Optimization algorithms in compressive sensing (CS) sparse magnetic resonance imaging (MRI)

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.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Modelling and Computational Science

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2010

Author dc:creator

• Takeva-Velkova, Viliyana

Advisor dc:contributor.advisor

• Aruliah, Dhavide

• Compressive sensing
• Sparse MRI
• Convex optimization

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/104

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/104