Vascular and intravascular imaging trends, analysis, and challenges. Volume 2, Plaque characterization / Petia Radeva and Jasjit S. Suri.

By: Radeva, Petia [author.]Contributor(s): Suri, Jasjit S [author.] | Institute of Physics (Great Britain) [publisher.]Material type: TextTextSeries: IOP (Series)Release 6 | IOP expanding physicsPublisher: Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) : IOP Publishing, [2019]Description: 1 online resource (various pagings) : illustrations (some color)Content type: text Media type: electronic Carrier type: online resourceISBN: 9780750320023 ebookOther title: Plaque characterizationSubject(s): Cardiovascular system -- Diseases -- Imaging | Cardiovascular system -- Diseases -- Computer simulation | Atherosclerotic plaque | Cardiovascular Diseases -- diagnostic imaging | Cardiovascular Diseases | Computer Simulation | Plaque, Atherosclerotic | Biomedical engineering | TECHNOLOGY & ENGINEERING / BiomedicalAdditional physical formats: Print version:: No titleDDC classification: 616.1/075 LOC classification: RC670 .R348 2019eb vol. 2NLM classification: WG 141Online resources: e-book Full-text access Also available in print.
Contents:
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 mul
2. 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 dise
3. 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 quantific
4. 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 rh
section 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 ris
6. 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 prot
section 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 a
8. 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 regressio
section 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. Introduct
10. 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 eva
11. 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. Conclusion
section 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
Abstract: 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. Com

"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 mul

2. 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 dise

3. 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 quantific

4. 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 rh

section 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 ris

6. 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 prot

section 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 a

8. 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 regressio

section 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. Introduct

10. 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 eva

11. 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. Conclusion

section 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

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. Com

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 Im

Title from PDF title page (viewed on September 5, 2019).