Echocardiography

The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography

An Autopsy based Examination of heart findings in Electrocution and Sudden Cardiac Death cases

INTRODUCTION: One of the biggest challenges faced by forensic pathologists is death due to electrocution and sudden death below the age of 40 years without any pre existing illnesses. Ventricular fibrillation is the most common mechanism of death in electrocution. But it is not clear if, the fibrillation is purely due to electrophysiological changes or to identifiable structural abnormalities in the heart. AIM AND OBJECTIVES: 1. Examination of Autopsy findings of heart in Electrocution and Sudden Cardiac Death Cases. 2. Comparison of autopsy findings of heart in Electrocution and Sudden Cardiac Deaths. 3. Assessment of Electrocution and Sudden Cardiac Deaths related to age and gender. MATERIALS AND METHOD: This prospective study was undertaken in the department of Forensic Medicine, Coimbatore medical college, Coimbatore. Autopsied heart specimens from 52 electrocution deaths and 52 sudden cardiac deaths were preserved, stained with eosin and haemotoxylin and examined under microscope. RESULTS: There was higher incidence of electrocution deaths among as compared to females. The common age group involved is 31-40 years, mean age is 34.27. There was higher incidence of sudden cardiac deaths among males as compared to females under 40 years. The common age group involved is 21-30 years, mean age is 26.88. Out of 52 heart specimens from electrocution 51 showed evidence of myofibre breakup at histopathology. Out of 52 heart specimens from sudden cardiac deaths, there were no remarkable findings in 26 cases at autopsy and 39 cases during histopathology. No correlation was seen with respect to age, gender, coronary atherosclerosis and myofibre breakup. CONCLUSION: Based on our study we recommend histopathological examination of heart as an ancillary investigation for electrocution deaths and newer researches on physiological dysfunction of heart in sudden cardiac deaths. We also recommend further systematic studies for the betterment

A Deep Learning Approach to Evaluating Disease Risk in Coronary Bifurcations

Cardiovascular disease represents a large burden on modern healthcare systems, requiring significant resources for patient monitoring and clinical interventions. It has been shown that the blood flow through coronary arteries, shaped by the artery geometry unique to each patient, plays a critical role in the development and progression of heart disease. However, the popular and well tested risk models such as Framingham and QRISK3 current cardiovascular disease risk models are not able to take these differences when predicting disease risk. Over the last decade, medical imaging and image processing have advanced to the point that non-invasive high-resolution 3D imaging is routinely performed for any patient suspected of coronary artery disease. This allows for the construction of virtual 3D models of the coronary anatomy, and in-silico analysis of blood flow within the coronaries. However, several challenges still exist which preclude large scale patient-specific simulations, necessary for incorporating haemodynamic risk metrics as part of disease risk prediction. In particular, despite a large amount of available coronary medical imaging, extraction of the structures of interest from medical images remains a manual and laborious task. There is significant variation in how geometric features of the coronary arteries are measured, which makes comparisons between different studies difficult. Modelling blood flow conditions in the coronary arteries likewise requires manual preparation of the simulations and significant computational cost. This thesis aims to solve these challenges. The "Automated Segmentation of Coronary Arteries (ASOCA)" establishes a benchmark dataset of coronary arteries and their associated 3D reconstructions, which is currently the largest openly available dataset of coronary artery models and offers a wide range of applications such as computational modelling, 3D printed for experiments, developing, and testing medical devices such as stents, and Virtual Reality applications for education and training. An automated computational modelling workflow is developed to set up, run and postprocess simulations on the Left Main Bifurcation and calculate relevant shape metrics. A convolutional neural network model is developed to replace the computational fluid dynamics process, which can predict haemodynamic metrics such as wall shear stress in minutes, compared to several hours using traditional computational modelling reducing the computation and labour cost involved in performing such simulations

Morphologic evaluation of ruptured abdominal aortic aneurysm by 3D modeling

This thesis was created in Word and converted to PDF using Mac OS X 10.7.5 Quartz PDFContext.Abdominal aortic aneurysm (AAA) is defined as a dilatation of the abdominal aorta exceeding the normal diameter by more than 50%. The standard and widely used approach to assess AAA size is by measuring the maximal diameter (Dmax). Currently, the main predictors of rupture risk are the Dmax, sex, and the expansion rate of the aneurysm. Yet, Dmax has some limitations. AAAs of vastly different shapes may have the same maximal diameter. Dmax lacks sensitivity for rupture risk, especially among smaller AAAs. Thus, there is a need to evaluate the susceptibility of a given AAA to rupture on a patient-specific basis. We present the design concept and workflow of the AAA segmentation software developed at our institution. We describe the previous validation steps in which we evaluated the reproducibility of manual Dmax, compared software Dmax against manual Dmax, validated reproducibility of software Dmax and volume in cross-sectional and longitudinal studies for detection of AAA growth, and evaluated the reproducibility of software measurements in unenhanced computed tomographic angiography (CTA) and in the presence of stent-graft. In order to define new geometric features associated with rupture, we performed a case-control study in which we compared 63 cases with ruptured or symptomatic AAA and 94 controls with asymptomatic AAA. Univariate logistic regression analysis revealed 14 geometric indices associated with AAA rupture. In the multivariate logistic regression analysis, adjusting for Dmax and sex, the AAA with a higher bulge location and higher mean averaged surface area were associated with AAA rupture. Our preliminary results suggest that incorporating geometrical indices obtained by segmentation of CT shows a trend toward improvement of the classification accuracy of AAA with high rupture risk at CT over a traditional model based on Dmax and sex alone. Larger longitudinal studies are needed to verify the validity of the proposed model. Addition of flow and biomechanical simulations should be investigated to improve rupture risk prediction based on AAA modeling.Un anévrysme de l'aorte abdominale (AAA) est défini par une dilatation de plus de 50% par rapport au diamètre normal. La méthode standard et largement répandue pour mesurer la dimension d'un AAA consiste à mesurer le diamètre maximal (Dmax). Présentement, les principaux prédicteurs de risque de rupture sont le Dmax, le sexe et le taux d'expansion d'un anévrysme. Toutefois, le Dmax a certaines limitations. Des AAAs de formes très différentes peuvent avoir le même diamètre maximal. Le Dmax manque de sensibilité pour détecter le risque de rupture, en particulier pour les petits anévrysmes. Par conséquent, il y a un besoin d'évaluer de manière spécifique et individuelle la susceptibilité de rupture d'un AAA. Nous présentons le concept et le flux de travail d'un logiciel de segmentation des AAAs développé à notre institution. Nous décrivons les étapes antérieures de validation: évaluation de la reproductibilité du Dmax manuel, comparaison de Dmax par logiciel avec Dmax manuel, validation de la reproductibilité du Dmax et volume par logiciel dans des études transversale et longitudinale pour la détection de croissance et évaluation de la reproductibilité de mesures sur angiographie par tomodensitométrie et en présence d'endoprothèse. En vue d’identifier de nouveaux paramètres géométrique associés avec le risque de rupture, nous avons réalisé une étude cas-témoin comparant 63 cas avec AAA rompu ou symptomatique et 94 contrôles avec AAA asymptomatique. Une analyse de régression logistique univariée a identifié 14 indices géométriques associés avec une rupture de AAA. Dans l'analyse de régression logistique multivariée, en ajustant pour le Dmax et le sexe, les AAA avec un bombement plus haut situé et une surface moyenne plus élevée étaient associés à une rupture. Nos résultats préliminaires suggèrent que l'inclusion d'indices géométriques obtenus par segmentation de tomodensitométrie tend à améliorer la classification de AAA avec un risque de rupture par rapport à un modèle traditionnel seulement basé sur le Dmax et le sexe. De plus larges études longitudinales sont requises pour vérifier la validité du modèle proposé. Des simulations de flux et biomécaniques devraient être envisagées pour améliorer la prédiction du risque de rupture basée sur la modélisation d'anévrysmes

The study of blood flow in human arterial bifurcations by the combination of CFD and MRI

Atherosclero8is represents a major health problem in the western world. The local haemodynamics is believed to be an initiating and localizing factor in this multilactorial disease process. To fully understand this interaction it is important to obtain detailed information about the local haemodynamics in accurate models of the hnrnan vascular system. Because of the complexity of arterial geometry, in mvo velocity measurements are subject to large errors by currently available techniques. It is also difficult to construct the highly irregular arterial bifurcation model for in vitro investigations. By using a combination of two new methodologies,namely magnetic resonance angiography (MRA) and computational fluid dynamics CFD), the precise patterns of flow anticipating the onset of disease at arterial bifurcations can now, in principle, be determined. However, flow simulations based on in vzvo data directly acquired from clinical measurements have rarely been performed, due to difficulties involved in converting medical images into a data set that CFD software packages can accept. ,In this study, a computer modelling technique, which integrates dinically acquired MR angiograms, image processing and CFD, for the reconstruction of 3D blood flow patterns in realistic arterial geometry, was developed. In the procedure, human arteries are scanned non-invasively by MR angiography. With the MR angiograms, image processing and 3D reconstruction are performed and structured numerical grid is generated for the arteries scanned. Together with MR in tnvo measured velocity profiles at the boundary planes of the model, CFD simulations are undertaken. To test the capability and reliability of the whole procedure, two examples are given, of the human abdominal and right carotid bifurcations. The complete haemodynamic patterns obtained allow a full clinical understanding to be gained of individual patient behaviour. Aspects such as wall shear stress variation, secondary flow and flow separations are demonstrated. The problem of quantitative reliability of the predictions is discussed in some depth

Variable Scale Statistics For Cardiac Segmentation and Shape Analysis

A novel framework for medical image analysis, known as Shells and Spheres, has been developed by our research lab. This framework utilizes spherical operators of variable radius, centered at each image pixel and sized to reach, but not cross, the nearest boundary. Statistical population tests are performed on the populations of pixels within adjacent spheres to compare image regions across boundaries, delineating bothindependent image objects and the boundaries between them. This research has focused on developing the Shells and Spheres frameworkand applying it to the problem of segmentation of anatomical objects. Furthermore, we have rigorously studied the framework and its applications to clinical segmentation, validating and improving our n-dimensional segmentation algorithm. To this end, we have enhanced the original Shells and Spheres segmentation algorithm by adding a priori information, developing techniques for optimizing algorithm parameters, implementing a software platform for experimentation, and performing validation experiments using real 3D ovine cardiac MRI data. The system developed provides automated 3D segmentation given a priori information in the form of a trivial 2D manual training procedure, which involves tracing a single 2D contour from which 3D algorithm parameters are then automatically derived. We apply this system tosegmentation of the Right Ventricular Outflow Tract (RVOT) to aid in research toward the creation of a Tissue Engineered Pulmonary Valve(TEPV). Experimental methods are presented for the development and validation of the system, as well as a detailed description of the Shells and Spheres framework, our segmentation algorithm, and the clinical significance of this work