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A block-wise approximate parallel implementation for ART algorithm on CUDA-enabled GPU

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 4th International Conference on Biomedical Engineering and Biotechnology, 18–21 August 2015, Shanghai, China

Guest editors: Feng Liu, Dong-Hoon Lee, Ricardo Lagoa and Sandeep Kumar

Article type: Research Article

Authors: Fan, Zhongyina; b | Xie, Yaoqina; *

Affiliations: [a] Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China | [b] Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, China

Correspondence: [*] Address for correspondence: Yaoqin Xie, Research Centre for Medical Robotics and Minimally Invasive Surgical Devices, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China. Tel.: 86-755-86392281/13620902469; Fax: 86-755-86392299; E-mail: [email protected].

Keywords: CT, ART, GPU, block-wise, approximate parallel

DOI: 10.3233/BME-151398

Journal: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S1027-S1035, 2015

Published: 2015
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Computed tomography (CT) has been widely used to acquire volumetric anatomical information in the diagnosis and treatment of illnesses in many clinics. However, the ART algorithm for reconstruction from under-sampled and noisy projection is still time-consuming. It is the goal of our work to improve a block-wise approximate parallel implementation for the ART algorithm on CUDA-enabled GPU to make the ART algorithm applicable to the clinical environment. The resulting method has several compelling features: (1) the rays are allotted into blocks, making the rays in the same block parallel; (2) GPU implementation caters to the actual industrial and medical application demand. We test the algorithm on a digital shepp-logan phantom, and the results indicate that our method is more efficient than the existing CPU implementation. The high computation efficiency achieved in our algorithm makes it possible for clinicians to obtain real-time 3D images.

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