Cardiovascular and Coronary Artery Imaging: Volume 2

Description

Cardiovascular and Coronary Artery Imaging, Volume Two presents the basics of echocardiography, nuclear imaging and magnetic resonance imaging (MRI) and provides insights into their appropriate use. The book covers state-of-the-art approaches for automated non-invasive systems for early cardiovascular and coronary artery disease diagnosis. It includes several prominent imaging modalities such as MRI, CT and PET technologies. Other sections focus on major trends and challenges in this area and present the latest techniques for cardiovascular and coronary image analysis. 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 Back Cover