CUDA-based high-performance computing of the S-BPF algorithm with no-waiting pipelining

Lin Deng; Bin Yan; Qingmei Chang; Yu Han; Xiang Zhang; Xiaoqi Xi; Lei Li

doi:10.1117/12.2199794

15 October 2015 CUDA-based high-performance computing of the S-BPF algorithm with no-waiting pipelining

Lin Deng, Bin Yan, Qingmei Chang, Yu Han, Xiang Zhang, Xiaoqi Xi, Lei Li

Proceedings Volume 9673, AOPC 2015: Micro/Nano Optical Manufacturing Technologies; and Laser Processing and Rapid Prototyping Techniques; 96730R (2015) https://doi.org/10.1117/12.2199794
Event: Applied Optics and Photonics China (AOPC2015), 2015, Beijing, China

Abstract

The backprojection-filtration (BPF) algorithm has become a good solution for local reconstruction in cone-beam computed tomography (CBCT). However, the reconstruction speed of BPF is a severe limitation for clinical applications. The selective-backprojection filtration (S-BPF) algorithm is developed to improve the parallel performance of BPF by selective backprojection. Furthermore, the general-purpose graphics processing unit (GP-GPU) is a popular tool for accelerating the reconstruction. Much work has been performed aiming for the optimization of the cone-beam back-projection. As the cone-beam back-projection process becomes faster, the data transportation holds a much bigger time proportion in the reconstruction than before. This paper focuses on minimizing the total time in the reconstruction with the S-BPF algorithm by hiding the data transportation among hard disk, CPU and GPU. And based on the analysis of the S-BPF algorithm, some strategies are implemented: (1) the asynchronous calls are used to overlap the implemention of CPU and GPU, (2) an innovative strategy is applied to obtain the DBP image to hide the transport time effectively, (3) two streams for data transportation and calculation are synchronized by the cudaEvent in the inverse of finite Hilbert transform on GPU. Our main contribution is a smart reconstruction of the S-BPF algorithm with GPU’s continuous calculation and no data transportation time cost. a 512³ volume is reconstructed in less than 0.7 second on a single Tesla-based K20 GPU from 182 views projection with 512² pixel per projection. The time cost of our implementation is about a half of that without the overlap behavior.

Citation Download Citation

Lin Deng, Bin Yan, Qingmei Chang, Yu Han, Xiang Zhang, Xiaoqi Xi, and Lei Li "CUDA-based high-performance computing of the S-BPF algorithm with no-waiting pipelining", Proc. SPIE 9673, AOPC 2015: Micro/Nano Optical Manufacturing Technologies; and Laser Processing and Rapid Prototyping Techniques, 96730R (15 October 2015); https://doi.org/10.1117/12.2199794

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KEYWORDS

Reconstruction algorithms

Algorithm development

Image processing

Sensors

GPU based image processing

X-ray computed tomography

Data hiding

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