Graphic processing unit based phase retrieval and CT reconstruction for differential X-ray phase contrast imaging
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Compared with the conventional X-ray absorption imaging, the X-ray phase-contrast imaging shows higher contrast on samples with low attenuation coefficient like blood vessels and soft tissues. Among the modalities of phase-contrast imaging, the grating-based phase contrast imaging has been widely accepted owing to the advantage of wide range of sample selections and exemption of coherent source. However, the downside is the substantially larger amount of data generated from the phase-stepping method which slows down the reconstruction process. Graphic processing unit (GPU) has the advantage of allowing parallel computing which is very useful for large quantity data processing. In this paper, a compute unified device architecture (CUDA) C program based on GPU is introduced to accelerate the phase retrieval and filtered back projection (FBP) algorithm for grating-based tomography. Depending on the size of the data, the CUDA C program shows different amount of speed-up over the standard C program on the same Visual Studio 2010 platform. Meanwhile, the speed-up ratio increases as the size of data increases.
Key wordsgrating-based phase contrast imaging parallel computing graphic processing unit (GPU) compute unified device architecture (CUDA) filtered back projection (FBP)
CLC numberR 318.6
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- Jerjen I, Revol V, Kottler C, et al. Phase contrast cone beam tomography with an X-ray grating interferometer [C]//American Institute of Physics Conference Proceedings. New York, USA: American Institute of Physics, 2010: 227–231.Google Scholar
- Nvidia C, Cuda C. Programming guide [M]. Santa Clara, USA: NVIDIA Corporation Cuda Toolkit, 2014.Google Scholar
- Scherl H, Keck B, Kowarschik M, et al. Fast GPU-based CT reconstruction using the common unified device architecture (CUDA) [C]//Nuclear Science Symposium Conference Record. Hawaii, USA: IEEE. 2007: 4464–4466.Google Scholar
- Mukherjeet S, Moore N, Brock J, et al. CUDA and OpenCL implementations of 3D CT reconstruction for biomedical imaging [C]//IEEE Conference on High Performance Extreme Computing (HPEC). Massachusetts, USA: IEEE, 2012: 1–6.Google Scholar
- Nvidia C. C programming best practices guide [M]. USA: NVIDIA Corporation Cuda Toolkit, 2014.Google Scholar
- Bech M. X-ray imaging with a grating interferometer [D]. Copenhagen, Denmark: Niels Bohr Institute, University of Copenhagen, 2009.Google Scholar
- Bech M, Jensen T H, Bunk O, et al. Advanced contrast modalities for X-ray radiology: Phase-contrast and dark-field imaging using a grating interferometer [J]. Journal of Medical Physics, 2010, 20(1): 7–16.Google Scholar
- Zhuang T. The principle and algorithm of CT [M]. Shanghai, China: Shanghai Jiao Tong University Press, 1992 (in Chinese).Google Scholar