Optimization algorithms in compressive sensing (CS) sparse magnetic resonance imaging (MRI)
| dc.contributor.advisor | Aruliah, Dhavide | |
| dc.contributor.author | Takeva-Velkova, Viliyana | |
| dc.date.accessioned | 2010-10-13T20:40:19Z | |
| dc.date.accessioned | 2022-03-29T17:06:31Z | |
| dc.date.available | 2010-10-13T20:40:19Z | |
| dc.date.available | 2022-03-29T17:06:31Z | |
| dc.date.issued | 2010-06-01 | |
| dc.degree.discipline | Modelling and Computational Science | en |
| dc.degree.level | Master of Science (MSc) | en |
| dc.description.abstract | 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. | en |
| dc.description.sponsorship | University of Ontario Institute of Technology | en |
| dc.identifier.uri | https://hdl.handle.net/10155/104 | |
| dc.language.iso | en | en |
| dc.subject | Compressive sensing | en |
| dc.subject | Sparse MRI | en |
| dc.subject | Convex optimization | en |
| dc.title | Optimization algorithms in compressive sensing (CS) sparse magnetic resonance imaging (MRI) | en |
| dc.type | Thesis | en |