{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/144787"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/144787","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Modeling the Arterial System to Improve Ultrasound Measurements of Hemodynamic Parameters","abstract":"One of the most crucial parameters for monitoring Cardiovascular disease (CVD) risk is one’s Arterial Blood Pressure (ABP). Clinicians use a radial arterial catheter to measure ABP in an intensive care unit (ICU). Although this method is considered the gold standard, its invasive nature makes it undesirable and inaccessible outside an ICU. One solution to this problem is to take advantage of ultrasonic measurements, which are noninvasive and extremely accessible. However, developing an algorithm to convert ultrasound data into a legitimate ABP waveform requires an extensive amount of patient data. The limitation is that this data is difficult to obtain and impossible to fully control. The solution presented here is to use a flow phantom: a physical, hydraulic system that mimics arterial blood flow. The phantom provides pressure waveforms, which come directly from a catheterized tube, and volumetric flow waveforms, from an ultrasonic flow meter, that closely match the morphology of patient data. Developing a physical model of the arterial system allows for control over an expanded range of parameter relationships for experimentation. To help understand the behavior and results of the flow phantom, a hydraulic fluids simulation and a circuit simulation were also developed. The combination of data from all models enables increased understanding of parameter relationships and intuitive understanding of the behaviors of the flow phantom. This data is used to inform the development of the ABP estimation algorithm from blood flow velocity and arterial distension, as well as validating the algortihm’s outputs.","abstract_html":"One of the most crucial parameters for monitoring Cardiovascular disease (CVD) risk is one’s Arterial Blood Pressure (ABP). Clinicians use a radial arterial catheter to measure ABP in an intensive care unit (ICU). Although this method is considered the gold standard, its invasive nature makes it undesirable and inaccessible outside an ICU. One solution to this problem is to take advantage of ultrasonic measurements, which are noninvasive and extremely accessible. However, developing an algorithm to convert ultrasound data into a legitimate ABP waveform requires an extensive amount of patient data. The limitation is that this data is difficult to obtain and impossible to fully control. The solution presented here is to use a flow phantom: a physical, hydraulic system that mimics arterial blood flow. The phantom provides pressure waveforms, which come directly from a catheterized tube, and volumetric flow waveforms, from an ultrasonic flow meter, that closely match the morphology of patient data. Developing a physical model of the arterial system allows for control over an expanded range of parameter relationships for experimentation. To help understand the behavior and results of the flow phantom, a hydraulic fluids simulation and a circuit simulation were also developed. The combination of data from all models enables increased understanding of parameter relationships and intuitive understanding of the behaviors of the flow phantom. This data is used to inform the development of the ABP estimation algorithm from blood flow velocity and arterial distension, as well as validating the algortihm’s outputs.","abstract_has_math":false,"creators":["Harabedian, Jeanne"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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Clinicians use a radial arterial catheter to measure ABP in an intensive care unit (ICU). Although this method is considered the gold standard, its invasive nature makes it undesirable and inaccessible outside an ICU. One solution to this problem is to take advantage of ultrasonic measurements, which are noninvasive and extremely accessible. However, developing an algorithm to convert ultrasound data into a legitimate ABP waveform requires an extensive amount of patient data. The limitation is that this data is difficult to obtain and impossible to fully control. The solution presented here is to use a flow phantom: a physical, hydraulic system that mimics arterial blood flow. The phantom provides pressure waveforms, which come directly from a catheterized tube, and volumetric flow waveforms, from an ultrasonic flow meter, that closely match the morphology of patient data. Developing a physical model of the arterial system allows for control over an expanded range of parameter relationships for experimentation. To help understand the behavior and results of the flow phantom, a hydraulic fluids simulation and a circuit simulation were also developed. The combination of data from all models enables increased understanding of parameter relationships and intuitive understanding of the behaviors of the flow phantom. This data is used to inform the development of the ABP estimation algorithm from blood flow velocity and arterial distension, as well as validating the algortihm’s outputs."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Modeling the Arterial System to Improve Ultrasound Measurements of Hemodynamic Parameters"]}]}],"canonical_facts":{"dc:contributor.advisor":["Sodini, Charles G."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Harabedian, Jeanne"],"dc:date.accessioned":["2022-08-29T16:11:38Z"],"dc:date.available":["2022-08-29T16:11:38Z"],"dc:date.issued":["2022-05"],"dc:description.abstract":["One of the most crucial parameters for monitoring Cardiovascular disease (CVD) risk is one’s Arterial Blood Pressure (ABP). Clinicians use a radial arterial catheter to measure ABP in an intensive care unit (ICU). Although this method is considered the gold standard, its invasive nature makes it undesirable and inaccessible outside an ICU. One solution to this problem is to take advantage of ultrasonic measurements, which are noninvasive and extremely accessible. However, developing an algorithm to convert ultrasound data into a legitimate ABP waveform requires an extensive amount of patient data. The limitation is that this data is difficult to obtain and impossible to fully control. The solution presented here is to use a flow phantom: a physical, hydraulic system that mimics arterial blood flow. The phantom provides pressure waveforms, which come directly from a catheterized tube, and volumetric flow waveforms, from an ultrasonic flow meter, that closely match the morphology of patient data. Developing a physical model of the arterial system allows for control over an expanded range of parameter relationships for experimentation. To help understand the behavior and results of the flow phantom, a hydraulic fluids simulation and a circuit simulation were also developed. The combination of data from all models enables increased understanding of parameter relationships and intuitive understanding of the behaviors of the flow phantom. This data is used to inform the development of the ABP estimation algorithm from blood flow velocity and arterial distension, as well as validating the algortihm’s outputs."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/144787"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Modeling the Arterial System to Improve Ultrasound Measurements of Hemodynamic Parameters"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Engineering in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:21:00Z"}