Table of Contents Author Guidelines Submit a Manuscript
International Journal of Biomedical Imaging
Volume 2012, Article ID 452910, 11 pages
http://dx.doi.org/10.1155/2012/452910
Research Article

High Performance 3D PET Reconstruction Using Spherical Basis Functions on a Polar Grid

1Instituto de Física Corpuscular, Universitat de València/CSIC, Edificio Institutos de Investigación, 22085 Valencia, Spain
2Departamento de Física Atómica, Molecular y Nuclear, Universitat de València, 46100 Valencia, Spain

Received 14 October 2011; Revised 18 January 2012; Accepted 26 January 2012

Academic Editor: Habib Zaidi

Copyright © 2012 J. Cabello et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. E. Ü. Mumcuoǧlu, R. M. Leahy, and S. R. Cherry, “Bayesian reconstruction of PET images: methodology and performance analysis,” Physics in Medicine and Biology, vol. 41, no. 9, pp. 1777–1807, 1996. View at Publisher · View at Google Scholar · View at Scopus
  2. J. Qi and R. M. Leahy, “Iterative reconstruction techniques in emission computed tomography,” Physics in Medicine and Biology, vol. 51, no. 15, pp. R541–R578, 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. R. Lecomte, “Technology challenges in small animal PET imaging,” Nuclear Instruments and Methods in Physics Research A, vol. 527, no. 1-2, pp. 157–165, 2004. View at Publisher · View at Google Scholar · View at Scopus
  4. V. Y. Panin, F. Kehren, C. Michel, and M. Casey, “Fully 3-D PET reconstruction with system matrix derived from point source measurements,” IEEE Transactions on Medical Imaging, vol. 25, no. 7, Article ID 1644806, pp. 907–921, 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. A. M. Alessio, C. W. Stearns, S. Tong et al., “Application and evaluation of a measured spatially variant system model for PET image reconstruction,” IEEE Transactions on Medical Imaging, vol. 29, no. 3, pp. 938–949, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. F. A. Kotasidis, J. C. Matthews, G. I. Angelis et al., “Single scan parameterization of space-variant point spread functions in image space via a printed array: the impact for two PET/CT scanners,” Physics in Medicine and Biology, vol. 56, no. 10, pp. 2917–2942, 2011. View at Publisher · View at Google Scholar
  7. S. Moehrs, M. Defrise, N. Belcari et al., “Multi-ray-based system matrix generation for 3D PET reconstruction,” Physics in Medicine and Biology, vol. 53, no. 23, pp. 6925–6945, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. P. Aguiar, M. Rafecas, J. E. Ortuo et al., “Geometrical and Monte Carlo projectors in 3D PET reconstruction,” Medical Physics, vol. 37, no. 11, pp. 5691–5702, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Rafecas, B. Mosler, M. Dietz et al., “Use of a monte carlo-based probability matrix for 3-D iterative reconstruction of MADPET-II data,” IEEE Transactions on Nuclear Science, vol. 51, no. 5, pp. 2597–2605, 2004. View at Publisher · View at Google Scholar · View at Scopus
  10. J. L. Herraiz, S. España, J. J. Vaquero, M. Desco, and J. M. Udías, “FIRST: Fast Iterative Reconstruction Software for (PET) tomography,” Physics in Medicine and Biology, vol. 51, no. 18, pp. 4547–4565, 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. L. Zhang, S. Staelens, R. Van Holen et al., “Fast and memory-efficient Monte Carlo-based image reconstruction for whole-body PET,” Medical Physics, vol. 37, no. 7, pp. 3667–3676, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. C. Mora and M. Rafecas, “Polar pixels for high resolution small animal PET,” in IEEE Nuclear Science Symposium Conference Record, vol. 5, pp. 2812–2817, October 2007. View at Publisher · View at Google Scholar
  13. R. Ansorge, “List mode 3D PET reconstruction using an exact system matrix and polar voxels,” in IEEE Nuclear Science Symposium and Medical Imaging Conference, vol. 5, pp. 3454–3457, October 2007. View at Publisher · View at Google Scholar
  14. J. J. Scheins, H. Herzog, and N. J. Shah, “Fully-3D PET image reconstruction using scanner-independent, adaptive projection data and highly rotation-symmetric voxel assemblies,” IEEE Transactions on Medical Imaging, vol. 30, no. 3, pp. 879–892, 2011. View at Publisher · View at Google Scholar
  15. J. Cabello and M. Rafecas, “Comparison of basis functions for 3D PET reconstruction using a Monte Carlo system matrix,” Physics in Medicine and Biology, vol. 57, no. 7, pp. 1759–1777, 2012. View at Google Scholar
  16. J. Cabello, J. F. Oliver, I. Torres-Espallardo, and M. Rafecas, “Polar voxelization schemes combined with a Monte-Carlo based system matrix for image reconstruction in high resolution PET,” in IEEE Nuclear Science Symposium, Medical Imaging Conference, pp. 3256–3261, October 2010. View at Publisher · View at Google Scholar
  17. R. M. Lewitt, “Multidimensional digital image representations using generalized Kaiser-Bessel window functions,” Journal of the Optical Society of America A, vol. 7, no. 10, pp. 1834–1846, 1990. View at Google Scholar · View at Scopus
  18. M. E. Daube-Witherspoon, S. Matej, J. S. Karp, and R. M. Lewitt, “Application of the row action maximum likelihood algorithm with spherical basis functions to clinical PET imaging,” IEEE Transactions on Nuclear Science, vol. 48, no. 1, pp. 24–30, 2001. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Yendiki and J. A. Fessler, “A comparison of rotation- and blob-based system models for 3D SPECT with depth-dependent detector response,” Physics in Medicine and Biology, vol. 49, no. 11, pp. 2157–2165, 2004. View at Publisher · View at Google Scholar · View at Scopus
  20. A. Andreyev, M. Defrise, and C. Vanhove, “Pinhole SPECT reconstruction using blobs and resolution recovery,” IEEE Transactions on Nuclear Science, vol. 53, no. 5, Article ID 1710261, pp. 2719–2728, 2006. View at Publisher · View at Google Scholar · View at Scopus
  21. A. Andreyev, A. Sitek, and A. Celler, “Acceleration of blob-based iterative reconstruction algorithm using tesla GPU,” in IEEE Nuclear Science Symposium Conference Record (NSS/MIC '09), pp. 4095–4098, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  22. J. L. Herraiz, S. España, R. Cabido et al., “GPU-based fast iterative reconstruction of fully 3-D PET Sinograms,” IEEE Transactions on Nuclear Science, vol. 58, no. 5, pp. 2257–2263, 2011. View at Publisher · View at Google Scholar
  23. J. Zhou and J. Qi, “Fast and efficient fully 3D PET image reconstruction using sparse system matrix factorization with GPU acceleration,” Physics in Medicine and Biology, vol. 56, no. 20, pp. 6739–6757, 2011. View at Publisher · View at Google Scholar
  24. J.-Y. Cui, G. Pratx, S. Prevrhal, and C. S. Levin, “Fully 3D list-mode time-of-flight PET image reconstruction on GPUs using CUDA,” Medical Physics, vol. 38, no. 12, pp. 6775–6786, 2011. View at Publisher · View at Google Scholar
  25. G. Pratx and C. Levin, “Online detector response calculations for high-resolution PET image reconstruction,” Physics in Medicine and Biology, vol. 56, no. 13, pp. 4023–4040, 2011. View at Publisher · View at Google Scholar
  26. D. P. McElroy, W. Pimpl, B. J. Pichler, M. Rafecas, T. Schüler, and S. I. Ziegler, “Characterization and readout of MADPET-II detector modules: validation of a unique design concept for high resolution small animal PET,” IEEE Transactions on Nuclear Science, vol. 52, no. 1, pp. 199–204, 2005. View at Publisher · View at Google Scholar · View at Scopus
  27. S. Jan, G. Santin, D. Strul et al., “GATE: a simulation toolkit for PET and SPECT,” Physics in Medicine and Biology, vol. 49, no. 19, pp. 4543–4561, 2004. View at Publisher · View at Google Scholar · View at Scopus
  28. S. Matej and R. M. Lewitt, “Practical considerations for 3-D image reconstruction using spherically symmetric volume elements,” IEEE Transactions on Medical Imaging, vol. 15, no. 1, pp. 68–78, 1996. View at Google Scholar · View at Scopus
  29. D. J. Kadrmas, “LOR-OSEM: statistical PET reconstruction from raw line-of-response histograms,” Physics in Medicine and Biology, vol. 49, no. 20, pp. 4731–4744, 2004. View at Publisher · View at Google Scholar · View at Scopus
  30. M. S. Tohme and J. Qi, “Iterative reconstruction of Fourier-rebinned PET data using sinogram blurring function estimated from point source scans,” Medical Physics, vol. 37, no. 10, pp. 5530–5540, 2010. View at Publisher · View at Google Scholar · View at Scopus
  31. W. P. Segars, B. M. W. Tsui, E. C. Frey, G. A. Johnson, and S. S. Berr, “Development of a 4-digital mouse phantom for molecular imaging research,” Molecular Imaging and Biology, vol. 6, no. 3, pp. 149–159, 2004. View at Publisher · View at Google Scholar · View at Scopus
  32. X. Song, B. W. Pogue, S. Jiang et al., “Automated region detection based on the contrast-to-noise ratio in near-infrared tomography,” Applied Optics, vol. 43, no. 5, pp. 1053–1062, 2004. View at Google Scholar · View at Scopus
  33. J. Cabello, J. F. Oliver, and M. Rafecas, “Using spherical basis functions on a polar grid for iterative image reconstruction in small animal PET,” in Medical Imaging, vol. 7961 of Proceedings of SPIE, February 2011. View at Publisher · View at Google Scholar