Vascular and intravascular imaging trends, analysis, and challenges. Volume 2, Plaque characterization /

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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Radeva, Petia (Autor), Suri, Jasjit S. (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) : IOP Publishing, [2019]
Colección:IOP (Series). Release 6.
IOP expanding physics.
Temas:
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 / Biomedical.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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. Conclusions
  • 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 diseases
  • 2.6. Treatments for cardiovascular disease
  • 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 quantification
  • 3.6. Risk assessment systems
  • 3.7. Discussion
  • 3.8. Conclusion
  • 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 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 conclusion
  • 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 risk
  • 5.1. Introduction
  • 5.2. Data demographics
  • 5.3. Methodology
  • 5.4. Results
  • 5.5. Discussion
  • 5.6. Conclusion
  • 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 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. Conclusion
  • 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 and methodology
  • 7.3. Results and statistical analysis
  • 7.4. Discussion
  • 7.5. Conclusion
  • 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 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. Conclusions
  • 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. 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. Discussion
  • 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 evaluation
  • 10.6. Discussion
  • 10.7. Conclusion
  • 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 laws of miniaturized blood vessels
  • 12.5. Microfabrication of blood vessels
  • 12.6. Microvessel design
  • 12.7. Conclusion.

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