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

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Sensors for Vital Signs Monitoring

Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data

Robust Eigen-Filter Design for Ultrasound Flow Imaging Using a Multivariate Clustering

Blood flow visualization is a challenging task in the presence of tissue motion. Unsuppressed tissue clutter produces flashing artefacts in ultrasound flow imaging which hampers blood flow detection by dominating part of the blood flow signal in certain challenging clinical imaging applications, ranging from cardiac imaging (maximal tissue vibrations) to microvascular flow imaging (very low blood flow speeds). Conventional clutter filtering techniques perform poorly since blood and tissue clutter echoes share similar spectral characteristics. Eigen-based filtering was recently introduced and has shown good clutter rejection performance; however, flow detection performance in eigen filtering suffers if tissue and flow signal subspaces overlap after eigen components are projected to a single signal feature space for clutter rank selection. To address this issue, a novel multivariate clustering based singular value decomposition (SVD) filter design is developed. The proposed multivariate clustering based filter robustly detects and removes non-blood eigen components by leveraging on three key spatiotemporal statistics: singular value magnitude, spatial correlation and the mean Doppler frequency of singular vectors. A better clutter suppression framework is necessary for high-frame-rate (HFR) ultrasound imaging since it is more susceptible to tissue motion due to poorer spatial resolution (tissue clutter bleeds into flow pixels easily). Hence, to test the clutter rejection performance of the proposed filter, HFR plane wave data was acquired from an in vitro flow phantom testbed and in vivo from a subject’s common carotid artery and jugular vein region induced with extrinsic tissue motion (voluntary probe motion). The proposed method was able to adaptively detect and preserve blood eigen components and enabled fully automatic identification of eigen components corresponding to tissue clutter, blood and noise that removes dependency on the operator for optimal rank selection. The flow detection efficacy of the proposed multivariate clustering based SVD filter was statistically evaluated and compared with current clutter rank estimation methods using the receiver operating characteristic (ROC) analysis. Results for both in vitro and in vivo experiments showed that the multivariate clustering based SVD filter yielded the highest area under the ROC curve at both peak systole (0.98 for in vitro; 0.95 for in vivo) and end diastole (0.96 for in vitro; 0.93 for in vivo) in comparison with other clutter rank estimation methods, signifying its improved flow detection capability. The impact of this work is on the automated as well as adaptive (in contrast to a fixed cut-off) selection of eigen components which can potentially allow to overcome the flow detection challenges associated with fast tissue motion in cardiovascular imaging and slow flow in microvascular imaging which is critical for cancer diagnoses

Speckle Noise Reduction via Homomorphic Elliptical Threshold Rotations in the Complex Wavelet Domain

Many clinicians regard speckle noise as an undesirable artifact in ultrasound images masking the underlying pathology within a patient. Speckle noise is a random interference pattern formed by coherent radiation in a medium containing many sub-resolution scatterers. Speckle has a negative impact on ultrasound images as the texture does not reflect the local echogenicity of the underlying scatterers. Studies have shown that the presence of speckle noise can reduce a physician's ability to detect lesions by a factor of eight. Without speckle, small high-contrast targets, low contrast objects, and image texture can be deduced quite readily. Speckle filtering of medical ultrasound images represents a critical pre-processing step, providing clinicians with enhanced diagnostic ability. Efficient speckle noise removal algorithms may also find applications in real time surgical guidance assemblies. However, it is vital that regions of interests are not compromised during speckle removal. This research pertains to the reduction of speckle noise in ultrasound images while attempting to retain clinical regions of interest. Recently, the advance of wavelet theory has lead to many applications in noise reduction and compression. Upon investigation of these two divergent fields, it was found that the speckle noise tends to rotate an image's homomorphic complex-wavelet coefficients. This work proposes a new speckle reduction filter involving a counter-rotation of these complex-wavelet coefficients to mitigate the presence of speckle noise. Simulations suggest the proposed denoising technique offers superior visual quality, though its signal-to-mean-square-error ratio (S/MSE) is numerically comparable to adaptive frost and kuan filtering. This research improves the quality of ultrasound medical images, leading to improved diagnosis for one of the most popular and cost effective imaging modalities used in clinical medicine

Age-related macular degeneration: choroidal ischaemia?

Aim: Our aim is to use ultrasound to non-invasively detect differences in choroidal microarchitecture possibly related to ischaemia among normal eyes and those with wet and dry age-related macular degeneration (AMD). Design Prospective case series of subjects with dry AMD, wet AMD and age-matched controls. Methods: Digitized 20 MHz B-scan radiofrequency ultrasound data of the region of the macula were segmented to extract the signal from the retina and choroid. This signal was processed by a wavelet transform, and statistical modelling was applied to the wavelet coefficients to examine differences among dry, wet and non-AMD eyes. Receiver operating characteristic (ROC) analysis was used to evaluate a multivariate classifier. Results: In the 69 eyes of 52 patients, 18 did not have AMD, 23 had dry AMD and 28 had wet AMD. Multivariate models showed statistically significant differences between groups. Multiclass ROC analysis of the best model showed an excellent volume-under-curve of 0.892±0.17. The classifier is consistent with ischaemia in dry AMD. Conclusions: Wavelet augmented ultrasound is sensitive to the organisational elements of choroidal microarchitecture relating to scatter and fluid tissue boundaries such as seen in ischaemia and inflammation, allowing statistically significant differentiation of dry, wet and non-AMD eyes. This study further supports the association of ischaemia with dry AMD and provides a rationale for treating dry AMD with pharmacological agents to increase choroidal perfusion

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%