Multi-streaming and multi-GPU optimization for a matched pair of Projector and Backprojector

Abstract : Iterative reconstruction methods are used in X-ray Computed Tomography in order to improve the quality of reconstruction compared to filtered backprojection methods. However, these methods are computationally expensive due to repeated projection and backprojection operations. Among the possible pairs of projector and backprojector, the Separable Footprint (SF) pair has the advantage to be matched in order to ensure the convergence of the reconstruction algorithm. Nevertheless, this pair implies more computations compared to unmatched pairs commonly used in order to reduce the computation time. In order to speed up this pair, the projector and the backprojector can be parallelized on GPU. Following one of our previous work, in this paper, we propose a new implementation which takes benefits from the factorized calculations of the SF pair in order to increase the number of data handled by each thread. We also describe the adaptation of this implementation for multi-streaming computations. The method is tested on large volumes of size 1024 3 and 2048 3 voxels.
Document type :
Conference papers
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-02070223
Contributor : Nicolas Gac <>
Submitted on : Thursday, March 28, 2019 - 10:45:30 PM
Last modification on : Tuesday, June 4, 2019 - 4:02:17 PM

File

SF_GPU_Georgin_fully3d_2019_fi...
Files produced by the author(s)

Identifiers

Citation

Nicolas Georgin, Camille Chapdelaine, Nicolas Gac, Mohammad-Djafari Ali, Estelle Parra-Denis. Multi-streaming and multi-GPU optimization for a matched pair of Projector and Backprojector. The 2019 International Conference on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Jun 2019, Philadelphia, United States. ⟨10.1117/12.2534108⟩. ⟨hal-02070223⟩

Share

Metrics

Record views

70

Files downloads

36