| 청구기호 |
RC670 .R348 2019eb vol. 2 |
| 다른형태 서명 |
Plaque characterization.
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| 형태사항 |
1 online resource (various pagings) : illustrations (some color).
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| 총서명 |
[IOP release 6]
IOP expanding physics, 2053-2563
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| 언어 |
English |
| 일반주기 |
"Version: 20190801"--Title page verso.
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| 서지주기 |
Includes bibliographical references.
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| 내용 |
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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| 주제 |
Cardiovascular system --Diseases --Imaging.
Cardiovascular system --Diseases --Computer simulation.
Atherosclerotic plaque.
Cardiovascular Diseases --diagnostic imaging.
Cardiovascular Diseases.
Computer Simulation.
Plaque, Atherosclerotic.
Biomedical engineering. --bicssc
TECHNOLOGY & ENGINEERING / Biomedical. --bisacsh
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| 보유판 및 특별호 저록 |
Print version: 9780750319997
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| ISBN |
9780750320023, 9780750320016, 9780750319997 |
| 기타 표준번호 |
10.1088/2053-2563/ab0820 |
| QR CODE |
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