Vascular and intravascular imaging trends, analysis, and challenges. Volume 1, Stent applications /

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.
Stents (Surgery)
Cardiovascular Diseases > diagnostic imaging.
Cardiovascular Diseases.
Computer Simulation.
Stents.
Biomedical engineering.
TECHNOLOGY & ENGINEERING / Biomedical.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • section I. Vascular and intravascular clinical analysis. 1. OCT in the evaluation of late stent pathology : restenosis, neoatherosclerosis and late malapposition
  • 1.1. Stent evolution and late stent pathology
  • 1.2. OCT characterization of late stent pathology
  • 1.3. OCT evaluation of bioresorbable vascular scaffolds
  • 1.4. Future perspectives
  • 2. Bioresorbable eluting scaffolds in the era of optical coherence tomography : real-world clinical practice
  • 2.1. Introduction
  • 2.2. Historical background and the search for the ideal bioresorbable scaffold
  • 2.3. Bioresorbable scaffolds : current clinical evidence
  • 2.4. The clinical utility of optical coherence tomography in the optimization of bioresorbable scaffolds
  • 2.5. Bioresorbable scaffolds in real-world clinical settings
  • 2.6. Conclusions
  • section II. Computer modeling and computational fluid hemodynamics. 3. Computer modeling of blood flow and plaque progression in the stented coronary artery
  • 3.1. Introduction
  • 3.2. Methods
  • 3.3. Results
  • 3.4. Discussion and conclusions
  • 4. Current status of computational fluid dynamics for modeling of diseased vessels
  • 4.1. Introduction
  • 4.2. Constitutive equation of blood flow in a diseased vessel
  • 4.3. Viscoelastic models of diseased blood
  • 4.4. CFD modeling of blood flow in a diseased vessel
  • 4.5. Evaluation of the shear index on the vascular wall
  • 4.6. Conclusion
  • 5. Fast virtual endovascular stenting : technique, validation and applications in computational haemodynamics
  • 5.1. Motivation
  • 5.2. Virtual stenting
  • 5.3. The fast virtual stenting method
  • 5.4. Validation--how accurate is accurate enough?
  • 5.5. Discussion and future work
  • section III. Vessel and stent segmentation. 6. Graph-based cross-sectional intravascular image segmentation
  • 6.1. Introduction
  • 6.2. Pre-processing
  • 6.3. Feature extraction
  • 6.4. Single- and double-interface segmentation
  • 6.5. Results : IVUS
  • 6.6. Results : OCT
  • 6.7. Conclusion
  • 7. Blind inpainting and outlier detection using logarithmic transformation and total variation
  • 7.1. Introduction
  • 7.2. Blind inpainting
  • 7.3. Experimental results
  • 7.4. Conclusions and future work
  • 8. Differential imaging for the detection of extra-luminal blood perfusion due to the vasa vasorum
  • 8.1. Introduction
  • 8.2. Methods
  • 8.3. Results
  • 8.4. Discussion
  • 8.5. Conclusion
  • 9. Assessment of atherosclerosis in large arteries from PET images
  • 9.1. Introduction
  • 9.2. The formation of atherosclerosis
  • 9.3. Management of atherosclerosis
  • 9.4. Detection of atherosclerosis
  • 9.5. Imaging of atherosclerosis with PET/CT
  • 9.6. Discussion
  • 9.7. Conclusions
  • 10. 3D-2D registration of vascular structures
  • 10.1. Clinical interventions and 3D-2D registration
  • 10.2. Mathematical definition of 3D-2D registration
  • 10.3. Classification of 3D-2D registration
  • 10.4. Review of registration bases
  • 10.5. Review of transformation estimation approaches
  • 10.6. Validation procedures
  • 10.7. Validation of 3D-2D registration on cerebral angiograms
  • 10.8. Challenges in translation to clinical application
  • 11. Endovascular navigation with intravascular imaging
  • 11.1. Introduction
  • 11.2. Existing research into intravascular imaging for navigation
  • 11.3. IVUS for navigation
  • 11.4. The future of intravascular imaging for navigation
  • 11.5. Conclusion
  • section IV. Risk stratification in carotid and coronary artery. 12. A cloud-based smart IMT measurement tool for multi-center clinical trial and stroke risk stratification in carotid ultrasound
  • 12.1. Introduction
  • 12.2. Patient demographics and data acquisition
  • 12.3. Methodology and cloud-based workflow
  • 12.4. Results : measurements and visualization
  • 12.5. Performance evaluation of the AtheroCloud(Tm) system
  • 12.6. Discussion
  • 12.7. Conclusion
  • 13. Stroke risk stratification and its validation using ultrasonic echolucent carotid wall plaque morphology : a machine learning paradigm
  • 13.1. Introduction
  • 13.2. Demographics, data acquisition and data preparation
  • 13.3. Methodology
  • 13.4. Experimental protocol
  • 13.5. Results
  • 13.6. Performance evaluation
  • 13.7. Discussion
  • 13.8. Conclusions
  • 14. An improved framework for IVUS-based coronary artery disease risk stratification by fusing wall-based and texture-based features during a machine learning paradigm
  • 14.1. Introduction
  • 14.2. Patient demographics and data acquisition
  • 14.3. Methodology
  • 14.4. Results
  • 14.5. Performance evaluation
  • 14.6. Discussion
  • 14.7. Conclusion.

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