Iterative Reconstruction for Transmission Tomography on GPU Using Nvidia CUDA

被引：10
作者：
Vintache D. [1 ]
Humbert B. [1 ]
Brasse D. [1 ]
机构：
[1] Institut Pluridisciplinaire Hubert Curien, CNRS/IN2P3, 67037 Strasbourg, 23 rue du Loess
来源：
Tsinghua Science and Technology | 2010年 / 15卷 / 01期
关键词：
image reconstruction; parallel processing; tomography;
D O I：
10.1016/S1007-0214(10)70002-X
中图分类号：
学科分类号：
摘要：
The iterative reconstruction algorithms for X-ray CT image reconstruction suffer from their high computational cost. Recently Nvidia releases common unified device architecture (CUDA), allowing developers to access to the processing power of Nvidia graphical processing units (GPUs), in order to perform general purpose computations. The use of the GPU, as an alternative computation platform, allows decreasing processing times, for parallel algorithms. This paper aims to demonstrate the feasibility of such an implementation for the iterative image reconstruction. The ordered subsets convex (OSC) algorithm, an iterative reconstruction algorithm for transmission tomography, has been developed with CUDA. The performances have been evaluated and compared with another implementation using a single CPU node. The result shows that speed-ups of two orders of magnitude, with a negligible impact on image accuracy, have been observed. © 2010 Tsinghua University Press.
引用
收藏
页码：11 / 16
页数：5
相关论文
共 18 条
  • [1] Feldkamp L.A., Davis L.C., Kress J.W., Practical cone-beam algorithm, Journal of the Optical Society of America A, 1, 6, pp. 612-619, (1984)
  • [2] Kak A.C., Slanet M., Principles of Computerized Tomographic Imaging, (1988)
  • [3] Lange K., Fessler J.A., Globally convergent algorithms for maximum a posteriori transmission tomography, IEEE Transactions on Image Processing, 4, 10, pp. 1430-1438, (1995)
  • [4] Beekman F.J., Kamphuis C., Ordered subset reconstruction for x-ray CT, Physics in Medicine and Biology, 46, pp. 1835-1844, (2001)
  • [5] Elbakri I.A., Fessler J.A., Statistical image reconstruction for polyenergetic x-ray computed tomography, IEEE Transactions on Medical Imaging, 21, 2, pp. 89-99, (2002)
  • [6] Chueh H.S., Tsai W.K., Chang C.C., Et al., An iterative reconstruction for poly-energetic X-ray computed tomography, Medical Imaging and Informatics, 4987, pp. 44-50, (2008)
  • [7] Kole J.S., Beekman F.J., Evaluation of accelerated iterative x-ray CT image reconstruction using floating point graphics hardware, Physics in Medicine and Biology, 51, pp. 875-889, (2006)
  • [8] Weinlich A., Keck B., Scherl H., Et al., Comparison of high-speed ray casting on GPU using CUDA and openGL, Proceedings of the First International Workshop on New Frontiers in High-Performance and Hardware-Aware Computing, (2008)
  • [9] Ollinger J.M., Maximum-likelihood reconstruction of transmission images in emission computed tomography via the EM algorithm, IEEE Transactions on Image Processing, 13, 1, pp. 89-101, (1994)
  • [10] Shetye A., Shekhar R., A statistical approach to high-quality CT reconstruction at low radiation doses for real-time guidance and navigation, Proceedings of SPIE Medical Imaging, 6510, (2007)
12