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| 001 |
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| 005 |
20150922151219.0 |
| 006 |
m fo d |
| 007 |
cr unu|||||||| |
| 008 |
121127s2012 txu obm 000 0 eng d |
| 035 |
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|a (OCoLC)ocn819423611
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| 035 |
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|a (TxCM)http://hdl.handle.net/1969.1/ETD-TAMU-2010-08-8449
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| 040 |
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|a TXA
|c TXA
|d UtOrBLW
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| 049 |
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|a TXAM
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| 099 |
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|a 2010
|a Thesis
|a 1969.1/ETD-TAMU-2010-08-8449
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| 100 |
1 |
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|a Arzouni, Nibal.
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| 245 |
1 |
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|a Performance analysis between two sparsity constrained mri methods :
|b highly constrained backprojection(hypr) and compressed sensing(cs) for dynamic imaging /
|c by Nibal Arzouni.
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| 264 |
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1 |
|a [College Station, Tex.] :
|b [Texas A&M University],
|c [2012]
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|a 1 online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 500 |
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|a "Major Subject: Electrical Engineering"
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| 588 |
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|a Description from author supplied metadata (automated record created 2012-10-22 13:24:58).
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| 502 |
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|b Master of Science
|c Texas A&M University
|d 2010
|o http://hdl.handle.net/1969.1/ETD-TAMU-2010-08-8449
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| 504 |
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|a Includes bibliographical references.
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| 516 |
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|a Text (Thesis)
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|a One of the most important challenges in dynamic magnetic resonance imaging (MRI) is to achieve high spatial and temporal resolution when it is limited by system performance. It is desirable to acquire data fast enough to capture the dynamics in the image time series without losing high spatial resolution and signal to noise ratio. Many techniques have been introduced in the recent decades to achieve this goal. Newly developed algorithms like Highly Constrained Backprojection (HYPR) and Compressed Sensing (CS) reconstruct images from highly undersampled data using constraints. Using these algorithms, it is possible to achieve high temporal resolution in the dynamic image time series with high spatial resolution and signal to noise ratio (SNR). In this thesis we have analyzed the performance of HYPR to CS algorithm. In assessing the reconstructed image quality, we considered computation time, spatial resolution, noise amplification factors, and artifact power (AP) using the same number of views in both algorithms, and that number is below the Nyquist requirement. In the simulations performed, CS always provides higher spatial resolution than HYPR, but it is limited by computation time in image reconstruction and SNR when compared to HYPR. HYPR performs better than CS in terms of SNR and computation time when the images are sparse enough. However, HYPR suffers from streaking artifacts when it comes to less sparse image data.
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| 500 |
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|a Electronic resource.
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| 650 |
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|a Major Electrical Engineering.
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|a sparsity
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| 653 |
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|a image reconstruction
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| 653 |
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|a compressed sensing
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| 653 |
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|a undersampled data
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| 653 |
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|a Highly constrained backprojection
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| 653 |
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|a Dynamic MRI
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| 653 |
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|a projection imaging
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| 700 |
1 |
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|a Ji, Jim X.,
|e thesis advisor.
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| 856 |
4 |
0 |
|u http://hdl.handle.net/1969.1/ETD-TAMU-2010-08-8449
|z Link to OAK Trust copy
|t 0
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| 948 |
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|a cataloged
|b h
|c 2012/11/27
|d c
|e ceaton
|f 2:03:40 pm
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| 994 |
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|a C0
|b TXA
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| 999 |
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|a MARS
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| 999 |
f |
f |
|s aa45261a-fa6b-3a49-92cf-1cd51063d73c
|i 73d6ee8b-d619-32a1-a0f5-fae292e413d4
|t 0
|
| 952 |
f |
f |
|a Texas A&M University
|b College Station
|c Electronic Resources
|d Available Online
|t 0
|e 2010 Thesis 1969.1/ETD-TAMU-2010-08-8449
|h Other scheme
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| 998 |
f |
f |
|a 2010 Thesis 1969.1/ETD-TAMU-2010-08-8449
|t 0
|l Available Online
|