Prediction of the development of coronary atherosclerotic plaques using computational modeling in 3D reconstructed coronary arteries

Pleouras, Dimitrios S.; Sakellarios, Antonis I.; Loukas, Vasileios S.; Kyriakidis, Savvas; Fotiadis, Dimitrios I.

doi:10.1109/embc44109.2020.9176219

Cited by 2 publications

(1 citation statement)

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“…Using the three-dimensional reconstruction of plaque geometry from CCTA images, many geometric parameters can be accurately estimated, including the burden (i.e., the severity of stenosis), size, diameter, and composition of coronary artery plaques (Liu et al, 2021). The geometry of a plaque can influence the hemodynamic parameters, such as wall shear stress, which, in turn, influence the evolution of plaque geometry (Yamamoto et al, 2017;Arzani, 2020;Pleouras et al, 2020a). For example, the clinical observation and computational simulation have disclosed that plaques grow faster in the downstream areas than the upstream areas (Arzani, 2020).…”

Section: Introductionmentioning

confidence: 99%

Consistency in Geometry Among Coronary Atherosclerotic Plaques Extracted From Computed Tomography Angiography

et al. 2021

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Background: The three-dimensional (3D) geometry of coronary atherosclerotic plaques is associated with plaque growth and the occurrence of coronary artery disease. However, there is a lack of studies on the 3D geometric properties of coronary plaques. We aim to investigate if coronary plaques of different sizes are consistent in geometric properties.Methods: Nineteen cases with symptomatic stenosis caused by atherosclerotic plaques in the left coronary artery were included. Based on attenuation values on computed tomography angiography images, coronary atherosclerotic plaques and calcifications were identified, 3D reconstructed, and manually revised. Multidimensional geometric parameters were measured on the 3D models of plaques and calcifications. Linear and non-linear (i.e., power function) fittings were used to investigate the relationship between multidimensional geometric parameters (length, surface area, volume, etc.). Pearson correlation coefficient (r), R-squared, and p-values were used to evaluate the significance of the relationship. The analysis was performed based on cases and plaques, respectively. Significant linear relationship was defined as R-squared > 0.25 and p < 0.05.Results: In total, 49 atherosclerotic plaques and 56 calcifications were extracted. In the case-based analysis, significant linear relationships were found between number of plaques and number of calcifications (r = 0.650, p = 0.003) as well as total volume of plaques (r = 0.538, p = 0.018), between number of calcifications and total volume of plaques (r = 0.703, p = 0.001) as well as total volume of calcification (r = 0.646, p = 0.003), and between the total volumes of plaques and calcifications (r = 0.872, p < 0.001). In plaque-based analysis, the power function showed higher R-squared values than the linear function in fitting the relationships of multidimensional geometric parameters. Two presumptions of plaque geometry in different growth stages were proposed with simplified geometric models developed. In the proposed models, the exponents in the power functions of geometric parameters were in accordance with the fitted values.Conclusion: In patients with coronary artery disease, coronary plaques and calcifications are positively related in number and volume. Different coronary plaques are consistent in the relationship between geometry parameters in different dimensions.

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Section: Introductionmentioning

confidence: 99%

Consistency in Geometry Among Coronary Atherosclerotic Plaques Extracted From Computed Tomography Angiography

et al. 2021

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Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

2023

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Growth prediction technology is not only a practical application but also a crucial approach that strengthens the safety of image processing techniques. By supplementing the growth images obtained from the original images, especially in insufficient data sets, we can increase the robustness of machine learning. Therefore, predicting the growth of living organisms is an important technology that increases the safety of existing applications that target living organisms and can extend to areas not yet realized. This paper is a systematic literature review (SLR) investigating biological growth prediction based on the PRISMA 2020 guidelines. We systematically survey existing studies from 2017 to 2022 to provide other researchers with current trends. We searched four digital libraries—IEEE Xplore, ACM Digital Library, Science Direct, and Web of Science—and finally analyzed 47 articles. We summarize the methods used, year, features, accuracy, and dataset of each paper. In particular, we explained LSTM, GAN, and STN, the most frequently used methods among the 20 papers related to machine learning (40% of all papers).

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Prediction of the development of coronary atherosclerotic plaques using computational modeling in 3D reconstructed coronary arteries

Cited by 2 publications

References 12 publications

Consistency in Geometry Among Coronary Atherosclerotic Plaques Extracted From Computed Tomography Angiography

Consistency in Geometry Among Coronary Atherosclerotic Plaques Extracted From Computed Tomography Angiography

Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

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