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http://hdl.handle.net/11375/16810
Title: | Sequence Design and Contrast Optimization of Susceptibility Weighted Imaging |
Authors: | Xu, Yingbiao |
Advisor: | Haacke, E. Mark Wong, Max |
Department: | Electrical and Computer Engineering |
Keywords: | Susceptibility Weighted Imaging;MRI;susceptibility difference;contrast to noice ratio;voxel;imaging parameters |
Publication Date: | Mar-2008 |
Abstract: | Susceptibility Weighted Imaging (SWI) utilizes the susceptibility difference between tissues to create a new type of imaging contrast in MRI that is different from conventional spin density, T1-, or T2-weighted imaging. The SWI sequence is a high resolution, fully flow compensated gradient echo sequence. High resolution reduces the signal loss caused by local field inhomogeneities yet with relatively long echo time sufficient contrast can be generated between tissues with a susceptibility difference. Contrast between tissues in the phase image is directly proportional to the susceptibility difference and can be used to enhance the contrast in the magnitude image. In this thesis, we optimize the contrast to noise ratio (CNR) in the magnitude image as a function of the multiplication of the phase mask generated from the phase image. We find that a shorter echo time has the advantage of achieving higher CNR efficiency compared with longer echo times. SWI has found numerous clinical applications due to its sensitivity to blood products. Partial volume effects occur when a voxel contains both venous blood and brain parenchyma. We studied the apparent phase of a voxel as a function of imaging resolution and predict what the best imaging parameters for a specific clinical application should be. Currently, a long acquisition time is the bottleneck for SWI to be used as a routine protocol in the clinical environment. This thesis evaluates segmented echo planar imaging (SEPI) as an alternative to speed up the acquisition while reducing the artifacts usually associated with other fast imaging methods. |
URI: | http://hdl.handle.net/11375/16810 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Xu Yingbiao.pdf | Thesis | 31.2 MB | Adobe PDF | View/Open |
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