Title page for etd-0804110-144829


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URN etd-0804110-144829
Author Chun-Cheng Chen
Author's Email Address No Public.
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Department Computer Science and Engineering
Year 2009
Semester 2
Degree Master
Type of Document
Language English
Title GPU Acceleration of 3D MRSI using CUDA
Date of Defense 2010-06-28
Page Count 77
Keyword
  • Fourier transform
  • GPU
  • Magnetic Resonance Spectroscopy
  • CUDA
  • Magnetic Resonance Imaging
  • Abstract Using Graphic Processor Unit (GPU) to process the parallel operation via Compute Unified Device Architecture (CUDA) is a new technology in recent years. In the past, the GPU has been used in parallel operation but it was not easy for programming so that it couldn’t be widely used in applications. CUDA is the newly-developed environment based on C language mainly for improving the complexity in programming with CUDA. The applications of GPU with CUDA has been expending to various fields gradually due to support of IEEE floating point as well as its lower cost in hardware while comparing to the super computers. Magnetic Resonance Spectroscopy (MRS) has the feature of non-invasive to probe the concentration distributed of metabolites in vivo. It can assist doctor in clinical diagnosis. The Magnetic Resonance Spectroscopy Imaging (MRSI) is imaging by many Signal Voxel Spectroscopy (SVS) to become multi-dimension MRS image. In MRSI, it can offer more information than SVS. CUDA are applied to MR image widely such as accelerating the image reconstruction and promoting the image quality, but in MRS it is seldom for the related application. In this paper, we using the CUDA to applied in MRS, the MRSI data pre-processing, to accelerate the spatial location in MRSI.
    In this work, we firstly use random data with different dimensions: 1D (one-dimension), 2D and 3D to evaluate the performance of Fourier transformation by using CUDA. We also finally apply some GE 2D/3D MRSI data to see how the acceleration of using CUDA works. Our results show that the acceleration rate of Fastest Fourier Transform (FFT) with CUDA in 1D, 2D and 3D random data largely increases as the data size increases. In the experiment of 2D/3D MRSI data, we find that using CUDA for accelerating the MRSI RAW-file generating procedure would avoid the data moving times, and it is not good for CUDA 1D FFT with parallel architecture while too small data amount processing in kernel. Therefore, how to solve the relationship between MRSI data format with CUDA FFT library and how to decrease the data moving time will discuss in the study.
    Advisory Committee
  • Tzu-Chao Chuang - chair
  • Shang-Yueh Tsai - co-chair
  • Teng-Yi Huang - co-chair
  • Cheng-Wen Ko - advisor
  • Files
  • etd-0804110-144829.pdf
  • indicate in-campus access in a year and off_campus not accessible
    Date of Submission 2010-08-04

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