Title page for etd-0710112-093957


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URN etd-0710112-093957
Author Chia-Hsing Tai
Author's Email Address No Public.
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Department Computer Science and Engineering
Year 2011
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title The Comparison of Using MATLAB, C++ and Parallel Computing for Proton Echo Planar Spectroscopic Imaging Reconstruction
Date of Defense 2012-04-27
Page Count 55
Keyword
  • Parallel Computing
  • Reconstruction
  • Graphic Processing Unit
  • Proton Echo Planar Spectroscopic Imaging
  • Magnetic Resonance Spectroscopic Imaging
  • Abstract Proton echo planar spectroscopic imaging(PEPSI) is a novel and rapid technique of magnetic resonance spectroscopic imaging(MRSI). To analyze the metabolite in PEPSI by using LCModel, an automatic reconstruction system is necessary. Recently, many researches use graphic processing unit(GPU) to accelerate imaging reconstruction, and Compute Unified Device Architecture(CUDA) is developed by C language, so the programmers can write the program in parallel computing easily.
      PEPSI data acquisition includes non water suppression and water suppression scans, each scan contains odd and even echoes, these two data are reconstructed separately. The image reconstruction contains k-space filter, time-domain filter, three-dimension fast Fourier transform(FFT), phase correction and combine odd and even data. We use MATLAB, C++ and parallel computing to implement PEPSI reconstruction, and parallel computing applied CUDA which proposed by NVIDIA.
      In our study, the averaged non water suppression spectroscopic imaging executed by three different programming language are almost the same. In our data scale, the execution time of parallel computing is faster than MATLAB and C++, especially in the FFT step. Therefore, we simulated and compared the performance of one- to three-dimension FFT.
      Our result shows that accelerating performance of GPU depends on the number of data points according to the performance of FFT and the execution time of single coil PEPSI reconstruction. While the amount of data points is larger than 65536, as demonstrated in our study, parallel computing contribute in terms of computational acceleration.
    Advisory Committee
  • Shang-Yueh Tsai - chair
  • Ming-Long Wu - co-chair
  • Ming-Chung Chou - co-chair
  • Tzu-Chao Chuang - co-chair
  • Cheng-Wen Ko - advisor
  • Files
  • etd-0710112-093957.pdf
  • Indicate in-campus at 2 year and off-campus access at 5 year.
    Date of Submission 2012-07-10

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