Cardiovascular and Coronary Artery Imaging

Volume 1

1st Edition - November 24, 2021
Editors: Ayman S. El-Baz, Jasjit Suri
Language: English
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 2 2 7 0 6 - 0
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 2 2 7 0 7 - 7

Cardiovascular and Coronary Artery Imaging, Volume One covers state-of-the-art approaches for automated non-invasive systems in early cardiovascular disease diagnosis. The book i… Read more

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Cardiovascular and Coronary Artery Imaging, Volume One covers state-of-the-art approaches for automated non-invasive systems in early cardiovascular disease diagnosis. The book includes several prominent imaging modalities, such as MRI, CT and PET technologies. A special emphasis is placed on automated imaging analysis techniques, which are important to biomedical imaging analysis of the cardiovascular system. This is a comprehensive, multi-contributed reference work that details the latest developments in spatial, temporal and functional cardiac imaging.

Cover image
Title page
Table of Contents
Copyright
List of contributors
Chapter 1. Advanced coronary artery imaging: optical coherence tomography
Abstract
1.1 Introduction
1.2 Basic principles of light
1.3 Mechanism and technical modalities of OCT
1.4 Scanning techniques
1.5 Pullback
1.6 Image interpretation
1.7 Image artifact
1.8 Clinical applications
1.9 Safety and complications
1.10 Innovations of OCT
1.11 Clinical trials
References
Chapter 2. Technique of cardiac magnetic resonance imaging
Abstract
2.1 Introduction
2.2 Physical principles and pulse sequences
References
Chapter 3. The role of automated 12-lead ECG interpretation in the diagnosis and risk stratification of cardiovascular disease
Abstract
3.1 Introduction
3.2 Basic knowledge of ECG physiology
3.3 The 12-lead ECG
3.4 ECG signal processing
3.5 Cardiovascular diseases diagnosed by the 12-lead ECG
3.6 Automated ECG interpretation
3.7 “Logic” used in automated ECG interpretation systems
3.8 Machine learning and automated 12-lead ECG analysis
3.9 Basic principles of risk stratification
3.10 ECG-derived markers for risk stratification
3.11 Challenges and opportunities
References
Chapter 4. Extracting heterogeneous vessels in X-ray coronary angiography via machine learning
Abstract
4.1 Introduction
4.2 Related works
4.3 MCR-RPCA: motion coherency regularized RPCA for vessel extraction
4.4 SVS-net: sequential vessel segmentation via channel attention network
4.5 VRBC-t-TNN: accurate heterogeneous vessel extraction via tensor completion of X-ray coronary angiography backgrounds
4.6 Conclusion
Acknowledgments
References
Chapter 5. Assessing coronary artery disease using coronary computed tomography angiography
Abstract
5.1 Introduction
5.2 Patient selection
5.3 Spatial resolution
5.4 Temporal resolution
5.5 Technical issues in specific patient subgroups
5.6 Clinical trials comparing CCTA to other modalities
5.7 Conclusion
References
Chapter 6. Multimodality noninvasive cardiovascular imaging for the evaluation of coronary artery disease
Abstract
6.1 Introduction
6.2 Ischemic cascade
6.3 Exercise stress echocardiography
6.4 Pharmacologic stress echocardiography
6.5 Myocardial perfusion stress echocardiography
6.6 Left ventricular strain in exercise stress echocardiography
6.7 Limitations of stress echocardiography
6.8 Computed tomography coronary calcium score
6.9 Limitations of coronary artery calcium
6.10 Computed tomography coronary angiogram
6.11 Limitations of computed tomography coronary angiogram
6.12 Computed tomography in combination with single-photon emission tomography
6.13 Computed tomography in combination with positron emitting tomography
6.14 Limitations and strengths of positron emission tomography and SPECT imaging
6.15 CTCA and fractional flow reserve
6.16 Limitations of FFR CCTA
6.17 Cardiac magnetic resonance angiography
6.18 Conclusion
References
Chapter 7. Magnetic resonance imaging of ischemic heart disease
Abstract
7.1 Introduction
7.2 Cardiac MR imaging of myocardial infarction
7.3 MR indicators of myocardial infraction severity
7.4 Myocardial infarction complications
7.5 Future directions
References
Chapter 8. CT angiography of anomalous pulmonary veins
Abstract
8.1 Introduction
8.2 Classification
8.3 Anomalous in caliber of pulmonary veins
8.4 Total anomalous pulmonary venous return
8.5 Partial anomalous pulmonary venous return
8.6 Merits, limitations, and future directions
8.7 Conclusion
References
Further reading
Chapter 9. Machine learning to predict mortality risk in coronary artery bypass surgery
Abstract
9.1 Introduction
9.2 Principles and applications of machine learning
9.3 Conclusion
References
Chapter 10. Computed tomography angiography of congenital anomalies of pulmonary artery
Abstract
10.1 Introduction
10.2 Classification
10.3 Merits, limitations, and future directions
10.4 Conclusion
References
Chapter 11. Obstructive coronary artery disease diagnostics: machine learning approach for an effective preselection of patients
Abstract
11.1 Introduction
11.2 In search for additional diagnostic information
11.3 Materials and methods
11.4 Results
11.5 Conclusions
References
Chapter 12. Heart disease prediction using convolutional neural network
Abstract
12.1 Introduction
12.2 Materials
12.3 Methods
12.4 Conclusion/summary
Acknowledgments
Author contribution
Conflict of interest
References
Chapter 13. Gene polymorphism and the risk of coronary artery disease
Abstract
13.1 Introduction
13.2 Methodology
13.3 Results
13.4 Discussion
13.5 Conclusion
References
Chapter 14. Role of optical coherence tomography in borderline coronary lesions
Abstract
14.1 Introduction
14.2 Physics of optical coherence tomography
14.3 Imaging technique
14.4 Optical coherence tomography image
14.5 Optical coherence tomography versus intravascular ultrasound
14.6 Optical coherence tomography in borderline lesions
14.7 Conclusion
References
Index

Ayman S. El-Baz

Dr. El-Baz is a Professor, University Scholar, and Chair of the Bioengineering Department at the University of Louisville, KY. Dr. El-Baz earned his bachelor's and master’s degrees in Electrical Engineering in 1997 and 2001, respectively. He earned his doctoral degree in electrical engineering from the University of Louisville in 2006. In 2009, Dr. El-Baz was named a Coulter Fellow for his contributions to the field of biomedical translational research. Dr. El-Baz has 15 years of hands-on experience in the fields of bio-imaging modeling and non-invasive computer-assisted diagnosis systems. He has authored or coauthored more than 450 technical articles (105 journals, 15 books, 50 book chapters, 175 refereed-conference papers, 100 abstracts, and 15 US patents).

Affiliations and expertise

University of Louisville, USA

Jasjit Suri

Dr. Jasjit Suri, PhD, MBA, is an innovator, visionary, scientist, and internationally known world leader. Dr Suri received the Director General’s Gold medal in 1980 and Fellow of (i) American Institute of Medical and Biological Engineering, awarded by the National Academy of Sciences, Washington DC, (ii) Institute of Electrical and Electronics Engineers, (iii) American Institute of Ultrasound in Medicine, (iv) Society of Vascular Medicine, (v) Asia Pacific Vascular Society, and (vi) Asia Association of Artificial Intelligence. Dr. Suri was honored with life time achievement awards by Marcus, NJ, USA and Graphics Era University, Dehradun, India. He has published nearly 300 peer-reviewed Artificial Intelligence articles, nearly 2000 Google Scholar Publications, 100 books, and 100 innovations/trademarks leading to an H-index of nearly 100 with about 43,000 citations. He has held positions as chairman of AtheroPoint, CA, USA, IEEE Denver section, Colorado, USA, and advisory board member to healthcare industries and several universities in the United States of America and abroad.

Affiliations and expertise

Chairman, AtheroPoint LLC, USA

Life sciences

Physical sciences & Engineering

Social sciences & Humanities

Health