scholarly journals Functional cardiac CT–Going beyond Anatomical Evaluation of Coronary Artery Disease with Cine CT, CT-FFR, CT Perfusion and Machine Learning

2020 ◽  
Vol 93 (1113) ◽  
pp. 20200349 ◽  
Author(s):  
Joyce Peper ◽  
Dominika Suchá ◽  
Martin Swaans ◽  
Tim Leiner
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Myocardial Perfusion ◽  
Coronary Arteries ◽  
Cardiac Ct ◽  
Ct Perfusion ◽  
Coronary Artery Stenoses ◽  
Cine Ct ◽  
Artery Disease

The aim of this review is to provide an overview of different functional cardiac CT techniques which can be used to supplement assessment of the coronary arteries to establish the significance of coronary artery stenoses. We focus on cine-CT, CT-FFR, CT-myocardial perfusion and how developments in machine learning can supplement these techniques.

scholarly journals Medical Prior Knowledge Guided Automatic Detection of Coronary Arteries Calcified Plaque with Cardiac CT

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2122
Author(s):  
Mengxue Zhao ◽  
Xiangjiu Che ◽  
Hualuo Liu ◽  
Quanle Liu
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Object Detection ◽  
Prior Knowledge ◽  
Coronary Arteries ◽  
Cardiac Ct ◽  
Medical Images ◽  
Calcified Plaque ◽  
Artery Disease ◽  
Plaque Detection

Calcified plaque in coronary arteries is one major cause and prediction of future coronary artery disease risk. Therefore, the detection of calcified plaque in coronary arteries is exceptionally significant in clinical for slowing coronary artery disease progression. At present, the Convolutional Neural Network (CNN) is exceedingly popular in natural images’ object detection field. Therefore, CNN in the object detection field of medical images also has a wide range of applications. However, many current calcified plaque detection methods in medical images are based on improving the CNN model algorithm, not on the characteristics of medical images. In response, we propose an automatic calcified plaque detection method in non-contrast-enhanced cardiac CT by adding medical prior knowledge. The training data merging with medical prior knowledge through data augmentation makes the object detection algorithm achieve a better detection result. In terms of algorithm, we employ a deep learning tool knows as Faster R-CNN in our method for locating calcified plaque in coronary arteries. To reduce the generation of redundant anchor boxes, Region Proposal Networks is replaced with guided anchoring. Experimental results show that the proposed method achieved a decent detection performance.

scholarly journals Machine Learning for Assessment of Coronary Artery Disease in Cardiac CT: A Survey

2019 ◽  
Vol 6 ◽  
Author(s):  
Nils Hampe ◽  
Jelmer M. Wolterink ◽  
Sanne G. M. van Velzen ◽  
Tim Leiner ◽  
Ivana Išgum
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Cardiac Ct ◽  
Artery Disease
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