Cardiovascular and Coronary Artery Imaging: Volume 2

Front Cover
Cardiovascular and Coronary Artery Imaging
Copyright Page
Dedication
Contents
List of Contributors
About the editors
Acknowledgments
1 Predictors of outcome in ST-segment elevation myocardial infarction
	1.1 Clinical predictors
		1.1.1 Heart failure
		1.1.2 Tachycardia
			1.1.2.1 Electrocardiogram
				1.1.2.1.1 Ventricular arrhythmias
			1.1.2.2 Atrial fibrillation
			1.1.2.3 Chronic kidney disease
			1.1.2.4 Peripheral artery disease
		1.1.3 Biomarkers
		1.1.4 CK-MB
		1.1.5 Troponin
		1.1.6 High-sensitivity troponin assays
		1.1.7 Myoglobin
	1.2 Brain natriuretic peptide
		1.2.1 Ischemia-modified albumin
		1.2.2 Unbound free fatty acids
		1.2.3 Circulating microRNAs are new and sensitive biomarkers of myocardial infarction
		1.2.4 Lipoprotein-associated phospholipase A2
		1.2.5 Plasma fibrinogen level
		1.2.6 Interleukin-6+, interleukin-10+, and interleukin-6-interleukin-10+ cytokine
		1.2.7 Routinely feasible multiple biomarker score to predict prognosis after revascularized ST elevation myocardial infarction
		1.2.8 Serum potassium
		1.2.9 Glycemic control
		1.2.10 White blood cell count
	1.3 Differential white blood cell count
		1.3.1 Anemia
		1.3.2 Findings at the time of angiography and percutaneous coronary intervention
		1.3.3 Thrombolysis in myocardial infarction frame count
		1.3.4 Left ventricular ejection fraction
	References
2 ST-segment elevation myocardial infarction
	2.1 Definition
	2.2 Epidemiology of ST elevation myocardial infarction
	2.3 Etiology
	2.4 Pathophysiology
	2.5 Management
		2.5.1 Diagnosis
		2.5.2 Differential diagnosis
		2.5.3 Logistics of management
		2.5.4 Prehospital management
		2.5.5 Hospital management
			2.5.5.1 Medical management
			2.5.5.2 Fibrinolysis in a hospital without percutaneuos coronary intervention capability
			2.5.5.3 Primary percutaneuos coronary intervention
	2.6 Prevention
	2.7 Complications
	2.8 Prognosis
	2.9 Conclusion
	References
3 The effect of patient-centered education in adherence to the treatment regimen in patients with coronary artery disease
	3.1 Introduction
	3.2 Methods
		3.2.1 Type of research
		3.2.2 Research environment
		3.2.3 Research community
		3.2.4 Sample
		3.2.5 Sample size
		3.2.6 Sampling method
		3.2.7 Inclusion criteria
		3.2.8 Exclusion criteria
		3.2.9 Data collection tool
		3.2.10 Procedure
		3.2.11 Session 1: interview
		3.2.12 Session 2: patients’ participation in the design and implementation of educational goals
		3.2.13 How to analyze data
	3.3 Findings
		3.3.1 Comparison of demographic and contextual variables in two groups of intervention and control
		3.3.2 Comparison of treatment adherence and its dimensions in two groups of intervention and control
	3.4 Discussion
	3.5 Limitations
	3.6 Conclusion
	References
4 Artificial intelligence in cardiovascular imaging
	4.1 Introduction
	4.2 Artificial intelligence
		4.2.1 The concept of artificial intelligence
		4.2.2 The history of artificial intelligence
		4.2.3 Briefly division of artificial intelligence
	4.3 Cardiovascular imaging with machine learning
		4.3.1 The diagnosis based on coronary artery computed tomography
		4.3.2 The diagnosis based on ultrasonic cardiogram
		4.3.3 The diagnosis based on electrocardiogram
		4.3.4 The diagnosis based on nuclear medicine technology
		4.3.5 Other diagnostic methods
	4.4 Cardiovascular imaging with deep learning
		4.4.1 The diagnosis based on coronary artery computed tomography
		4.4.2 The diagnosis based on electrocardiogram
			4.4.2.1 The arrhythmia diagnosis
			4.4.2.2 The myocardial infarction diagnosis
			4.4.2.3 Other diagnostic methods based on electrocardiogram
		4.4.3 Cardiovascular magnetic resonance imaging
		4.4.4 Other diagnostic methods
	4.5 Discussion
		4.5.1 Open challenges
		4.5.2 Recommendations
	4.6 Summary
	References
5 Valvular assessment and flow quantification
	5.1 Introduction
	5.2 Techniques
		5.2.1 Assessment of valve structure
		5.2.2 Evaluation of ventricular volume and function
		5.2.3 Flow visualization
			5.2.3.1 Flow quantification
	5.3 Individual valvular assessment
		5.3.1 Aortic valve
			5.3.1.1 Aortic regurgitation
			5.3.1.2 Cine imaging for valve morphology and left ventricle volumes
			5.3.1.3 Cardiovascular magnetic resonance quantification of aortic regurgitation severity
		5.3.2 Mitral valve
			5.3.2.1 Mitral regurgitation
			5.3.2.2 Cardiovascular magnetic resonance quantification of mitral regurgitation severity
		5.3.3 Right-sided valve assessment
			5.3.3.1 Pulmonary valve
			5.3.3.2 Tricuspid valve
	5.4 Recent techniques
		5.4.1 Four-dimensional flow MRI
		5.4.2 Wall shear stress
	References
6 Software-based analysis for computed tomography coronary angiography: current status and future aspects
	6.1 Introduction
	6.2 Coronary artery calcification measurement
	6.3 Software-based plaque analysis
	6.4 Quantitative analysis for obstructive coronary artery
	6.5 Computational fluid dynamics
	6.6 Anatomical 2D bull’s eye display
	6.7 Territorial analysis with Voronoi diagram
	6.8 Nobel analysis for computed tomography angiography
		6.8.1 Pericardial and pericoronary fat measurement
	6.9 Computed tomography myocardial perfusion imaging
	6.10 The analysis of dynamic images by motion coherence technique
	6.11 Closing remarks
	References
	Further reading
7 Medical image analysis for the early prediction of hypertension
	7.1 Introduction
	7.2 Methodology
		7.2.1 Cerebrovascular segmentation
		7.2.2 Extraction of cerebrovascular descriptive features
		7.2.3 Classification
	7.3 Experimental results
		7.3.1 Dataset description
		7.3.2 Classification results
	7.4 Discussion
	7.5 Conclusion and future work
	References
8 Left ventricle segmentation and quantification using deep learning
	8.1 Heart: anatomy, function, and diseases
		8.1.1 Location, size, and shape of the heart
		8.1.2 Anatomy of the heart and circulation system
		8.1.3 Cardiac cycle
		8.1.4 Cardiovascular diseases
	8.2 Left ventricle segmentation and quantification
	8.3 Related work on left ventricle segmentation and quantification
	8.4 Methods
		8.4.1 Region-of-interest extraction
	8.5 Cardiac segmentation
		8.5.1 Loss function
		8.5.2 Network training settings
	8.6 Experimental results
		8.6.1 Cardiac datasets
		8.6.2 Framework training and validation
		8.6.3 Evaluation of LV-ROI extraction
		8.6.4 Evaluation of the proposed loss function
		8.6.5 Evaluation of the proposed network model FCN2
		8.6.6 Generalization evaluation
		8.6.7 Physiological parameters estimation
	8.7 Discussion
	References
9 Cardiac magnetic resonance imaging of cardiomyopathy
	9.1 Introduction
	9.2 Iron overload cardiomyopathy
	9.3 Idiopathic dilated cardiomyopathy
	9.4 Hypertrophic cardiomyopathy
	9.5 Sarcoidosis
	9.6 Myocarditis
	9.7 Amyloidosis
	9.8 Left ventricle noncompaction
	9.9 Arrhythmogenic right ventricular dysplasia/cardiomyopathy
	9.10 Stress-induced (Takotsubo) cardiomyopathy
	9.11 Fabry disease
	9.12 Muscular dystrophy
	References
10 Magnetic resonance imaging of pericardial diseases
	10.1 Introduction
	10.2 Normal pericardium
	10.3 Pericarditis
		10.3.1 Chronic inflammatory pericarditis
		10.3.2 Chronic fibrosing pericarditis
	10.4 Pericardial effusion
	10.5 Pericardial hematoma
	10.6 Cardiac tamponade
	10.7 Pericardial constriction
	10.8 Pericardial neoplasms
		10.8.1 Pericardial metastasis
		10.8.2 Primary benign pericardial neoplasm
		10.8.3 Primary pericardial malignant neoplasms
	10.9 Pericardial cyst and diverticulum
	10.10 Congenital absence of pericardium
	10.11 Pericardial diaphragmatic hernia
	10.12 Extracardiac lesions
	References
11 Imaging modalities for congenital heart disease and genetic polymorphism associated with coronary artery and cardiovascu...
	11.1 Introduction
	11.2 Sources of information and search
	11.3 Study selection
	11.4 Diet and cardiovascular disease risk
	11.5 High-density lipoprotein cholesterol
	11.6 Low-density lipoprotein cholesterol
	11.7 Triglycerides
	11.8 Inherited genetic susceptibility
		11.8.1 Coronary artery disease
		11.8.2 Hypertension
		11.8.3 Myocardial infarction
	11.9 Imaging strategy and techniques
	11.10 Plain radiography
	11.11 Echocardiography
	11.12 Computed tomography
		11.12.1 Magnetic resonance imaging
	11.13 Methodology
		11.13.1 Literature search
		11.13.2 Selection criteria
		11.13.3 Extracted information
		11.13.4 Hardy-Weinberg equilibrium testing
		11.13.5 Statistical analysis
	11.14 Results and discussion
	11.15 Results and discussion of SMARCA4 gene polymorphism
	11.16 Conclusion
	Author contributions
	References
Index
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