Network-Based Approach for Modeling and Analyzing Coronary Angiography
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Significant intra-observer and inter-observer variability in the interpretation of coronary angiograms are reported. This variability is in part due to the common practices that rely on performing visual inspections by specialists (e.g., the thickness of coronaries). Quantitative Coronary Angiography (QCA) approaches are emerging to minimize observer’s error and furthermore perform predictions and analysis on angiography images. However, QCA approaches suffer from the same problem as they mainly rely on performing visual inspections by utilizing image processing techniques. In this work, we propose an approach to model and analyze the entire cardiovascular tree as a complex network derived from coronary angiography images. This approach enables to analyze the graph structure of coronary arteries. We conduct the assessments of network integration, degree distribution, and controllability on a healthy and a diseased coronary angiogram. Through our discussion and assessments, we propose modeling the cardiovascular system as a complex network is an essential phase to fully automate the interpretation of coronary angiographic images. We show how network science can provide a new perspective to look at coronary angiograms.
KeywordsComplex networks Coronary heart disease Angiography Quantitative coronary angiography Coronary network Complex systems
The authors acknowledge Professor Joaquín Goñi, School of Industrial Engineering at Purdue University, West Lafayette, USA and Dr. Sophoclis Sophocleous, Pulmonology Resident in Bethanien Hospital, Solingen, Germany for their help and guidance on this paper. We like to thank Mr. Javad Darivandpour, Ph.D. candidate in the Department of Computer Science at Purdue University, West Lafayette, USA for his constructive criticism of the manuscript. We would also like to show our gratitude to Dr. Saied Ravandi for his pearls of wisdom with us during the course of this research.
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