Vascular and intravascular imaging trends, analysis, and challenges. Volume 1, Stent applications / Petia Radeva and Jasjit S. Suri.

By: Radeva, Petia [author.]Contributor(s): Suri, Jasjit S [author.]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: 9780750319973 ebookOther title: Stent applicationsSubject(s): Cardiovascular system -- Diseases -- Imaging | Cardiovascular system -- Diseases -- Computer simulation | Stents (Surgery) | Cardiovascular Diseases -- diagnostic imaging | Cardiovascular Diseases | Computer Simulation | Stents | Biomedical engineering | TECHNOLOGY & ENGINEERING / BiomedicalAdditional physical formats: Print version:: No titleDDC classification: 616.1/075 LOC classification: RC670 .R348 2019eb vol. 1NLM classification: WG 141Online resources: e-book Full-text access Also available in print.
Contents:
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 la
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 :
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
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 enoug
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
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 P
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
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. C
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
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 --
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 -- 1
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. 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 la

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 :

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

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 enoug

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

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 P

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

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. C

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

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 --

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 -- 1

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).