Publications by authors named "Luuk Van Knippenberg"

Doppler ultrasound is a non-invasive imaging technique that measures blood flow velocity and is commonly used in cardiac evaluation and vascular assessment. Compared to the conventional longitudinal view, cross-sectional Doppler is more robust to motion, making it more suitable for monitoring applications. In this paper, an adaptive framework is presented to automatically monitor flow in the common carotid artery using cross-sectional Doppler.

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. Carotid ultrasound (US) has been studied as a non-invasive alternative for hemodynamic monitoring. A long-axis (LA) view is traditionally employed but is difficult to maintain and operator experience may impact the diameter estimates, making it unsuitable for monitoring.

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Accurate haemodynamic monitoring is the cornerstone in the management of critically ill patients. It guides the optimization of tissue and organ perfusion in order to prevent multiple organ failure. In the past decades, carotid Doppler ultrasound (CDU) has been explored as a non-invasive alternative for long-established invasive haemodynamic monitoring techniques.

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Background And Objectives: Automatic vessel segmentation in ultrasound is challenging due to the quality of the ultrasound images, which is affected by attenuation, high level of speckle noise and acoustic shadowing. Recently, deep convolutional neural networks are increasing in popularity due to their great performance on image segmentation problems, including vessel segmentation. Traditionally, large labeled datasets are required to train a network that achieves high performance, and is able to generalize well to different orientations, transducers and ultrasound scanners.

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The Doppler ultrasound is the most common technique for noninvasive quantification of blood flow, which, in turn, is of major clinical importance for the assessment of the cardiovascular condition. In this article, a method is proposed in which the vessel is imaged in the short axis, which has the advantage of capturing the whole flow profile while measuring the vessel area simultaneously. This view is easier to obtain than the longitudinal image that is currently used in flow velocity estimation, reducing operator dependence.

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