Doppler ultrasound is an important noninvasive diagnostic tool for cardiovascular diseases. Modern ultrasound imaging systems utilize spectral Doppler techniques for quantitative evaluation of blood flow velocities, and these measurements play a crucial rule in the diagnosis and grading of arterial stenosis. One drawback of Doppler-based blood flow quantification is that the operator has to manually specify the angle between the Doppler ultrasound beam and the vessel orientation, which is called the Doppler angle, in order to calculate flow velocities. In this paper, we will describe a computer vision approach to automate the Doppler angle estimation. Our approach starts with the segmentation of blood vessels in ultrasound color Doppler images. The segmentation step is followed by an estimation technique for the Doppler angle based on a skeleton representation of the segmented vessel. We conducted preliminary clinical experiments to evaluate the agreement between the expert operator's angle specification and the new automated method. Statistical regression analysis showed strong agreement between the manual and automated methods. We hypothesize that the automation of the Doppler angle will enhance the workflow of the ultrasound Doppler exam and achieve more standardized clinical outcome.
Abstract. 3D-ultrasound can become a new, fast, non-radiative, noninvasive, and inexpensive tomographic medical imaging technique with unique advantages for the localization of vessels and tumors in soft tissue (spleen, kidneys, liver, breast etc.). In general, unlike the usual 2D-ultrasound, in the 3D-case a complete volume is covered with a whole series of cuts, which would enable a 3D reconstruction and visualization.In the last two decades, many researchers have attempted to produce systems that would allow the construction and visualization of threedimensional (3-D) images from ultrasound data. There is a general agreement that this development represents a positive step forward in medical imaging, and clinical applications have been suggested in many different areas. However, it is clear that 3-D ultrasound has not yet gained widespread clinical acceptance, and that there are still important problems to solve before it becomes a common tool.
Color Doppler ultrasound imaging is a powerful non-invasive diagnostic tool for many clinical applications that involve examining the anatomy and hemodynamics of human blood vessels. These clinical applications include cardio-vascular diseases, obstetrics, and abdominal diseases. Since its commercial introduction in the early eighties, color Doppler ultrasound imaging has been used mainly as a qualitative tool with very little attempts to quantify its images. Many imaging artifacts hinder the quantification of the color Doppler images, the most important of which is the aliasing artifact that distorts the blood flow velocities measured by the color Doppler technique. In this work we will address the color Doppler aliasing problem and present a recovery methodology for the true flow velocities from the aliased ones. The problem is formulated as a 2D phase-unwrapping problem, which is a well-defined problem with solid theoretical foundations for other imaging domains, including synthetic aperture radar and magnetic resonance imaging. This paper documents the need for a phase unwrapping algorithm for use in color Doppler ultrasound image analysis. It describes a new phase-unwrapping algorithm that relies on the recently developed cutline detection approaches. The algorithm is novel in its use of heuristic information provided by the ultrasound imaging modality to guide the phase unwrapping process. Experiments have been performed on both in-vitro flow-phantom data and in-vivo human blood flow data. Both data types were acquired under a controlled acquisition protocol developed to minimize the distortion of the color Doppler data and hence to simplify the phase-unwrapping task. In addition to the qualitative assessment of the results, a quantitative assessment approach was developed to measure the success of the results. The results of our new algorithm have been compared on ultrasound data to those from other well-known algorithms, and it outperforms all of them.
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