Three-Dimensional Reconstruction of Electron Tomography Using Graphic Processing Units (GPUs)

Chapter

Abstract

Three-dimensional (3D) reconstruction of electron tomography (ET) has emerged as a leading technique to elucidate the molecular structures of complex biological specimens. Iterative methods using blob basis functions are advantageous reconstruction methods due to their good performance especially under noisy and limited-angle conditions. However, iterative reconstruction algorithms for ET pose tremendous computational challenges. Graphic processing units (GPUs) offer an affordable platform to meet these demands. Nevertheless, due to the limited available memory of GPUs, the weighted matrix involved by iterative methods cannot be located into GPUs especially for the large images. To meet high computational demands, we propose a multilevel parallel scheme to perform iterative algorithm reconstruction using blob on GPUs. In order to address the large memory requirements of the weighted matrix, we also present a matrix storage technique, called blobELL-R, suitable for GPUs. In the storage technique, several geometric related symmetry relationships have been exploited to significantly reduce the storage space. Experimental results indicate that the multilevel parallel reconstruction scheme on GPUs can achieve high and stable speedups. The blobELL-R data structure only needs nearly 1/16 of the storage space in comparison with ELLPACK-R (ELL-R) storage structure and yields significant acceleration compared to the standard and matrix with CRS implementations on CPU.

Keywords

Electron tomography Three-dimensional reconstruction  Iterative methods Blob GPUs 

References

  1. Andersen AH, Kak AC (1984) Simultaneous algebraic reconstruction technique (SART): a superior implementation of the ART algorithm. Ultrason Imaging 6:81–94CrossRefGoogle Scholar
  2. Bilbao-Castro JR, Carazo JM, Garcia I, Fernandze JJ (2006) Parallelization of reconstruction algorithms in three-dimensional electron microscopy. Appl Math Model 30:688–701MATHCrossRefGoogle Scholar
  3. Bisseling RH (2004) Parallel scientific computation. Oxford University Press, OxfordMATHCrossRefGoogle Scholar
  4. Castano-Diez D, Mueller H, Frangakis AS (2007) Implementation and performance evaluation of reconstruction algorithms on graphics processors. J Struct Biol 157:288–295CrossRefGoogle Scholar
  5. Fernandez JJ (2008) High performance computing in structural determination by electron cryomicroscopy. J Struct Biol 164:1–6CrossRefGoogle Scholar
  6. Fernandez JJ, Garcia I, Garazo JM (2004) Three-dimensional reconstruction of cellular structures by electron microscope tomography and parallel computing. J Parallel Distrib Comput 64:285–300MATHCrossRefGoogle Scholar
  7. Frank J (2006) Electron tomography: methods for three-dimensional visualization of structures in the cell, 2nd edn. Springer, New YorkGoogle Scholar
  8. Gilbert P (1972a) Iterative methods for the 3D reconstruction of an object from projections. J Theor Biol 76:105–117CrossRefGoogle Scholar
  9. Gilbert P (1972b) Iterative methods for the 3D reconstruction of an object from projections. J Theor Biol 36:105–117CrossRefGoogle Scholar
  10. Herman GT (2009) Image reconstruction from projections: the fundamentals of computerized tomography, 2nd edn. Springer, LondonGoogle Scholar
  11. John RR, Ronald FB (1985) Solving elliptic problems using ELLPACK. Springer, New YorkMATHGoogle Scholar
  12. Lewitt RM (1992) Alternatives to voxels for image representation in iterative reconstruction algorithms. Phys Med Biol 37:705–716CrossRefGoogle Scholar
  13. Matej S, Lewitt RM (1995) Efficient 3D grids for image-reconstruction using spherically-symmetrical volume elements. IEEE Trans Nucl Sci 42:1361–1370CrossRefGoogle Scholar
  14. NVIDIA (2008) CUDA Programming Guide. http://www.nvidia.com/cuda
  15. Shufeng S et al (2009) 3D structural investigation of caveolae from porcine aorta endothelial cell by electron tomography. Prog Biochem Biophy 36(6):729–735Google Scholar
  16. Vazquez F, Garzon EM, Fernandez JJ (2009) Accelerating sparse matrix-vector product with GPUs. In: Proceedings of CMMSE09’, pp 1081–1092.Google Scholar
  17. Vazquez F, Garzon EM, Fernandez JJ (2010) A matrix approach to tomographic reconstruction and its implementation on GPUs. J Struct Biol 170:146–151CrossRefGoogle Scholar
  18. Xiaohua W (2009) Modified simultaneous algebraic reconstruction technique and its parallelization in cryo-electron tomography. In: Proceedings of ICPADS09’, 2009.Google Scholar
  19. Xu W et al (2010) High-performance iterative electron tomography reconstruction wigh long-object compensation using graphics processing units (GPUs). J Struct Biol 171:142–153CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of Computing TechnologyBeijingChina
  2. 2.Chinese Academy of SciencesGraduate UniversityBeijingChina

Personalised recommendations