Weakly- and Semi-Supervised Probabilistic Segmentation and Quantification of Ultrasound Needle-Reverberation Artifacts to Allow Better AI Understanding of Tissue Beneath Needles

Ultrasound image quality has continually been improving. However, when needles or other metallic objects are operating inside the tissue, the resulting reverberation artifacts can severely corrupt the surrounding image quality. Such effects are challenging for existing computer vision algorithms for medical image analysis. Needle reverberation artifacts can be hard to identify at times and affect various pixel values to different degrees. The boundaries of such artifacts are ambiguous, leading to disagreement among human experts labeling the artifacts. We propose a weakly- and semi-supervised, probabilistic needle-and-reverberation-artifact segmentation algorithm to separate the desired tissue-based pixel values from the superimposed artifacts. Our method models the intensity decay of artifact intensities and is designed to minimize the human labeling error. We demonstrate the applicability of the approach and compare it against other segmentation algorithms. Our method is capable of differentiating between the reverberations from artifact-free patches as well as of modeling the intensity fall-off in the artifacts. Our method matches state-of-the-art artifact segmentation performance and sets a new standard in estimating the per-pixel contributions of artifact vs underlying anatomy, especially in the immediately adjacent regions between reverberation lines. Our algorithm is also able to improve the performance downstream image analysis algorithms

Frequency smoothed robust Capon beamformer applied to medical ultrasound imaging

Recently, adaptive array beamforming has been applied to medical ultrasound imaging and achieved promising performance improvement. However, the current robust Capon beamformer with spatial smoothing (RCB-SS) is implemented in the time domain, which does not fully utilise the large bandwidth of ultrasound signals and spatial smoothing reduces the effective aperture. In this dissertation, we propose a robust Capon beamformer with frequency smoothing (RCB-FS) and compare its performance with RCB-SS. To further reduce the speckle noise and utilise the large bandwidth of the signal, we combine RCB-FS and frequency com- pounding (FC) and propose a robust Capon beamformer with frequency smoothing combined with frequency compounding (RCB-FS-FC). The proposed RCB-FS method shows a narrower mainlobe width, lower sidelobes, better reconstruction at higher depths and less speckle than RCB-SS. FC is an e ective method to improve the contrast resolution and suppress speckle noise by combining sub-band images, at the expense of resolution. Compared to standard FC, the proposed RCB-FS-FC method has a better contrast resolution and speckle reduction and a significant improvement in resolution. RCB-FS offers a promising approach to find the optimal weights for the transducers in forming the sub-band images needed for frequency compounding

Automated Analysis of 3D Stress Echocardiography

__Abstract__ The human circulatory system consists of the heart, blood, arteries, veins and capillaries. The heart is the muscular organ which pumps the blood through the human body (Fig. 1.1,1.2). Deoxygenated blood flows through the right atrium into the right ventricle, which pumps the blood into the pulmonary arteries. The blood is carried to the lungs, where it passes through a capillary network that enables the release of carbon dioxide and the uptake of oxygen. Oxygenated blood then returns to the heart via the pulmonary veins and flows from the left atrium into the left ventricle. The left ventricle then pumps the blood through the aorta, the major artery which supplies blood to the rest of the body [Drake et a!., 2005; Guyton and Halt 1996]. Therefore, it is vital that the cardiovascular system remains healthy. Disease of the cardiovascular system, if untreated, ultimately leads to the failure of other organs and death