Vascular and intravascular imaging trends, analysis, and challenges.

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Vascular and intravascular imaging trends, analysis, and challenges.

Radeva, Petia, - Personal Name; Institute of Physics (Great Britain), - Personal Name; Suri, Jasjit S., - Personal Name;

"Version: 20190801"--Title page verso.Includes bibliographical references.section I. Review on wall quantification, tissue characterization and coronary and carotid artery risk stratification. 1. Coronary and carotid artery calcium detection, its quantification and grayscale morphology-based risk stratification in multimodality big data : a review -- 1.1. Introduction -- 1.2. Calcium detection in coronary and carotid arteries -- 1.3. Calcium area/volume quantification in coronary and carotid arteries -- 1.4. Metrics for performance evaluation for calcium detection algorithms and its validation -- 1.5. Machine-learning-based risk stratification -- 1.6. Discussion -- 1.7. Conclusions2. Risk of coronary artery disease : genetics and external factors -- 2.1. Introduction -- 2.2. External factors -- 2.3. Genetics of coronary artery disease -- 2.4. Multimodal coronary imaging -- 2.5. Association of CVD with other prevalent diseases -- 2.6. Treatments for cardiovascular disease3. Wall quantification and tissue characterization of the coronary artery -- 3.1. Introduction -- 3.2. Physics of image acquisition -- 3.3. Tissue characterization -- 3.4. A link between carotid and coronary artery disease -- 3.5. Wall quantification -- 3.6. Risk assessment systems -- 3.7. Discussion -- 3.8. Conclusion4. Rheumatoid arthritis : its link to atherosclerosis imaging and cardiovascular risk assessment using machine-learning-based tissue characterization -- 4.1. Introduction -- 4.2. Search strategy -- 4.3. Brief description of the pathogensis of rheumatoid arthritis -- 4.4. Atherosclerosis driven by rheumatoid arthritis -- 4.5. The role of platelets in atherothrombosis in RA -- 4.6. The role of amyloidosis in RA -- 4.7. Traditional CV risk factors in rheumatoid arthritis -- 4.8. RA-specific CV risk factors in rheumatoid arthritis -- 4.9. Conventional CV risk algorithms -- 4.10. Cardiovascular imaging in rheumatoid arthritis -- 4.11. RA-driven atherosclerotic plaque wall tissue characterization : intelligence paradigm -- 4.12. Research agenda -- 4.13. Summary and conclusionsection II. Deep learning strategy for accurate lumen and carotid intima-media thickness measurement. 5. A deep-learning fully convolutional network for lumen characterization in diabetic patients using carotid ultrasound : a tool for stroke risk -- 5.1. Introduction -- 5.2. Data demographics -- 5.3. Methodology -- 5.4. Results -- 5.5. Discussion -- 5.6. Conclusion6. Deep-learning strategy for accurate carotid intima-media thickness measurement : an ultrasound study on a Japanese diabetic cohort -- 6.1. Introduction -- 6.2. Data demographics and US acquisition -- 6.3. Methodology -- 6.4. Experimental protocol and results -- 6.5. Performance of the DL systems and variability analysis -- 6.6. Statistical tests and risk analysis -- 6.7. Discussion -- 6.8. Conclusionsection III. Association of morphological and echolucency-based phenotypes with HbA1c 7 Echolucency-based phenotype in carotid atherosclerosis disease for risk stratification of diabetes patients. 7.1. Introduction -- 7.2. Patient demographics and methodology -- 7.3. Results and statistical analysis -- 7.4. Discussion -- 7.5. Conclusion8. Morphologic TPA (mTPA) and composite risk score for moderate carotid atherosclerotic plaque is strongly associated with HbA1c in a diabetes cohort -- 8.1. Introduction -- 8.2. Materials and methods -- 8.3. Results -- 8.3..4 Logistic regression for the effect of the six phenotypes on HbA1c for the operator of AtheroEdge(Tm) -- 8.4. Inter-operator variability and statistical tests -- 8.5. Discussion -- 8.6. Conclusionssection IV. Deep learning strategy for accurate lumen and carotid intima-media thickness measurement. 9. Plaque tissue morphology-based stroke risk stratification using carotid ultrasound : a polling-based PCA learning paradigm -- 9.1. Introduction -- 9.2. Demographics, data collection and preparation -- 9.3. Risk assessment methodology -- 9.4. Experimental protocol and results -- 9.5. Performance evaluation -- 9.6. Discussion10. Multiresolution-based coronary calcium volume measurement techniques from intravascular ultrasound videos -- 10.1. Introduction -- 10.2. Patient demographics and data acquisition -- 10.3. Methodology -- 10.4. Results -- 10.5. Performance evaluation -- 10.6. Discussion -- 10.7. Conclusion11. A cloud-based smart lumen diameter measurement tool for stroke risk assessment during multicenter clinical trials -- 11.1. Introduction -- 11.2. Materials and methods -- 11.3. Results -- 11.4. Discussion -- 11.5. Conclusionsection V. Micro-electro-mechanical-system (MEMS) 12 A MEMS-based manufacturing technique of vascular bed. 12.1. Introduction -- 12.2. Microstructural anatomy of blood vessels -- 12.3. Modeling of blood vessels as a microsystem -- 12.4. Scaling laws of miniaturized blood vessels -- 12.5. Microfabrication of blood vessels -- 12.6. Microvessel design -- 12.7. Conclusion.Cardiovascular Diseases (CVDs) are responsible for a third of all deaths in women and more than a half in men. Despite continuous improvements in treatment devices and imaging, there is still a rise in the morbidity rate from CVDs each year. Compiled by experts in the field, a thorough investigation is given to current topics and problems relating to CVDs and it will enable the scientific and medical community to search for the most effective strategies for atherosclerotic for dealing with these diseases. As one of the most prominent diseases in our society, CVD requires dedicated analysis and investigation in order to reduce the mortality rate worldwide. Scholars, biomedical engineers and medical practitioners will greatly benefit from the detailed information in this book as it will give a better understanding of the causes, diagnosis and treatment of CVD.Academia and researchers, graduate students in medical imaging.Also available in print.Mode of access: World Wide Web.System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.Professor Petia Radeva is a senior researcher and Full professor at the University of Barcelona. She is the head of Computer Vision and Machine Learning Consolidated Research Group (CVUB) at the University of Barcelona and the head of Medical Imaging Laboratory (MiLab) of Computer Vision Centre, Spain. Her research interests include the development of Deep learning, Computer Vision and Lifelogging, and their applications to healthcare. Petia Radeva is IAPR Fellow and received Icrea Academia and the CIARP 'Aurora Pons Porrata' awards. Dr. Jasjit S Suri is an innovator, scientist, industrialist and an internationally known world leader in Biomedical Engineering, Sciences and its Management. He has numerous publications and is currently the Chairman of AtheroPoint, California, USA, dedicated in Stroke and Cardiovascular Imaging. He is a recipient of Life Time Achievement Award by Marquis (2018) and Fellow of American Institute of Medical and Biological Engineering (2004).Title from PDF title page (viewed on September 5, 2019).

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Series Title: -
Call Number: -
Publisher: : .,
Collation: 1 online resource (various pagings) :illustrations (some color).
Language: English
ISBN/ISSN: 9780750320023
Classification: 616.1/075
Content Type: -
Media Type: -
Carrier Type: -
Edition: -
Subject(s): Cardiovascular system
Computer Simulation.
Biomedical engineering.
TECHNOLOGY & ENGINEERING / Biomedical.
Cardiovascular Diseases
Cardiovascular Diseases.
Plaque, Atherosclerotic.
Atherosclerotic plaque.
Specific Detail Info: -
Statement of Responsibility: Petia Radeva and Jasjit S. Suri.

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