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dc.contributor.advisorJi, Jim
dc.creatorJiraraksopakun, Yuttapong
dc.date.accessioned2010-07-15T00:11:31Z
dc.date.accessioned2010-07-23T21:43:08Z
dc.date.available2010-07-15T00:11:31Z
dc.date.available2010-07-23T21:43:08Z
dc.date.created2009-05
dc.date.issued2010-07-14
dc.date.submittedMay 2009
dc.identifier.urihttps://hdl.handle.net/1969.1/ETD-TAMU-2009-05-335
dc.description.abstractA complex and challenging problem in flow study is to obtain quantitative flow information in opaque systems, for example, blood flow in biological systems and flow channels in chemical reactors. In this regard, MRI is superior to the conventional optical flow imaging or ultrasonic Doppler imaging. However, for high speed flows, complex flow behaviors and turbulences make it difficult to image and analyze the flows. In MR flow imaging, MR tagging technique has demonstrated its ability to simultaneously visualize motion in a sequence of images. Moreover, a quantification method, namely HARmonic Phase (HARP) analysis, can extract a dense velocity field from tagged MR image sequence with minimal manual intervention. In this work, we developed and validated two new MRI methods for quantification of very rapid flows. First, HARP was integrated with a fast MRI imaging method called SEA (Single Echo Acquisition) to image and analyze high velocity flows. Second, an improved HARP method was developed to deal with tag fading and data noise in the raw MRI data. Specifically, a regularization method that incorporates the law of flow dynamics in the HARP analysis was developed. Finally, the methods were validated using results from the computational fluid dynamics (CFD) and the conventional optimal flow imaging based on particle image velocimetry (PIV). The results demonstrated the improvement from the quantification using solely the conventional HARP method.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.subjectimage motionen
dc.subjectmotion estimationen
dc.subjectMR flow quantificationen
dc.subjectMRIen
dc.subjectMR taggingen
dc.titleFlow Imaging Using MRI: Quantification and Analysisen
dc.typeBooken
dc.typeThesisen
thesis.degree.departmentElectrical and Computer Engineeringen
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorTexas A&M Universityen
thesis.degree.nameDoctor of Philosophyen
thesis.degree.levelDoctoralen
dc.contributor.committeeMemberChan, Andrew K.
dc.contributor.committeeMemberKundur, Deepa
dc.contributor.committeeMemberChoe, Yoonsuck
dc.contributor.committeeMemberMcDougall, Mary P.
dc.type.genreElectronic Dissertationen
dc.type.materialtexten


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