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Noise Suppression in Ultrasound Beamforming Using Convolutional Neural Networks

dc.creatorChen, Zhanwen
dc.date.accessioned2020-08-23T15:47:25Z
dc.date.available2019-11-19
dc.date.issued2019-11-19
dc.identifier.urietd-11182019-231129
dc.identifier.urihttp://hdl.handle.net/1803/14615
dc.description.abstractMedical ultrasound is a noninvasive, affordable, portable, and real-time diagnostic modality that provides a cross-sectional view of tissues. Ultrasound beamforming is a widely used approach to acquire and process ultrasound data in a focused manner. However, noises from scatterers outside the main lobe of the beam degrade the beamformed images. Recently, frequency-domain multi-layer perceptrons (MLPs) prove effective in suppressing off-axis scattering and improving image contrast. This thesis extends the frequency-domain neural network approach to study the effectiveness of convolutional neural networks (CNNs). I propose a variety of convolutional architectures and investigate the contribution of the convolution operation. Preliminary results show that although hybrid convolutional- and full-connected neural networks can achieve a similar performance compared with MLPs, fully-convolutional neural networks do not perform well because they do not learn additional features. Instead, they approximate full connections by having a large effective receptive field.
dc.format.mimetypeapplication/pdf
dc.subjectneural networks
dc.subjectdeep learning
dc.subjectultrasound
dc.subjectbeamforming
dc.subjectconvolutional neural networks
dc.subjectconvolution
dc.titleNoise Suppression in Ultrasound Beamforming Using Convolutional Neural Networks
dc.typethesis
dc.contributor.committeeMemberMaithilee Kunda
dc.type.materialtext
thesis.degree.nameMS
thesis.degree.levelthesis
thesis.degree.disciplineComputer Science
thesis.degree.grantorVanderbilt University
local.embargo.terms2019-11-19
local.embargo.lift2019-11-19
dc.contributor.committeeChairMatthew Berger


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