High-Frame-Rate Ultrasound Imaging Innovations for Complex Flow Quantification
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Date
2021-09-07
Authors
Nahas, Hassan
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
Vascular pathology such as atherosclerosis or arterial stiffening involves significant disruption in the
blood flow dynamics found in the vasculature, with complex behavior such as jets and swirling
typically seen in diseased vessels. Detecting and mapping complex blood flow dynamics in the
human vasculature is therefore important for monitoring disease progression and understanding the
pathology. Existing solutions for flow imaging in the clinic cannot reliably capture complex blood
flow dynamics. These solutions also suffer from limited frame rates, high operational costs and
inaccessibility for point-of-care and routine use.
To meet this need, this dissertation has devised a novel ultrasound-based imaging framework suited
for visualizing and quantifying complex blood flow in human vasculature at the bedside. This was
achieved using next-generation ultrasound technology and a novel vector flow imaging framework.
The framework utilized deep learning and parallel computing principles to achieve real-time vector
flow imaging with an expanded range of measurable velocities. The proposed framework was
realized on a programmable and mobile ultrasound scanner to support point-of-care use. Performance
of the proposed framework was validated in phantom and human experiments, including healthy and
diseased arteries. Reliable vector flow imaging was achieved at the bedside in human trials where
complex and high flow speed blood flow was imaged at the bedside.
This dissertation presents important progress towards the visualization of complex blood flow
dynamics in human vasculature at the bedside and the translation of advanced ultrasound imaging
technologies to the clinic. By providing a reliable implementation of vector flow imaging at the
bedside, the presented framework should facilitate more comprehensive and large-scale human
studies into the hemodynamics of healthy and pathological vasculature. This work also showcases the
novel applications of deep learning and parallel computing principles to ultrasound flow imaging,
thereby contributing new strategies to the field.
Description
Keywords
ultrasound, high-frame rate, HiFRUS, Doppler, flow imaging, stenosis, vector flow imaging, GPU, deep learning, bedside