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dc.contributor.advisorBourouiba, Lydia
dc.contributor.advisorHeldt, Thomas
dc.contributor.authorLiu, Sabrina
dc.date.accessioned2022-08-29T16:19:56Z
dc.date.available2022-08-29T16:19:56Z
dc.date.issued2022-05
dc.date.submitted2022-05-27T16:18:38.400Z
dc.identifier.urihttps://hdl.handle.net/1721.1/144904
dc.description.abstractPatients undergoing extracorporeal membrane oxygenation (ECMO) therapy are prone to developing emboli. These unattached masses of solid blood clots and gaseous air bubbles have the potential to occlude blood vessels and lead to complications such as neurological damage. Existing ultrasound methods for detecting and characterizing them are designed and tested on data sets that often are small, are not representative of clinical conditions, or lack a ground truth to compare the results to. We aim to construct a flow phantom that fills these gaps. We build upon prior work on this project by mixing a translucent fluid that mimics the acoustic and rheological properties of blood. We explore various bubble generation designs and produce gaseous emboli mimics with diameters as small as 250 µm. In addition, we experimentally confirm a monotonic dependence between bubble diameter and peak backscattered power under no flow conditions, which can help with sizing emboli. Finally, we investigate interactions between ultrasonic acoustic waves and emboli through simulations in k-Wave. This work makes progress towards ultimately developing a well-tested Doppler ultrasound system that can detect and characterize emboli in ECMO.
dc.publisherMassachusetts Institute of Technology
dc.rightsIn Copyright - Educational Use Permitted
dc.rightsCopyright MIT
dc.rights.urihttp://rightsstatements.org/page/InC-EDU/1.0/
dc.titleGenerating Gaseous Emboli Mimics in an ECMO Flow Phantom
dc.typeThesis
dc.description.degreeM.Eng.
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
mit.thesis.degreeMaster
thesis.degree.nameMaster of Engineering in Electrical Engineering and Computer Science


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