Magnetic Resonance Imaging (MRI) is an essential instrument in clinical diag-
nosis; however, it is burdened by a slow data acquisition process due to physical
limitations. Compressive Sensing (CS) is a recently developed mathematical
framework that o ers signi cant bene ts in MRI image speed by reducing the
amount of acquired data without degrading the image quality. The process
of image reconstruction involves solving a nonlinear constrained optimization
problem. The reduction of reconstruction time in MRI is of signi cant bene t.
We reformulate sparse MRI reconstruction as a Second Order Cone Program
(SOCP).We also explore two alternative techniques to solving the SOCP prob-
lem directly: NESTA and speci cally designed SOCP-LB. / UOIT
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OOSHDU.10155/104 |
| Date | 01 June 2010 |
| Creators | Takeva-Velkova, Viliyana |
| Contributors | Aruliah, Dhavide |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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