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Proceedings Paper

GPU-based cone-beam reconstruction using wavelet denoising
Author(s): Kyungchan Jin; Jungbyung Park; Jongchul Park
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Paper Abstract

The scattering noise artifact resulted in low-dose projection in repetitive cone-beam CT (CBCT) scans decreases the image quality and lessens the accuracy of the diagnosis. To improve the image quality of low-dose CT imaging, the statistical filtering is more effective in noise reduction. However, image filtering and enhancement during the entire reconstruction process exactly may be challenging due to high performance computing. The general reconstruction algorithm for CBCT data is the filtered back-projection, which for a volume of 512×512×512 takes up to a few minutes on a standard system. To speed up reconstruction, massively parallel architecture of current graphical processing unit (GPU) is a platform suitable for acceleration of mathematical calculation. In this paper, we focus on accelerating wavelet denoising and Feldkamp-Davis-Kress (FDK) back-projection using parallel processing on GPU, utilize compute unified device architecture (CUDA) platform and implement CBCT reconstruction based on CUDA technique. Finally, we evaluate our implementation on clinical tooth data sets. Resulting implementation of wavelet denoising is able to process a 1024×1024 image within 2 ms, except data loading process, and our GPU-based CBCT implementation reconstructs a 512×512×512 volume from 400 projection data in less than 1 minute.

Paper Details

Date Published: 3 March 2012
PDF: 6 pages
Proc. SPIE 8313, Medical Imaging 2012: Physics of Medical Imaging, 83133Q (3 March 2012); doi: 10.1117/12.910704
Show Author Affiliations
Kyungchan Jin, Korea Institute of Industrial Technology (Korea, Republic of)
Jungbyung Park, DRGEM Corp. (Korea, Republic of)
Jongchul Park, Digital Imaging Tech. (Korea, Republic of)


Published in SPIE Proceedings Vol. 8313:
Medical Imaging 2012: Physics of Medical Imaging
Norbert J. Pelc; Robert M. Nishikawa; Bruce R. Whiting, Editor(s)

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