Correcting motion artifacts in coronary computed tomography angiography images using a dual-zone cycle generative adversarial network

2021 ◽  
pp. 1-19
Author(s):  
Fuquan Deng ◽  
Yingting Zeng ◽  
Yanbin Shi ◽  
Huiying Wu ◽  
Yu Wu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Coronary Artery ◽  
Coronary Computed Tomography Angiography ◽  
Spiral Ct ◽  
Motion Artifacts ◽  
Generative Adversarial Network ◽  
Clinical Images ◽  
Artifact Correction ◽  
Adversarial Network ◽  
Original Dataset

BACKGROUND: Coronary computed tomography angiography (CCTA) is a noninvasive imaging modality to detect and diagnose coronary artery disease. Due to the limitations of equipment and the patient’s physiological condition, some CCTA images collected by 64-slice spiral computed tomography (CT) have motion artifacts in the right coronary artery, left circumflex coronary artery and other positions. OBJECTIVE: To perform coronary artery motion artifact correction on clinical CCTA images collected by Siemens 64-slice spiral CT and evaluate the artifact correction method. METHODS: We propose a novel method based on the generative adversarial network (GAN) to correct artifacts of CCTA clinical images. We use CCTA clinical images collected by 64-slice spiral CT as the original dataset. Pairs of regions of interest (ROIs) cropped from original dataset or images with and without motion artifacts are used to train the dual-zone GAN. When predicting the CCTA images, the network inputs only the clinical images with motion artifacts. RESULTS: Experiments show that this network effectively corrects CCTA motion artifacts. Regardless of ROIs or images, the peak signal to noise ratio (PSNR), structural similarity (SSIM), mean square error (MSE) and mean absolute error (MAE) of the generated images are greatly improved compared to those of the input data. In addition, based on scores from physicians, the average score for the coronary artery artifact correction of the output images is higher. CONCLUSIONS: This study demonstrates that the dual-zone GAN has the excellent ability to correct motion artifacts in the coronary arteries and maintain the overall characteristics of CCTA clinical images.

Hypoplastic Left Circumflex Coronary Artery: Imaging Findings with Coronary Computed Tomography Angiography - A Case Report

2019 ◽  
Vol 15 (4) ◽  
pp. 427-429
Author(s):  
Asli Tanrivermis Sayit ◽  
Cetin Celenk
Keyword(s):  
Computed Tomography ◽  
Coronary Artery ◽  
Coronary Computed Tomography Angiography ◽  
Coronary Artery Imaging ◽  
Circumflex Coronary Artery ◽  
Circumflex Artery ◽  
Coronary Computed Tomography ◽  
True Incidence ◽  
Left Circumflex Artery ◽  
Artery Disease

<P>Background: Hypoplastic coronary artery disease is a rare congenital coronary artery anomaly. It is often detected incidentally, and its true incidence in the general population is not known. </P><P> Discussion: Symptoms of HCAD are syncope, palpitations, dyspnea, and chest pain. Also, arrhythmia and myocardial infarction can be seen; these can cause sudden death, especially in athletes and young people. Diagnosis is often made at autopsy. Conclusion: Here, we present the case of a 39-year-old male with isolated hypoplasia of the left circumflex artery detected by coronary Computed Tomography (CT) angiography who complained of palpitation.</P>

scholarly journals Clinical Relevance of Coronary Computed Tomography Angiography Beyond Coronary Artery Stenosis

2021 ◽  
Author(s):  
Mohammed Nooruddin Meah ◽  
Michelle C. Williams
Keyword(s):  
Computed Tomography ◽  
Coronary Artery ◽  
High Risk ◽  
Atherosclerotic Plaque ◽  
Computed Tomography Angiography ◽  
Coronary Computed Tomography Angiography ◽  
Calcium Score ◽  
Coronary Plaque ◽  
Coronary Computed Tomography ◽  
Low Attenuation Plaque

Background The capabilities of coronary computed tomography angiography (CCTA) have advanced significantly in the past decade. Its capacity to detect stenotic coronary arteries safely and consistently has led to a marked decline in invasive diagnostic angiography. However, CCTA can do much more than identify coronary artery stenoses. Method This review discusses applications of CCTA beyond coronary stenosis assessment, focusing in particular on the visual and quantitative analysis of atherosclerotic plaque. Results Established signs of visually assessed high-risk plaque on CT include positive remodeling, low-attenuation plaque, spotty calcification, and the napkin-ring sign, which correlate with the histological thin-cap fibroatheroma. Recently, quantification of plaque subtypes has further improved the assessment of coronary plaque on CT. Quantitatively assessed low-attenuation plaque, which correlates with the necrotic core of the thin-cap fibroatheroma, has demonstrated superiority over stenosis severity and coronary calcium score in predicting subsequent myocardial infarction. Current research aims to use radiomic and machine learning methods to further improve our understanding of high-risk atherosclerotic plaque subtypes identified on CCTA. Conclusion Despite rapid technological advances in the field of coronary computed tomography angiography, there remains a significant lag in routine clinical practice where use is often limited to lumenography. We summarize some of the most promising techniques that significantly improve the diagnostic and prognostic potential of CCTA. Key Points:  Citation Format

scholarly journals Use of Coronary Computed Tomography Angiography to Screen Hospital Employees with Cardiovascular Risk Factors

2021 ◽  
Vol 18 (10) ◽  
pp. 5462
Author(s):  
Po-Yi Li ◽  
Ru-Yih Chen ◽  
Fu-Zong Wu ◽  
Guang-Yuan Mar ◽  
Ming-Ting Wu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Computed Tomography ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Computed Tomography Angiography ◽  
Coronary Computed Tomography Angiography ◽  
Significant Risk ◽  
Coronary Computed Tomography ◽  
Hospital Employees ◽  
Artery Disease

The objective of this study was to determine how coronary computed tomography angiography (CCTA) can be employed to detect coronary artery disease in hospital employees, enabling early treatment and minimizing damage. All employees of our hospital were assessed using the Framingham Risk Score. Those with a 10-year risk of myocardial infarction or death of >10% were offered CCTA; the Coronary Artery Disease Reporting and Data System (CAD-RADS) score was the outcome. A total of 3923 hospital employees were included, and the number who had received CCTA was 309. Among these 309, 31 (10.0%) had a CAD-RADS score of 3–5, with 10 of the 31 (32.3%) requiring further cardiac catheterization; 161 (52.1%) had a score of 1–2; and 117 (37.9%) had a score of 0. In the multivariate logistic regression, only age of ≥ 55 years (p < 0.05), hypertension (p < 0.05), and hyperlipidemia (p < 0.05) were discovered to be significant risk factors for a CAD-RADS score of 3–5. Thus, regular and adequate control of chronic diseases is critical for patients, and more studies are required to be confirmed if there are more significant risk factors.

Towards Accuracy Enhancement of Age Group Classification Using Generative Adversarial Networks

2021 ◽  
pp. 1-17
Author(s):  
Khaled ELKarazle ◽  
Valliappan Raman ◽  
Patrick Then
Keyword(s):  
Age Estimation ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Original Dataset ◽  
Age Group Classification ◽  
Facial Images

Age estimation models can be employed in many applications, including soft biometrics, content access control, targeted advertising, and many more. However, as some facial images are taken in unrestrained conditions, the quality relegates, which results in the loss of several essential ageing features. This study investigates how introducing a new layer of data processing based on a super-resolution generative adversarial network (SRGAN) model can influence the accuracy of age estimation by enhancing the quality of both the training and testing samples. Additionally, we introduce a novel convolutional neural network (CNN) classifier to distinguish between several age classes. We train one of our classifiers on a reconstructed version of the original dataset and compare its performance with an identical classifier trained on the original version of the same dataset. Our findings reveal that the classifier which trains on the reconstructed dataset produces better classification accuracy, opening the door for more research into building data-centric machine learning systems.

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