A GPU-based parallel computing framework for accelerating the reconstruction of q-Ball Imaging

Hong Che Lai; Chun Hung Yeh; Kuan Hung Cho; Ching Po Lin; Chia Yen Lee; Yi Ping Chao

doi:10.1109/IS3C.2014.287

A GPU-based parallel computing framework for accelerating the reconstruction of q-Ball Imaging

Hong Che Lai
, Chun Hung Yeh
, Kuan Hung Cho
, Ching Po Lin
, Chia Yen Lee
, Yi Ping Chao^*

^*Corresponding author for this work

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

High-angular resolution diffusion imaging (HARDI) relative to the diffusion tensor imaging (DTI) can resolve the complex fiber crossing of each voxel in the brain, however, the image reconstruction time is longer than conventional technology, therefore, to improve the performance is a very important work to do. We used graphic processing unit (GPU) and CUDA to compute the spherical harmonic function on the reconstruction of QBI. It's very useful on massive performance data (Bid Data). With the large number of matrix elements in GPU for parallel computing. By evaluating with different video cards, the improved performance showed 179 to 574 speed up through shared memory access for computation. Finally, the results will be used in an extension of the three-dimensional spatial visualization and probabilistic tractography.

Original language	English
Title of host publication	Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014
Publisher	IEEE Computer Society
Pages	1103-1106
Number of pages	4
ISBN (Print)	9781479952779
DOIs	https://doi.org/10.1109/IS3C.2014.287
State	Published - 2014
Event	2nd International Symposium on Computer, Consumer and Control, IS3C 2014 - Taichung, Taiwan Duration: 10 06 2014 → 12 06 2014

Publication series

Name	Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014

Conference

Conference	2nd International Symposium on Computer, Consumer and Control, IS3C 2014
Country/Territory	Taiwan
City	Taichung
Period	10/06/14 → 12/06/14

Keywords

Graphic Processing Unit
High Angular Resolution Diffusion Imaging
Parallel Computing
Q-ball Imaging

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10.1109/IS3C.2014.287

Cite this

Lai, H. C., Yeh, C. H., Cho, K. H., Lin, C. P., Lee, C. Y., & Chao, Y. P. (2014). A GPU-based parallel computing framework for accelerating the reconstruction of q-Ball Imaging. In Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014 (pp. 1103-1106). Article 6846079 (Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014). IEEE Computer Society. https://doi.org/10.1109/IS3C.2014.287

Lai, Hong Che ; Yeh, Chun Hung ; Cho, Kuan Hung et al. / A GPU-based parallel computing framework for accelerating the reconstruction of q-Ball Imaging. Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014. IEEE Computer Society, 2014. pp. 1103-1106 (Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014).

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title = "A GPU-based parallel computing framework for accelerating the reconstruction of q-Ball Imaging",

abstract = "High-angular resolution diffusion imaging (HARDI) relative to the diffusion tensor imaging (DTI) can resolve the complex fiber crossing of each voxel in the brain, however, the image reconstruction time is longer than conventional technology, therefore, to improve the performance is a very important work to do. We used graphic processing unit (GPU) and CUDA to compute the spherical harmonic function on the reconstruction of QBI. It's very useful on massive performance data (Bid Data). With the large number of matrix elements in GPU for parallel computing. By evaluating with different video cards, the improved performance showed 179 to 574 speed up through shared memory access for computation. Finally, the results will be used in an extension of the three-dimensional spatial visualization and probabilistic tractography.",

keywords = "Graphic Processing Unit, High Angular Resolution Diffusion Imaging, Parallel Computing, Q-ball Imaging",

author = "Lai, \{Hong Che\} and Yeh, \{Chun Hung\} and Cho, \{Kuan Hung\} and Lin, \{Ching Po\} and Lee, \{Chia Yen\} and Chao, \{Yi Ping\}",

year = "2014",

doi = "10.1109/IS3C.2014.287",

language = "英语",

isbn = "9781479952779",

series = "Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014",

publisher = "IEEE Computer Society",

pages = "1103--1106",

booktitle = "Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014",

address = "美国",

note = "2nd International Symposium on Computer, Consumer and Control, IS3C 2014 ; Conference date: 10-06-2014 Through 12-06-2014",

}

Lai, HC, Yeh, CH, Cho, KH, Lin, CP, Lee, CY & Chao, YP 2014, A GPU-based parallel computing framework for accelerating the reconstruction of q-Ball Imaging. in Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014., 6846079, Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014, IEEE Computer Society, pp. 1103-1106, 2nd International Symposium on Computer, Consumer and Control, IS3C 2014, Taichung, Taiwan, 10/06/14. https://doi.org/10.1109/IS3C.2014.287

A GPU-based parallel computing framework for accelerating the reconstruction of q-Ball Imaging. / Lai, Hong Che; Yeh, Chun Hung; Cho, Kuan Hung et al.
Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014. IEEE Computer Society, 2014. p. 1103-1106 6846079 (Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Yeh, Chun Hung

AU - Cho, Kuan Hung

AU - Lin, Ching Po

AU - Lee, Chia Yen

AU - Chao, Yi Ping

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AB - High-angular resolution diffusion imaging (HARDI) relative to the diffusion tensor imaging (DTI) can resolve the complex fiber crossing of each voxel in the brain, however, the image reconstruction time is longer than conventional technology, therefore, to improve the performance is a very important work to do. We used graphic processing unit (GPU) and CUDA to compute the spherical harmonic function on the reconstruction of QBI. It's very useful on massive performance data (Bid Data). With the large number of matrix elements in GPU for parallel computing. By evaluating with different video cards, the improved performance showed 179 to 574 speed up through shared memory access for computation. Finally, the results will be used in an extension of the three-dimensional spatial visualization and probabilistic tractography.

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Lai HC, Yeh CH, Cho KH, Lin CP, Lee CY, Chao YP. A GPU-based parallel computing framework for accelerating the reconstruction of q-Ball Imaging. In Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014. IEEE Computer Society. 2014. p. 1103-1106. 6846079. (Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014). doi: 10.1109/IS3C.2014.287

A GPU-based parallel computing framework for accelerating the reconstruction of q-Ball Imaging

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