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Magnetic Resonance Imaging by Compressed Sensing

Oliaee, Ashkan | 2012

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 44649 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Fatemi-Zadeh, Emadodden
  7. Abstract:
  8. Magnetic Resonance Imaging (MRI) is a non-invasive imaging modality which can represents the structure, metabolism and the function of inner tissues and organs. Unlike other imaging modalities MRI does not use ionizing radiation.Reducing the imaging time will result in cost reduction and patient comfort. Therefore since the invention of MRI, increasing the speed of imaging has drawn a lot of attention. This was mainly done by improving and upgrading the data collecting hardware of the imaging module.With the advances in technology, a point has been nearly reached, that due to the physical and physiological constraints, such as nerve stimulation, quickening the hardware is impractical. Therefore having high quality images obtained from a lot fewer data, would be desired. One of the most suitable methods accomplishing this idea is the so called Compressive Sensing or shortly the CS, which attracted a lot of attention these days. In this method the data are implicitly compressed in the acquisition process by obtaining incoherent measurements and then they reconstructed by a non-linear algorithm. This so called incoherent sampling is achieved by randomized sampling of the k-space.In this dissertation we did try to combine the CS theory and the MR imaging. To be more precised, the MR imaging is treated like a compressed sensing problem with huge amounts of data and variable and different parts of CS idea such as sampling method, incoherent measurements and reconstruction algorithms are revised in a way which the speed increased, quality maintained and the computational burden is diminished
  9. Keywords:
  10. Magnetic Resonance Imagin (MRI) ; Convex Optimization ; Wavelet Transform ; Compressive Sensing ; Noncoherent Sampling

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