Limits...
An Effective CUDA Parallelization of Projection in Iterative Tomography Reconstruction.

Xie L, Hu Y, Yan B, Wang L, Yang B, Liu W, Zhang L, Luo L, Shu H, Chen Y - PLoS ONE (2015)

Bottom Line: In this paper, a strategy of Fixed Sampling Number Projection (FSNP) is proposed to ensure the operation synchronization in the ray-driven projection with Graphical Processing Unit (GPU).We validate the performance of this FSNP approach using both simulated and real cone-beam CT data.Experimental results show that compare to the conventional approach, the proposed FSNP method together with texture fetching is 10~16 times faster than the conventional approach based on global memory, and thus leads to more efficient iterative algorithm in CT reconstruction.

View Article: PubMed Central - PubMed

Affiliation: Oral Hospital of Jiangsu Province, Affiliated to Nanjing Medical University, Jiangsu, China.

ABSTRACT
Projection and back-projection are the most computationally intensive parts in Computed Tomography (CT) reconstruction, and are essential to acceleration of CT reconstruction algorithms. Compared to back-projection, parallelization efficiency in projection is highly limited by racing condition and thread unsynchronization. In this paper, a strategy of Fixed Sampling Number Projection (FSNP) is proposed to ensure the operation synchronization in the ray-driven projection with Graphical Processing Unit (GPU). Texture fetching is also used utilized to further accelerate the interpolations in both projection and back-projection. We validate the performance of this FSNP approach using both simulated and real cone-beam CT data. Experimental results show that compare to the conventional approach, the proposed FSNP method together with texture fetching is 10~16 times faster than the conventional approach based on global memory, and thus leads to more efficient iterative algorithm in CT reconstruction.

Show MeSH

Related in: MedlinePlus

Calculation of pixel driven back-projection with linear interpolation in 2-D case.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4664243&req=5

pone.0142184.g003: Calculation of pixel driven back-projection with linear interpolation in 2-D case.

Mentions: As illustrated in the 2-D schematic diagram of the pixel-driven back-projection (Fig 3), a line (ray) connecting the radiation source and voxel center intersects with the detector plane for each pixel. Routinely, the linear interpolation or kernel function convolution can be used to estimate the back-projected value with respect to intersection locations [23–28]. Fig 3 also shows that the back-projection operator is in fact a linear interpolation: xj = (p1l2 + p2l1)/(l1 + l2), where l1 + l2 is the distance between the two neighboring detector centers, and p1 and p2 are the corresponding projection values for the neighboring detectors. The outline of the voxel-driven based back-projection algorithm is also given below.


An Effective CUDA Parallelization of Projection in Iterative Tomography Reconstruction.

Xie L, Hu Y, Yan B, Wang L, Yang B, Liu W, Zhang L, Luo L, Shu H, Chen Y - PLoS ONE (2015)

Calculation of pixel driven back-projection with linear interpolation in 2-D case.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4664243&req=5

pone.0142184.g003: Calculation of pixel driven back-projection with linear interpolation in 2-D case.
Mentions: As illustrated in the 2-D schematic diagram of the pixel-driven back-projection (Fig 3), a line (ray) connecting the radiation source and voxel center intersects with the detector plane for each pixel. Routinely, the linear interpolation or kernel function convolution can be used to estimate the back-projected value with respect to intersection locations [23–28]. Fig 3 also shows that the back-projection operator is in fact a linear interpolation: xj = (p1l2 + p2l1)/(l1 + l2), where l1 + l2 is the distance between the two neighboring detector centers, and p1 and p2 are the corresponding projection values for the neighboring detectors. The outline of the voxel-driven based back-projection algorithm is also given below.

Bottom Line: In this paper, a strategy of Fixed Sampling Number Projection (FSNP) is proposed to ensure the operation synchronization in the ray-driven projection with Graphical Processing Unit (GPU).We validate the performance of this FSNP approach using both simulated and real cone-beam CT data.Experimental results show that compare to the conventional approach, the proposed FSNP method together with texture fetching is 10~16 times faster than the conventional approach based on global memory, and thus leads to more efficient iterative algorithm in CT reconstruction.

View Article: PubMed Central - PubMed

Affiliation: Oral Hospital of Jiangsu Province, Affiliated to Nanjing Medical University, Jiangsu, China.

ABSTRACT
Projection and back-projection are the most computationally intensive parts in Computed Tomography (CT) reconstruction, and are essential to acceleration of CT reconstruction algorithms. Compared to back-projection, parallelization efficiency in projection is highly limited by racing condition and thread unsynchronization. In this paper, a strategy of Fixed Sampling Number Projection (FSNP) is proposed to ensure the operation synchronization in the ray-driven projection with Graphical Processing Unit (GPU). Texture fetching is also used utilized to further accelerate the interpolations in both projection and back-projection. We validate the performance of this FSNP approach using both simulated and real cone-beam CT data. Experimental results show that compare to the conventional approach, the proposed FSNP method together with texture fetching is 10~16 times faster than the conventional approach based on global memory, and thus leads to more efficient iterative algorithm in CT reconstruction.

Show MeSH
Related in: MedlinePlus