High resolution iterative CT reconstruction using graphics hardware

Particular applications of computed tomography require high slice resolutions. The fastest iterative implementations on graphics cards use 3-D textures to exploit hardware-accelerated trilinear interpolation. However, the size of 3-D textures is subject to technical limitations, which makes them inapplicable here. Alternatively a 2-D texture array can be used instead of the 3-D texture as in early graphics implementations. The additional memory synchronizations cause a significant loss of performance. We utilize new features of the recently released CUDA 2.2 framework to improve the performance of the Simultaneous Algebraic Reconstruction Technique (SART). In this paper we present an enhanced version of our efficient implementation of the most time-consuming parts of the iterative reconstruction algorithm: forward- and back-projection. We explain the required strategy to adapt the algorithm for the CUDA 2.2 features, in particular the usage of 2-D texture lookups from pitchlinear memory. Finally, we compare the result to our previous ones with respect to both reconstruction speed and technical limitations. The proposed strategy is a new balance between performance and limitations in resolution.

[1]  Michael Knaup,et al.  GPU-based parallel-beam and cone-beam forward- and backprojection using CUDA , 2008, 2008 IEEE Nuclear Science Symposium Conference Record.

[2]  J. Hornegger,et al.  Fast GPU-Based CT Reconstruction using the Common Unified Device Architecture (CUDA) , 2007, 2007 IEEE Nuclear Science Symposium Conference Record.

[3]  Markus Kowarschik,et al.  GPU-accelerated SART reconstruction using the CUDA programming environment , 2009, Medical Imaging.

[4]  Ramesh R. Galigekere,et al.  Cone-beam reprojection using projection-matrices , 2003, IEEE Transactions on Medical Imaging.

[5]  H. Malcolm Hudson,et al.  Accelerated image reconstruction using ordered subsets of projection data , 1994, IEEE Trans. Medical Imaging.

[6]  Klaus Mueller,et al.  A comparative study of popular interpolation and integration methods for use in computed tomography , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[7]  Markus Kowarschik,et al.  Comparison of High-Speed Ray Casting on GPU using CUDA and OpenGL , 2008 .

[8]  Klaus Mueller,et al.  Why do commodity graphics hardware boards (GPUs) work so well for acceleration of computed tomography? , 2007, Electronic Imaging.

[9]  Jie Tian,et al.  Fast cone-beam CT image reconstruction using GPU hardware , 2008 .

[10]  Günter Lauritsch,et al.  On-the-fly-Reconstruction in Exact Cone-Beam CT using the Cell Broadband Engine Architecture , 2007 .

[11]  Gengsheng Lawrence Zeng,et al.  Unmatched projector/backprojector pairs in an iterative reconstruction algorithm , 2000, IEEE Transactions on Medical Imaging.

[12]  A. Kak,et al.  Simultaneous Algebraic Reconstruction Technique (SART): A Superior Implementation of the Art Algorithm , 1984, Ultrasonic imaging.

[13]  Henrik Turbell,et al.  Cone-Beam Reconstruction Using Filtered Backprojection , 2001 .

[14]  L. Shepp,et al.  Maximum Likelihood Reconstruction for Emission Tomography , 1983, IEEE Transactions on Medical Imaging.

[15]  Xinwei Xue,et al.  Hardware-accelerated cone-beam reconstruction on a mobile C-arm , 2007, SPIE Medical Imaging.

[16]  Klaus Mueller,et al.  Rapid 3D cone-beam reconstruction with the Algebraic Reconstruction Technique (ART) by utilizing texture mapping graphics hardware , 1998, 1998 IEEE Nuclear Science Symposium Conference Record. 1998 IEEE Nuclear Science Symposium and Medical Imaging Conference (Cat. No.98CH36255).