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

A clinical method for mapping and quantifying blood stasis in the left ventricle

In patients at risk of intraventrcular thrombosis, the benefits of chronic anticoagulation therapy need to be balanced with the pro-hemorrhagic effects of therapy. Blood stasis in the cardiac chambers is a recognized risk factor for intracardiac thrombosis and potential cardiogenic embolic events. In this work, we present a novel flow image-based method to assess the location and extent of intraventricular stasis regions inside the left ventricle (LV) by digital processing flow-velocity images obtained either by phase-contrast magnetic resonance (PCMR) or 2D color-Doppler velocimetry (echo-CDV). This approach is based on quantifying the distribution of the blood Residence Time (TR) from time-resolved blood velocity fields in the LV. We tested the new method in illustrative examples of normal hearts, patients with dilated cardiomyopathy and one patient before and after the implantation of a left ventricular assist device (LVAD). The method allowed us to assess in-vivo the location and extent of the stasis regions in the LV. Original metrics were developed to integrate flow properties into simple scalars suitable for a robust and personalized assessment of the risk of thrombosis. From a clinical perspective, this work introduces the new paradigm that quantitative flow dynamics can provide the basis to obtain subclinical markers of intraventricular thrombosis risk. The early prediction of LV blood stasis may result in decrease strokes by appropriate use of anticoagulant therapy for the purpose of primary and secondary prevention. It may also have a significant impact on LVAD device design and operation set-up

Automated Echocardiographic Image Interpretation Using Artificial Intelligence

In addition to remaining as one of the leading causes of global mortality, cardio vascular disease has a significant impact on overall health, well-being, and life expectancy. Therefore, early detection of anomalies in cardiac function has become essential for early treatment, and therefore reduction in mortalities. Echocardiography is the most commonly used modality for evaluating the structure and function of the heart. Analysis of echocardiographic images has an important role in the clinical practice in assessing the cardiac morphology and function and thereby reaching a diagnosis. The process of interpretation of echocardiographic images is considered challenging for several reasons. The manual annotation is still a daily work in the clinical routine due to the lack of reliable automatic interpretation methods. This can lead to time-consuming tasks that are prone to intra- and inter-observer variability. Echocardiographic images inherently suffer from a high level of noise and poor qualities. Therefore, although several studies have attempted automating the process, this re-mains a challenging task, and improving the accuracy of automatic echocardiography interpretation is an ongoing field. Advances in Artificial Intelligence and Deep Learning can help to construct an auto-mated, scalable pipeline for echocardiographic image interpretation steps, includingview classification, phase-detection, image segmentation with a focus on border detection, quantification of structure, and measurement of the clinical markers. This thesis aims to develop optimised automated methods for the three individual steps forming part of an echocardiographic exam, namely view classification, left ventricle segmentation, quantification, and measurement of left ventricle structure. Various Neural Architecture Search methods were employed to design efficient neural network architectures for the above tasks. Finally, an optimisation-based speckle tracking echocardiography algorithm was proposed to estimate the myocardial tissue velocities and cardiac deformation. The algorithm was adopted to measure cardiac strain which is used for detecting myocardial ischaemia. All proposed techniques were compared with the existing state-of-the-art methods. To this end, publicly available patients datasets, as well as two private datasets provided by the clinical partners to this project, were used for developments and comprehensive performance evaluations of the proposed techniques. Results demonstrated the feasibility of using automated tools for reliable echocardiographic image interpretations, which can be used as assistive tools to clinicians in obtaining clinical measurements

Doctor of Philosophy

dissertationCongenital heart defects are classes of birth defects that affect the structure and function of the heart. These defects are attributed to the abnormal or incomplete development of a fetal heart during the first few weeks following conception. The overall detection rate of congenital heart defects during routine prenatal examination is low. This is attributed to the insufficient number of trained personnel in many local health centers where many cases of congenital heart defects go undetected. This dissertation presents a system to identify congenital heart defects to improve pregnancy outcomes and increase their detection rates. The system was developed and its performance assessed in identifying the presence of ventricular defects (congenital heart defects that affect the size of the ventricles) using four-dimensional fetal chocardiographic images. The designed system consists of three components: 1) a fetal heart location estimation component, 2) a fetal heart chamber segmentation component, and 3) a detection component that detects congenital heart defects from the segmented chambers. The location estimation component is used to isolate a fetal heart in any four-dimensional fetal echocardiographic image. It uses a hybrid region of interest extraction method that is robust to speckle noise degradation inherent in all ultrasound images. The location estimation method's performance was analyzed on 130 four-dimensional fetal echocardiographic images by comparison with manually identified fetal heart region of interest. The location estimation method showed good agreement with the manually identified standard using four quantitative indexes: Jaccard index, Sørenson-Dice index, Sensitivity index and Specificity index. The average values of these indexes were measured at 80.70%, 89.19%, 91.04%, and 99.17%, respectively. The fetal heart chamber segmentation component uses velocity vector field estimates computed on frames contained in a four-dimensional image to identify the fetal heart chambers. The velocity vector fields are computed using a histogram-based optical flow technique which is formulated on local image characteristics to reduces the effect of speckle noise and nonuniform echogenicity on the velocity vector field estimates. Features based on the velocity vector field estimates, voxel brightness/intensity values, and voxel Cartesian coordinate positions were extracted and used with kernel k-means algorithm to identify the individual chambers. The segmentation method's performance was evaluated on 130 images from 31 patients by comparing the segmentation results with manually identified fetal heart chambers. Evaluation was based on the Sørenson-Dice index, the absolute volume difference and the Hausdorff distance, with each resulting in per patient average values of 69.92%, 22.08%, and 2.82 mm, respectively. The detection component uses the volumes of the identified fetal heart chambers to flag the possible occurrence of hypoplastic left heart syndrome, a type of congenital heart defect. An empirical volume threshold defined on the relative ratio of adjacent fetal heart chamber volumes obtained manually is used in the detection process. The performance of the detection procedure was assessed by comparison with a set of images with confirmed diagnosis of hypoplastic left heart syndrome and a control group of normal fetal hearts. Of the 130 images considered 18 of 20 (90%) fetal hearts were correctly detected as having hypoplastic left heart syndrome and 84 of 110 (76.36%) fetal hearts were correctly detected as normal in the control group. The results show that the detection system performs better than the overall detection rate for congenital heart defect which is reported to be between 30% and 60%

Aortic valve disease : novel imaging insights from diagnosis to therapy

The general introduction of this thesis outlines the epidemiology and the impact of aortic valve disease in the western world. The thesis further discusses the current and future role of advanced cardiac imaging modalities, using 3D echocardiography and speckle tracking echocardiography strain imaging in the diagnostic and clinical management of patients with aortic regurgitation. In addition, the clinical applications of multimodality cardiac imaging in (transcatheter aortic valve implantation (TAVI) for the treatment of severe aortic stenosis will be discussed: from pre-procedural patient evaluation, to the understanding of complications post-TAVI such as paravalvular regurgitation, and the assessment and monitoring of patients after TAVI.Ministry of Health Training Scholarship, SingaporeUBL - phd migration 201

Contrast echocardiography for cardiac quantifications

The indicator-dilution-theory for cardiac quantifications has always been limited in practice by the invasiveness of the available techniques. However, the recent introduction of stable ultrasound contrast agents opens new possibilities for indicator dilution measurements. This study describes a new and successful approach to overcome this invasiveness issue. We show a novel approach for minimally invasive quantification of several cardiac parameters based on the dilution of ultrasound contrast agents. A single peripheral injection of an ultrasound contrast agent bolus can result in the simultaneous assessment of cardiac output, pulmonary blood volume, and left and right ventricular ejection fraction. The bolus passage in different sites of the central circulation is detected by an ultrasound transducer. The detected acoustic (or video) intensities are processed and several indicator dilution curves are measured simultaneously. To this end, we exploit that for low concentrations the relation between contrast concentration and acoustic backscatter is approximately linear. The Local Density Random Walk Model is used to fit and interpret the indicator dilution curves for cardiac output, pulmonary blood volume, and ejection fraction measurements. Two fitting algorithms based either on a multiple linear regression in the logarithmic domain or on the solution of the moment equations are developed. The indicator dilution system can be also interpreted as a linear system and, therefore, characterized by an impulse response function. An adaptive Wiener deconvolution filter is implemented for robust dilution system identification. For ejection fraction measurements, the atrial and ventricular indicator dilution curves are measured and processed by the deconvolution filter, resulting in the estimate of the left ventricle dilution-system impulse response. This curve can be fitted and interpreted by a mono-compartment exponential model for the ejection fraction assessment. The proposed deconvolution filter is also used for the identification of the dilution system between right ventricle and left atrium. The Local Density Random Walk Model fit of the estimated impulse response allows the pulmonary blood volume assessment. Both cardiac output and pulmonary blood volume measurements are validated in vitro with accurate results (correlation coefficients larger than 0.99). The Pulmonary blood volume measurement feasibility is also tested in humans with promising results. The ejection fraction measurement is validated in-vivo. The impulse response approach allows accurate left ventricle ejection fraction estimates. Comparison with echocardiographic bi-plane measurements shows a correlation coefficient equal to 0.93. A dedicated image segmentation algorithm for videodensitometry has also been developed for automating the determination of regions of interest. The resulting algorithm has been integrated with the indicator dilution analysis system. The automatic determination of the measurement region results in improved dilution-curve signal-to-noise ratios. In conclusion, this study proves that quantification of cardiac output, pulmonary blood volume, and left and right ventricular ejection fraction by dilution of ultrasound contrast agents is feasible and accurate. Moreover, the proposed methods are applicable in different contexts (e.g., magnetic resonance imaging) and for different types of measurements, leading to a broad range of applications

Computed tomography and other imaging modalities in pediatric congenital heart disease

Congenital heart defects (CHD) are the most common congenital disabilities. Early and accurate diagnosis of coronary heart disease is very important for patients to get timely and effective treatment. In recent years, the accuracy of coronary heart disease diagnosis has been greatly improved with the development of medical imaging equipment and technology. At present, the clinical application of echocardiogram (echo), cardiovascular magnetic resonance (CMR) and computed tomography angiography (CTA) in coronary heart disease anatomy and function has increased significantly, which plays an important role in preoperative diagnosis, intraoperative monitoring, and postoperative recovery evaluation. However, each imaging technique has its indications. Providing the best examination plan for patients requires clinicians and radiologists’ close cooperation. Therefore, this study reviewed the imaging techniques for diagnosing coronary heart disease