WTU [13] based on wavelet threshold were introduced to compare with the ADF-U. The following evaluation indicators were selected to evaluate the quality of ultrasound images.


#### 2.4.1. Structural Similarity (SSIM)

It was used to quantify the change in image quality, which indicated the similarity between the reference image and the contrast image, and the equation could be obtained as follows:(5)SSIMx,y=2μxμy+c12σxy+c2μx2+μy2+c1σx2+σy2+c2.In equation (5), μx represented the average value of the reference image pixels and μx=∑i=1Nxi/N, μy represented the average value of the contrast image pixels and μy=∑i=1Nyi/N, σx2 stood for the variance of the reference image pixels and σx2=∑i=1Nx−iμx2/N−1, σy2 represented the variance of the contrast image pixels and σy2=∑i=1Ny−iμy2/N−1, σxy represented the covariance of the reference image and the contrast image and σxy=∑i=1Nx−iμxy−iμy/N−1, and c1 and c2 were all stability constants.


#### 2.4.2. MSE

It was assumed that the size of an image wasM×N; then, the MSE could be calculated with the following equation:
(6)MSE=∑i=1M∑j=1Ngi,j−fi,j2M×N.In the above equation,gi,j referred to the contrast image, and fi,j referred to the reference image.


#### 2.4.3. Average Gradient (AG)

It represented the difference between the edge pixels and the details of the image, reflecting the resolution of the image. Its equation was given as follows:(7)∇g¯=∑i=0M−∑j=0N−1ΔIx2+ΔIy2/2M×N.In equation (7), M represented the number of points in the image, N represented the number of lines in the image, and ΔIx2 and ΔIy2 represented the gray values of adjacent pixels.


#### 2.4.4. PSNR

It could be calculated with the following equation:(8)PSNR=10log2552×M×N∑m=1M∑n=1Ngm,n−fm,n2.In the above equation,M×N represented the number of image elements, fm,n represented unprocessed image data, and gm,n represented processed image data.


### 2.5. Statistical Analysis

The data was processed and analyzed by SPSS19.0 version statistical software, the measurement data was expressed bymean±standarddeviation (x¯±s), and the count data was given by percentage (%). The age, height, weight, male ratio, LVESV, SV, LVEDV, LVFS, LVEF, EPSS, CO, IVRT, EDT, AD, E, A, LVS, LVED, Sm, PVBV, POMC, CART, AgRP, and NPY of patients in three groups were compared by using the analysis of variance. The SSIM, running time, PSNR, MSE, and AG of ADF-U algorithm, BFU algorithm, and WTU algorithm were compared and analyzed with a paired t-test. The difference was statistically significant at P<0.05.


## 2.1. Sample Selection

144 patients (71 males and 43 females) with sepsis who were admitted to the hospital from February 5, 2019, to June 14, 2020, were selected as the research objects. According to theHeart Failure Management Guidelines jointly published by the American Heart Association (AHA), American College of Cardiology (ACC), and American Heart Failure Association (HFSA) in 2017 [10], they were divided into an experimental group (78 sepsis patients with cardiac insufficiency) and a control group (66 sepsis patients with normal heart function). In addition, 58 healthy volunteers who received physical examination during the same period were included in the blank group. The experiment had been approved by the hospital’s medical ethics committee, and the patients and their family members had learned about the experiment and signed the informed consents.The inclusion criteria were defined as follows: patients with more than 2 scores of Sequential Organ Failure Assessment (SOFA), patients older than 18 years old, patients who had not received relevant treatment before the examination, and patients who voluntarily cooperated with the examination.The exclusion criteria were defined as follows: patients with organic heart disease, patients with incomplete basic clinical data, patients with cardiovascular and cerebrovascular diseases, patients with mental diseases, and patients who withdrew from treatment due to personal reasons.


## 2.2. Ultrasonic Imaging Examination

The examination was implemented with ultrasonic diagnostic instrument. Below indicators were measured in the normal standard section of subjects: left ventricular end-diastolic volume (LVEDV), LVESV, LVEF, SV, CO, LVFS, EPSS, early mitral valve diastolic blood velocity (E), advanced mitral valve diastolic blood velocity (A), isovolumic relaxation time (IVRT), E-peak deceleration time (EDT), A-peak duration (AD), left ventricular systolic pulmonary venous blood velocity (LVS), left ventricular early diastolic pulmonary venous blood flow velocity (LVED), Sm, and pulmonary valve annulus blood flow velocity (PVBV).Besides, the brain tissue samples were prepared. The brain neuropeptides of subjects were detected with quantitative polymerase chain reaction (PCR) technology, including the mRNA expression levels of POMC, CART, AgRP, and neuropeptide Y (NPY).


## 2.3. Ultrasonic Image Reconstruction Algorithm Based on Anisotropic Diffusion Filtering

Diffusion theory was extended to the field of medical images by Perona et al., and it had a wide range of applications in image denoising. Therefore, ADF [11] based on this theory was introduced to denoise the ultrasound images. It was assumed that the image was a thermal field; then each pixel could be regarded as a heat flow; then the anisotropic diffusion model could be expressed as follows:
(1)∂Qx,y,t∂t=divcx,y,t∇Qx,y,t=∇c∇Q+cx,y,tΔQ,c∇Q=e−∇Q2K2.In equation (1), ∇ represented the gradient operator, Δ represented the Laplacian operator, K indicated the coefficient of thermal conductivity, t represented the number of iterations, cx,y,t referred to diffusion rate, and Q represented the original ultrasound image. When the diffusion coefficient changed to the direction in which the image heat flow flows, the place where it flowed became smooth. Then, the pixel value corresponding to each pixel in an ultrasound image could be expressed as
(2)Qt+1=Qt+λcNx,y∇NIt+cSx,y∇SIt+cEx,y∇EIt+cWx,y∇WIt.In equation (2), N,S,E,andW, respectively, represented one direction of the image, and I represented the image pixel. Then, the gradient equations in the four directions could be expressed as follows:
(3)∇NIx,y=Ix,y−1−Ix,y,∇SIx,y=Ix,y+1−Ix,y,∇EIx,y=Ix−1,y−Ix,y,∇WIx,y=Ix+1,y−Ix,y.The equations of diffusion coefficients in the four directions were given as follows:(4)cNx,y=e−∇NI2K2,cSx,y=e−∇SI2K2,cEx,y=e−∇EI2K2,cWx,y=e−∇WI2K2.The above-mentioned new ultrasound image reconstruction algorithm was constructed based on ADF, which was set as ADF-U.


## 2.4. Quality Evaluation Indicators of Ultrasound Image

The ultrasound image algorithm BFU [12] based on bilateral filter and the ultrasound image algorithm WTU [13] based on wavelet threshold were introduced to compare with the ADF-U. The following evaluation indicators were selected to evaluate the quality of ultrasound images.


### 2.4.1. Structural Similarity (SSIM)

It was used to quantify the change in image quality, which indicated the similarity between the reference image and the contrast image, and the equation could be obtained as follows:(5)SSIMx,y=2μxμy+c12σxy+c2μx2+μy2+c1σx2+σy2+c2.In equation (5), μx represented the average value of the reference image pixels and μx=∑i=1Nxi/N, μy represented the average value of the contrast image pixels and μy=∑i=1Nyi/N, σx2 stood for the variance of the reference image pixels and σx2=∑i=1Nx−iμx2/N−1, σy2 represented the variance of the contrast image pixels and σy2=∑i=1Ny−iμy2/N−1, σxy represented the covariance of the reference image and the contrast image and σxy=∑i=1Nx−iμxy−iμy/N−1, and c1 and c2 were all stability constants.


### 2.4.2. MSE

It was assumed that the size of an image wasM×N; then, the MSE could be calculated with the following equation:
(6)MSE=∑i=1M∑j=1Ngi,j−fi,j2M×N.In the above equation,gi,j referred to the contrast image, and fi,j referred to the reference image.


### 2.4.3. Average Gradient (AG)

It represented the difference between the edge pixels and the details of the image, reflecting the resolution of the image. Its equation was given as follows:(7)∇g¯=∑i=0M−∑j=0N−1ΔIx2+ΔIy2/2M×N.In equation (7), M represented the number of points in the image, N represented the number of lines in the image, and ΔIx2 and ΔIy2 represented the gray values of adjacent pixels.


### 2.4.4. PSNR

It could be calculated with the following equation:(8)PSNR=10log2552×M×N∑m=1M∑n=1Ngm,n−fm,n2.In the above equation,M×N represented the number of image elements, fm,n represented unprocessed image data, and gm,n represented processed image data.


## 2.4.1. Structural Similarity (SSIM)

It was used to quantify the change in image quality, which indicated the similarity between the reference image and the contrast image, and the equation could be obtained as follows:(5)SSIMx,y=2μxμy+c12σxy+c2μx2+μy2+c1σx2+σy2+c2.In equation (5), μx represented the average value of the reference image pixels and μx=∑i=1Nxi/N, μy represented the average value of the contrast image pixels and μy=∑i=1Nyi/N, σx2 stood for the variance of the reference image pixels and σx2=∑i=1Nx−iμx2/N−1, σy2 represented the variance of the contrast image pixels and σy2=∑i=1Ny−iμy2/N−1, σxy represented the covariance of the reference image and the contrast image and σxy=∑i=1Nx−iμxy−iμy/N−1, and c1 and c2 were all stability constants.


## 2.4.2. MSE

It was assumed that the size of an image wasM×N; then, the MSE could be calculated with the following equation:
(6)MSE=∑i=1M∑j=1Ngi,j−fi,j2M×N.In the above equation,gi,j referred to the contrast image, and fi,j referred to the reference image.


## 2.4.3. Average Gradient (AG)

It represented the difference between the edge pixels and the details of the image, reflecting the resolution of the image. Its equation was given as follows:(7)∇g¯=∑i=0M−∑j=0N−1ΔIx2+ΔIy2/2M×N.In equation (7), M represented the number of points in the image, N represented the number of lines in the image, and ΔIx2 and ΔIy2 represented the gray values of adjacent pixels.


## 2.4.4. PSNR

It could be calculated with the following equation:(8)PSNR=10log2552×M×N∑m=1M∑n=1Ngm,n−fm,n2.In the above equation,M×N represented the number of image elements, fm,n represented unprocessed image data, and gm,n represented processed image data.


## 2.5. Statistical Analysis

The data was processed and analyzed by SPSS19.0 version statistical software, the measurement data was expressed bymean±standarddeviation (x¯±s), and the count data was given by percentage (%). The age, height, weight, male ratio, LVESV, SV, LVEDV, LVFS, LVEF, EPSS, CO, IVRT, EDT, AD, E, A, LVS, LVED, Sm, PVBV, POMC, CART, AgRP, and NPY of patients in three groups were compared by using the analysis of variance. The SSIM, running time, PSNR, MSE, and AG of ADF-U algorithm, BFU algorithm, and WTU algorithm were compared and analyzed with a paired t-test. The difference was statistically significant at P<0.05.


## 3. Results

### 3.1. Basic Information of Subjects in Three Groups

As shown in Figure1, for patients in the experimental group, the age was 62.41±13.63 years, the height was 168.45±10.77 cm, the male ratio was 60.14%, and the weight was 65.73±7.44 kg; for patients in the control group, the age was 64.72±15.08 years, the height was 169.72±12.84 cm, the male ratio was 58.48%, and the weight was 67.21±8.56 kg; for patients in the blank group, the age, height, male ratio, and weight were 63.88±11.65 years, 170.63±9.64 cm, 58.22%, and 66.44±9.04 kg, respectively. Of which, the differences in age, height, weight, and male ratio of the three groups of patients were not statistically significant, but they were comparable (P>0.05). Figure 2 displays the cardiac ultrasound images of the three groups of subjects. Figure 2(a) shows that the anterior leaflet of the mitral valve cusp was echoed and thickened, the valve body formed a dome-like protrusion to the left ventricular outflow tract, the posterior leaflet body and cusp were both thickened, the echo of the posterior chordae was enhanced and thickened, and the valve opening was restricted. Figure 2(b) illustrates that the right atrium and the right ventricle were enlarged, the coronary sinus was enlarged, the anterior leaflet opens closed to the ventricular septum, the chordae echoes of the left and right ventricles were enhanced and shortened, and the posterior leaflet of the mitral valve cusp was thickened and poorly aligned with the anterior leaflet. Figure 2(c) shows the left ventricle, right ventricle, interventricular septum, mitral valve, and other cardiac structures, which belonged to normal cardiac ultrasound.Figure 1
Comparison on basic information of subjects in three groups.Figure 2
Heart ultrasound images of three groups of subjects: (a) the ultrasound image of a patient in the experimental group; (b) the ultrasound image of a patient in the control group; (c) the image of a subject in the blank group.
(a)(b)(c)


### 3.2. Comparison on Performance Indicators of Various Algorithms

As shown in Figure3, for the BFU algorithm, the SSIM was 0.834, the MSE was 8.103, the AG was 7.395, the PSNR was 37.663 dB, and the running time was 1.828 s; for the WTU algorithm, the SSIM, MSE, AG, PSNR, and running time were 0.827, 7.846, 7.631, 39.061 dB, and 1.744 s, respectively; and for the proposed ADF-U algorithm, the SSIM, MSE, AG, PSNR, and running time were 0.916, 5.871, 10.084, 61.514 dB, and 1.185 s, respectively. Of which, the running time, AG, and PSNR of the ADF-U algorithm were significantly greater than those of the other two algorithms, and the differences were statistically significant (P<0.05); MSE of the ADF-U algorithm was sharply smaller than that of BFU and WTU algorithm, and the difference was statistically meaningful (P<0.05); SSIM of the ADF-U algorithm was not statistically significant in the difference comparing with the BFU and WTU algorithm (P>0.05). Figure 4 reveals the reconstructed ultrasound images with different algorithms. The resolution and contrast of the reconstructed image in Figure 4(c) were the best, the noise and artifacts were significantly reduced, and the overall image quality was good.Figure 3
Comparison on performance indicators of different algorithms: (a) the comparison on SSIM and running time; (b) the results of PSNR; (c) the comparison results of MSE and AG.∗ indicates that the difference with the ADF-U algorithm was statistically significant (P<0.05).
(a)(b)(c)Figure 4
Reconstructed ultrasound images of various algorithms: (a) the original image; (b–d) the reconstructed images with the BFU, WTU, and ADF-U algorithms, respectively.
(a)(b)(c)(d)


### 3.3. Comparison on Left Ventricular Contraction Parameters of Three Groups of Patients

The comparison results for left ventricular contraction parameters of three groups of patients are given in Figure5. The level of LVESV, LVEDV, SV, LVEF, CO, LVFS, and EPSS for patients in the experimental group was 68.63±11.53 mL, 103.57±20.51 mL, 65.37±11.52 mL, 53.66±8.36%, 5.35±1.27 L/min, 29.51±5.83%, and 1.63±0.26 cm, respectively; the level of LVESV, LVEDV, SV, LVEF, CO, LVFS, and EPSS for patients in the control group was 53.24±9.85 mL, 142.61±23.07 mL, 80.82±8.92 mL, 66.72±10.45%, 7.28±2.06 L/min, 34.17±6.99%, and 1.13±0.35 cm, respectively; and the level of LVESV, LVEDV, SV, LVEF, CO, LVFS, and EPSS for subjects in the blank group was 56.37±10.44mL, 144.52±18.64mL, 83.31±12.15mL, 68.25±9.53%, 7.62±1.95L/min, 35.37±7.41%, and 1.06±0.11cm, respectively. The LVESV and EPSS levels in the experimental group were obviously higher than those of the control and blank group, and the differences were statistically significant (P<0.05); the levels of SV, LVEDV, LVFS, LVEF, and CO in the experimental group were greatly lower than the levels in the control and blank groups, and the differences had statistical significances (P<0.05).Figure 5
The comparison results for left ventricular contraction parameters of three groups of patients: (a) the comparison on levels of LVESV, SV, and LVEDV; (b) the levels of LVFS and LVEF; (c) the comparison on levels of EPSS and CO.∗ suggests that the different had a statistical significance comparing with the experimental group (P<0.05).
(a)(b)(c)


### 3.4. Comparison on Left Ventricular Diastolic Parameters of Subjects in Three Groups

The comparison on left ventricular diastolic parameters of subjects in three groups is given in Figure6. The level of E, A, IVRT, EDT, AD, LVS, and LVED for patients in the experimental group was 0.805±0.117m/s, 0.986±0.134m/s, 93.62±10.58ms, 127.75±22.56ms, 126.42±8.26ms, 0.371±0.063m/s, and 0.472±0.061m/s, respectively; the level of E, A, IVRT, EDT, AD, LVS, and LVED for patients in the control group was 0.793±0.235m/s, 1.032±0.168m/s, 81.61±9.62ms, 96.72±17.83ms, 112.63±7.93ms, 0.462±0.041m/s, and 0.625±0.085m/s, respectively; and the level of E, A, IVRT, EDT, AD, LVS, and LVED for subjects in the blank group was 0.789±0.137m/s, 1.074±0.175m/s, 79.52±8.94ms, 97.31±20.30ms, 109.48±8.51ms, 0.451±0.038m/s, and 0.621±0.087m/s, respectively. The IVRT, EDT, and AD in the experimental group were considerably higher than those of the control group and the blank group, and the differences were statistically significant (P<0.05); the LVS and LVED in the experimental group were lower greatly than those in the control group and blank group, and the differences had statistical significances (P<0.05); and the difference in E and A of the experimental group to the control and blank group was not statistically significant (P>0.05).Figure 6
The comparison on left ventricular diastolic parameters of subjects in three groups: (a) the levels of IVRT, EDT, and AD; (b) the levels of E and A; (c) the levels of LVS and LVED.∗ suggests that the difference with the experimental group had a statistical significance (P<0.05).
(a)(b)(c)


### 3.5. Comparison on Right Ventricular Parameters for Subjects in Three Groups

Figure7 discloses the comparison results for right ventricular parameters of subjects in each group. The level of Sm and PVBV for patients in experimental group was 11.56±2.37 cm/s and 1.076±0.116 m/s, respectively; the level of Sm and PVBV for patients in control group was 14.85±2.16 cm/s and 1.224±0.231 m/s, respectively; and the level of Sm and PVBV for subjects in blank group was 15.23±1.96 cm/s and 1317±0.174 m/s, respectively. The levels of these two indicators for subjects in the experimental group were lower obviously than those in the control and blank group, and the differences had statistical significances (P<0.05).Figure 7
The comparison results for right ventricular parameters of subjects in each group: (a) the Sm levels in three groups; (b) the PVBV levels in three groups.∗ indicates that there was a statistical significance for difference comparing with the experimental group (P<0.05).
(a)(b)


### 3.6. Comparison for Neuropeptides in Subjects of Each Group

As shown in Figure8, the comparison for neuropeptides in subjects of each group revealed that the mRNA level of POMC, CART, AgRP, and NPY in patients in the experimental group was 4.27±0.41,1.84±0.57,0.57±0.24,and0.49±0.17, respectively; the mRNA level of POMC CART, AgRP, and NPY in patients in the control group was 1.25±0.36,1.08±0.34,1.07±0.42,and0.97±0.21, respectively; and the mRNA level of POMC CART, AgRP, and NPY in subjects in the blank group was 1.17±0.26,1.07±0.42,0.96±0.27,and1.06±0.38, respectively. The mRNA levels of POMC and CART in the experimental group were higher sharply than those in the control and blank group, and the differences were significant statistically (P<0.05); the mRNA levels of AgRP and NPY in the experimental group were lower observably than those in the control and blank group, and the differences were statistically meaningful (P<0.05).Figure 8
The comparison for neuropeptides in subjects of each group: (a) the mRNA of POMC and CART; (b) the mRNA of AgRP and NPY.∗ suggests that the difference with experimental group had a statistical meaningfulness (P<0.05).
(a)(b)


## 3.1. Basic Information of Subjects in Three Groups

As shown in Figure1, for patients in the experimental group, the age was 62.41±13.63 years, the height was 168.45±10.77 cm, the male ratio was 60.14%, and the weight was 65.73±7.44 kg; for patients in the control group, the age was 64.72±15.08 years, the height was 169.72±12.84 cm, the male ratio was 58.48%, and the weight was 67.21±8.56 kg; for patients in the blank group, the age, height, male ratio, and weight were 63.88±11.65 years, 170.63±9.64 cm, 58.22%, and 66.44±9.04 kg, respectively. Of which, the differences in age, height, weight, and male ratio of the three groups of patients were not statistically significant, but they were comparable (P>0.05). Figure 2 displays the cardiac ultrasound images of the three groups of subjects. Figure 2(a) shows that the anterior leaflet of the mitral valve cusp was echoed and thickened, the valve body formed a dome-like protrusion to the left ventricular outflow tract, the posterior leaflet body and cusp were both thickened, the echo of the posterior chordae was enhanced and thickened, and the valve opening was restricted. Figure 2(b) illustrates that the right atrium and the right ventricle were enlarged, the coronary sinus was enlarged, the anterior leaflet opens closed to the ventricular septum, the chordae echoes of the left and right ventricles were enhanced and shortened, and the posterior leaflet of the mitral valve cusp was thickened and poorly aligned with the anterior leaflet. Figure 2(c) shows the left ventricle, right ventricle, interventricular septum, mitral valve, and other cardiac structures, which belonged to normal cardiac ultrasound.Figure 1
Comparison on basic information of subjects in three groups.Figure 2
Heart ultrasound images of three groups of subjects: (a) the ultrasound image of a patient in the experimental group; (b) the ultrasound image of a patient in the control group; (c) the image of a subject in the blank group.
(a)(b)(c)


## 3.2. Comparison on Performance Indicators of Various Algorithms

As shown in Figure3, for the BFU algorithm, the SSIM was 0.834, the MSE was 8.103, the AG was 7.395, the PSNR was 37.663 dB, and the running time was 1.828 s; for the WTU algorithm, the SSIM, MSE, AG, PSNR, and running time were 0.827, 7.846, 7.631, 39.061 dB, and 1.744 s, respectively; and for the proposed ADF-U algorithm, the SSIM, MSE, AG, PSNR, and running time were 0.916, 5.871, 10.084, 61.514 dB, and 1.185 s, respectively. Of which, the running time, AG, and PSNR of the ADF-U algorithm were significantly greater than those of the other two algorithms, and the differences were statistically significant (P<0.05); MSE of the ADF-U algorithm was sharply smaller than that of BFU and WTU algorithm, and the difference was statistically meaningful (P<0.05); SSIM of the ADF-U algorithm was not statistically significant in the difference comparing with the BFU and WTU algorithm (P>0.05). Figure 4 reveals the reconstructed ultrasound images with different algorithms. The resolution and contrast of the reconstructed image in Figure 4(c) were the best, the noise and artifacts were significantly reduced, and the overall image quality was good.Figure 3
Comparison on performance indicators of different algorithms: (a) the comparison on SSIM and running time; (b) the results of PSNR; (c) the comparison results of MSE and AG.∗ indicates that the difference with the ADF-U algorithm was statistically significant (P<0.05).
(a)(b)(c)Figure 4
Reconstructed ultrasound images of various algorithms: (a) the original image; (b–d) the reconstructed images with the BFU, WTU, and ADF-U algorithms, respectively.
(a)(b)(c)(d)


## 3.3. Comparison on Left Ventricular Contraction Parameters of Three Groups of Patients

The comparison results for left ventricular contraction parameters of three groups of patients are given in Figure5. The level of LVESV, LVEDV, SV, LVEF, CO, LVFS, and EPSS for patients in the experimental group was 68.63±11.53 mL, 103.57±20.51 mL, 65.37±11.52 mL, 53.66±8.36%, 5.35±1.27 L/min, 29.51±5.83%, and 1.63±0.26 cm, respectively; the level of LVESV, LVEDV, SV, LVEF, CO, LVFS, and EPSS for patients in the control group was 53.24±9.85 mL, 142.61±23.07 mL, 80.82±8.92 mL, 66.72±10.45%, 7.28±2.06 L/min, 34.17±6.99%, and 1.13±0.35 cm, respectively; and the level of LVESV, LVEDV, SV, LVEF, CO, LVFS, and EPSS for subjects in the blank group was 56.37±10.44mL, 144.52±18.64mL, 83.31±12.15mL, 68.25±9.53%, 7.62±1.95L/min, 35.37±7.41%, and 1.06±0.11cm, respectively. The LVESV and EPSS levels in the experimental group were obviously higher than those of the control and blank group, and the differences were statistically significant (P<0.05); the levels of SV, LVEDV, LVFS, LVEF, and CO in the experimental group were greatly lower than the levels in the control and blank groups, and the differences had statistical significances (P<0.05).Figure 5
The comparison results for left ventricular contraction parameters of three groups of patients: (a) the comparison on levels of LVESV, SV, and LVEDV; (b) the levels of LVFS and LVEF; (c) the comparison on levels of EPSS and CO.∗ suggests that the different had a statistical significance comparing with the experimental group (P<0.05).
(a)(b)(c)


## 3.4. Comparison on Left Ventricular Diastolic Parameters of Subjects in Three Groups

The comparison on left ventricular diastolic parameters of subjects in three groups is given in Figure6. The level of E, A, IVRT, EDT, AD, LVS, and LVED for patients in the experimental group was 0.805±0.117m/s, 0.986±0.134m/s, 93.62±10.58ms, 127.75±22.56ms, 126.42±8.26ms, 0.371±0.063m/s, and 0.472±0.061m/s, respectively; the level of E, A, IVRT, EDT, AD, LVS, and LVED for patients in the control group was 0.793±0.235m/s, 1.032±0.168m/s, 81.61±9.62ms, 96.72±17.83ms, 112.63±7.93ms, 0.462±0.041m/s, and 0.625±0.085m/s, respectively; and the level of E, A, IVRT, EDT, AD, LVS, and LVED for subjects in the blank group was 0.789±0.137m/s, 1.074±0.175m/s, 79.52±8.94ms, 97.31±20.30ms, 109.48±8.51ms, 0.451±0.038m/s, and 0.621±0.087m/s, respectively. The IVRT, EDT, and AD in the experimental group were considerably higher than those of the control group and the blank group, and the differences were statistically significant (P<0.05); the LVS and LVED in the experimental group were lower greatly than those in the control group and blank group, and the differences had statistical significances (P<0.05); and the difference in E and A of the experimental group to the control and blank group was not statistically significant (P>0.05).Figure 6
The comparison on left ventricular diastolic parameters of subjects in three groups: (a) the levels of IVRT, EDT, and AD; (b) the levels of E and A; (c) the levels of LVS and LVED.∗ suggests that the difference with the experimental group had a statistical significance (P<0.05).
(a)(b)(c)


## 3.5. Comparison on Right Ventricular Parameters for Subjects in Three Groups

Figure7 discloses the comparison results for right ventricular parameters of subjects in each group. The level of Sm and PVBV for patients in experimental group was 11.56±2.37 cm/s and 1.076±0.116 m/s, respectively; the level of Sm and PVBV for patients in control group was 14.85±2.16 cm/s and 1.224±0.231 m/s, respectively; and the level of Sm and PVBV for subjects in blank group was 15.23±1.96 cm/s and 1317±0.174 m/s, respectively. The levels of these two indicators for subjects in the experimental group were lower obviously than those in the control and blank group, and the differences had statistical significances (P<0.05).Figure 7
The comparison results for right ventricular parameters of subjects in each group: (a) the Sm levels in three groups; (b) the PVBV levels in three groups.∗ indicates that there was a statistical significance for difference comparing with the experimental group (P<0.05).
(a)(b)


## 3.6. Comparison for Neuropeptides in Subjects of Each Group

As shown in Figure8, the comparison for neuropeptides in subjects of each group revealed that the mRNA level of POMC, CART, AgRP, and NPY in patients in the experimental group was 4.27±0.41,1.84±0.57,0.57±0.24,and0.49±0.17, respectively; the mRNA level of POMC CART, AgRP, and NPY in patients in the control group was 1.25±0.36,1.08±0.34,1.07±0.42,and0.97±0.21, respectively; and the mRNA level of POMC CART, AgRP, and NPY in subjects in the blank group was 1.17±0.26,1.07±0.42,0.96±0.27,and1.06±0.38, respectively. The mRNA levels of POMC and CART in the experimental group were higher sharply than those in the control and blank group, and the differences were significant statistically (P<0.05); the mRNA levels of AgRP and NPY in the experimental group were lower observably than those in the control and blank group, and the differences were statistically meaningful (P<0.05).Figure 8
The comparison for neuropeptides in subjects of each group: (a) the mRNA of POMC and CART; (b) the mRNA of AgRP and NPY.∗ suggests that the difference with experimental group had a statistical meaningfulness (P<0.05).
(a)(b)


## 4. Discussion

Sepsis is mainly a life-threatening and serious organ failure caused by the body’s immune out-of-control response due to infection. The heart is one of the most involved organs. About 40% of sepsis patients suffer from myocardial damage. Therefore, the examination and diagnosis of early cardiac function of patients are a key method commonly used in clinical practice [14]. The reconstruction process of ultrasound image transmission and calculation is affected by various objective factors, such as the reduction of image resolution, which brings difficulties to the diagnosis and judgment of doctors in medical image processing. In addition, improving the denoising effect while effectively retaining the original image is also an important issue that needs to be solved urgently. The reconstruction of medical images has become a key means to improve the quality of ultrasound imaging. The ultrasound reconstruction algorithm uses diffusion theory to weaken the gradient change of noise through the pixel value change and edge change caused by noise, while achieving the dual purpose of removing noise and maintaining the complete structure of the image. It has a wide range of application prospects in the ultrasound field. Therefore, an ultrasound image reconstruction algorithm was constructed based on the diffusion theory to explore the cardiac dysfunction and neuronal adjustment of patients with sepsis. A new ultrasound image reconstruction algorithm ADF-U was constructed based on ADF and applied to 144 cases of sepsis patients with E-health ultrasound image diagnosis. Firstly, the BFU and WTU algorithms were introduced for comparison with the proposed ADF-U algorithm, and it was found that the running time, AG, and PSNR of the ADF-U algorithm were observably greater than those of the BFU and WTU algorithm, while its MSE was significantly smaller than those of the other two algorithms (P<0.05). It was similar to the research results of al Zoubi et al. [15], showing that the constructed ADF-U algorithm could not only effectively improve the definition and SNR of ultrasound images but also improve the efficiency of reconstruction operation based on the comparison with the BFU and WTU algorithms.The LVESV and EPSS levels of the experimental group were significantly higher than those of the control group and the blank group, while the levels of SV, LVEDV, LVFS, LVEF, and CO were largely lower than those in the control group and the blank group (P<0.05), indicating that sepsis patients with cardiac insufficiency suffered great change in left ventricular systolic function and server atrophy of myocardial function. The levels of IVRT, EDT, and AD of the experimental group were remarkably higher than those of the control group and the blank group, while the levels of LVS and LVED were outstandingly lower than those of the other two groups (P<0.05), which was similar with the results of Léger et al. [16]; IVRT was an important indicator reflecting the body’s myocardial diastolic function and was affected by the left ventricular diastolic rate and left atrial pressure, which indicated that patients with sepsis and cardiac insufficiency had reduced left ventricular myocardial relaxation and disordered myocardial cell energy metabolism. During the onset of sepsis, right ventricular dysfunction is affected by many factors. It was found in this article that the levels of Sm and PVBV in the experimental group were observably lower than those in the other two groups, and the differences were statistically significant (P<0.05). The reason may be that function of the right ventricle is similar to that of the left ventricle, so it was affected by diastolic and systolic activity of the left ventricle obviously. The mRNA levels of POMC and CART in the experimental group were visibly higher than those in the control and blank group (P<0.05), which was similar to the results of Guo et al. [17]; POMC and CART were the main secretions of POMC neurons; they could exert a suppression effect in the body, which indicated that patients with sepsis and cardiac insufficiency could activate hypothalamic suppressor neurons, leading to reduced food intake and difficulty in maintaining energy metabolism balance. The mRNA levels of AgRP and NPY in the experimental group were significantly lower than those in the other two groups (P<0.05). AgRP and NPY are the main secretions of the feeding neurons AgRP, which suggests that patients with sepsis and cardiac insufficiency can reduce the expression of hypothalamic feeding neurons.


## 5. Conclusion

A new ultrasound image reconstruction algorithm ADF-U was proposed based on ADF; the BFU algorithm and WTU algorithm were introduced to compare the reconstruction effect with the proposed algorithm. The results of ultrasound image diagnosis based on the ADF-U algorithm showed that the new algorithm performed well in terms of image resolution, SNR, image integrity, and reconstruction efficiency. Compared with sepsis patients with normal cardiac function, patients with sepsis and cardiac insufficiency had greater changes in left ventricular systolic and diastolic functions, a higher atrophy of myocardial function, increased expression of POMC in hypothalamic suppressor neurons, and decreased expression of AgRP in feeding neurons. However, the ultrasound measurement is affected by physiological position of right ventricle and its own physiological structure, so that the relevant parameters are less obtained. Later, it will consider increasing the patient sample size and further analyze the right ventricular ultrasound diagnosis. In conclusion, the results of this article provide a theoretical basis for the diagnosis of cardiac function and neuronal changes in patients with sepsis.



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*Source: 9415694-2022-01-06.xml*