Vos, HJ, Voorneveld, JD, Groot Jebbink, E et al. (6 more authors) (2020) Contrast-Enhanced High-Frame-Rate Ultrasound Imaging of Flow Patterns in Cardiac Chambers and Deep Vessels. Ultrasound in Medicine & Biology, 46 (11). pp. 2875-2890. ISSN 0301-5629
Abstract
Cardiac function and vascular function are closely related to the flow of blood within. The flow velocities in these larger cavities easily reach 1 m/s, and generally complex spatiotemporal flow patterns are involved, especially in a non-physiologic state. Visualization of such flow patterns using ultrasound can be greatly enhanced by administration of contrast agents. Tracking the high-velocity complex flows is challenging with current clinical echographic tools, mostly because of limitations in signal-to-noise ratio; estimation of lateral velocities; and/or frame rate of the contrast-enhanced imaging mode. This review addresses the state of the art in 2-D high-frame-rate contrast-enhanced echography of ventricular and deep-vessel flow, from both technological and clinical perspectives. It concludes that current advanced ultrasound equipment is technologically ready for use in human contrast-enhanced studies, thus potentially leading to identification of the most clinically relevant flow parameters for quantifying cardiac and vascular function.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 The Author(s). Published by Elsevier Inc. on behalf of World Federation for Ultrasound in Medicine & Biology. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
Keywords: | Cardiac function; Echography; Echo particle image velocimetry; High frame rate; Particle image velocimetry; Ultrafast; Ultrasound contrast agent; Vascular function; Vortex |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Aug 2020 11:41 |
Last Modified: | 25 Jun 2023 22:24 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.ultrasmedbio.2020.07.022 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:164814 |