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dc.contributorNordstrom, Kerstin
dc.contributorCamp, Amy
dc.contributor.advisorHerd, Maria Teresa
dc.contributor.authorBrandt, Naomi
dc.date.accessioned2019-06-24T13:03:29Z
dc.date.available2019-06-24T13:03:29Z
dc.date.issued2019-06-24
dc.identifier.urihttp://hdl.handle.net/10166/5701
dc.description.abstractIn the field of ultrasound imaging, it has been theorized that imaging noise, known as speckle, is the product of microscopic scatterers and abnormalities within the imaged tissue. This would result in certain speckling patterns revealing themselves over large datasets, which could be utilized to identify minuscule lesions within tissues, potentially creating a method to predict the early formation of tumors. Such a dataset would be difficult to analyze by hand, but machine learning algorithms could be used to recognize patterns in a effective manner. As of now, few attempts have been made to utilize machine learning in order to predict scatterer placement from ultrasound scans. In order to initiate machine learning, first a computational simulation must be constructed to consistently and accurately reproduce experimental data. Using Field II, a MATLAB-based program for ultrasound modelling, simulations were created to replicate data produced from experimental phantoms made from glass beads and agarose gel. These simulations were designed to account for bead placement and size, as well as experimental conditions. Comparisons between simulations and experimental data using statistical analysis show that ultrasound images can accurately be predicted using computational methods. With these software programs, it becomes possible to train a machine learning algorithm to recognize speckling pattern, which may allow for the resolution of previously unresovable scatterers.en_US
dc.description.sponsorshipPhysicsen_US
dc.language.isoen_USen_US
dc.subjectphysicsen_US
dc.subjectultrasounden_US
dc.subjectsimulationsen_US
dc.subjectradio frequency ultrasounden_US
dc.subjectmachine learningen_US
dc.subjectultrasound Imagingen_US
dc.subjectbrightness mode imagingen_US
dc.subjectspeckleen_US
dc.subjectacoustic speckleen_US
dc.titleDesigning Simulated Radio Frequency Ultrasound Traces for the Training of Machine Learning Algorithmsen_US
dc.typeThesis
dc.date.gradyear2019en_US
mhc.institutionMount Holyoke College
mhc.degreeUndergraduateen_US
dc.rights.restrictedpublicen_US


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