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Journal of the Optical Society of America A

Journal of the Optical Society of America A


  • Editor: Franco Gori
  • Vol. 29, Iss. 1 — Jan. 1, 2012
  • pp: 153–163

Strategy of computed tomography sinogram inpainting based on sinusoid-like curve decomposition and eigenvector-guided interpolation

Yinsheng Li, Yang Chen, Yining Hu, Ahmed Oukili, Limin Luo, Wufan Chen, and Christine Toumoulin  »View Author Affiliations

JOSA A, Vol. 29, Issue 1, pp. 153-163 (2012)

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Projection incompleteness in x-ray computed tomography (CT) often relates to sparse sampling or detector gaps and leads to degraded reconstructions with severe streak and ring artifacts. To suppress these artifacts, this study develops a new sinogram inpainting strategy based on sinusoid-like curve decomposition and eigenvector-guided interpolation, where each missing sinogram point is considered located within a group of sinusoid-like curves and estimated from eigenvector-guided interpolation to preserve the sinogram texture continuity. The proposed approach is evaluated on real two-dimensional fan-beam CT data, for which the projection incompleteness, due to sparse sampling and symmetric detector gaps, is simulated. A Compute Unified Device Architecture (CUDA)-based parallelization is applied on the operations of sinusoid fittings and interpolations to accelerate the algorithm. A comparative study is then conducted to evaluate the proposed approach with two other inpainting methods and with a compressed sensing iterative reconstruction. Qualitative and quantitative performances demonstrate that the proposed approach can lead to efficient artifact suppression and less structure blurring.

© 2012 Optical Society of America

OCIS Codes
(100.6950) Image processing : Tomographic image processing
(110.3000) Imaging systems : Image quality assessment
(110.7440) Imaging systems : X-ray imaging

ToC Category:
Imaging Systems

Original Manuscript: August 30, 2011
Revised Manuscript: October 20, 2011
Manuscript Accepted: October 21, 2011
Published: December 23, 2011

Yinsheng Li, Yang Chen, Yining Hu, Ahmed Oukili, Limin Luo, Wufan Chen, and Christine Toumoulin, "Strategy of computed tomography sinogram inpainting based on sinusoid-like curve decomposition and eigenvector-guided interpolation," J. Opt. Soc. Am. A 29, 153-163 (2012)

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  1. A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging (IEEE, 1988), pp. 177–201.
  2. J. L. Prince and A. S. Willsky, “Constrained sinogram restoration for limited-angle tomography,” Opt. Eng. 29, 535–544(1990). [CrossRef]
  3. P. M. Joseph and R. A. Schulz, “View sampling requirements in fan beam computed tomography,” Med. Phys. 7, 692–702(1980). [CrossRef] [PubMed]
  4. B. Ohnesorge, T. Flohr, K. Schwarz, J. P. Heiken, and K. T. Bae, “Efficient correction for CT image artifacts caused by objects extending outside the scan field of view,” Med. Phys. 27, 39–46(2000). [CrossRef] [PubMed]
  5. J. S. Maltz, S. Bose, H. P. Shukla, and A. R. Bani-Hashemi, “CT truncation artifact removal using water-equivalent thicknesses derived from truncated projection data,” in Proceedings of IEEE Conference on Engineering Medicine and Biology (IEEE, 2007), pp. 2907–2911.
  6. J. Xu, K. Taguchi, and B. M. W. Tsui, “Statistical projection completion in x-ray CT using consistency conditions,” IEEE Trans. Med. Imaging 29, 1528–40 (2010). [CrossRef] [PubMed]
  7. T. F. Chan and J. Shen, “Mathematical models for local nontexture inpaintings,” SIAM J. Appl. Math. 62, 1019–1043(2002). [CrossRef]
  8. H. Xue, L. Zhang, Y. Xiao, Z. Chen, and Y. Xing, “Metal artifact reduction in dual energy CT by sinogram segmentation based on active contour model and TV inpainting,” in 2009 IEEE Nuclear Science Symposium Conference Record (NSS/MIC) (IEEE, 2009), pp. 904–908. [CrossRef]
  9. J. Gu, L. Zhang, G. Yu, Y. Xing, and Z. Chen, “X-ray CT metal artifacts reduction through curvature based sinogram inpainting,” J. X-Ray Sci. Technol. 14, 73–82 (2006).
  10. H. Kostler, M. Prummer, U. Rude, and J. Hornegger, “Adaptive variational sinogram interpolation of sparsely sampled CT data,” in Proceedings of the 18th International Conference on Pattern Recognition (IEEE, 2006), pp. 778–781.
  11. M. Bertram, J. Wiegert, D. Schafer, T. Aach, and G. Rose, “Directional view interpolation for compensation of sparse angular sampling in cone-beam CT,” IEEE Trans. Med. Imaging 28, 1011–1022 (2009). [CrossRef] [PubMed]
  12. E. P. A. Constantino and K. B. Ozanyan, “Sinogram recovery for sparse angle tomography using sinusoidal Hough transform,” Meas. Sci. Technol. 19, 094015 (2008). [CrossRef]
  13. A. Zamyatin and N. Satoru, “Extension of the reconstruction field of view and truncation correction using sinogram decomposition,” Med. Phys. 34, 1593–1605 (2007). [CrossRef] [PubMed]
  14. R. Chityala, K. R. Hoffmann, S. Rudin, and D. R. Bednarek, “Artifact reduction in truncated CT using sinogram completion,” Proc. SPIE 5747, 2110–2117 (2005). [CrossRef]
  15. M. Oehler and T. M. Buzug, “Statistical image reconstruction for inconsistent CT projection data,” Methods Inf. Med. 46, 261–269 (2007). [CrossRef] [PubMed]
  16. W. Zbijewski, M. Defrise, M. Viergever, and F. Beekman, “Statistical reconstruction for x-ray CT systems with non-continuous detectors,” Phys. Med. Biol. 52, 403–418 (2007). [CrossRef] [PubMed]
  17. C. Lemmens, D. Faul, and J. Nuyts, “Suppression of metal artifacts in CT using a reconstruction procedure that combines MAP and projection completion,” IEEE Trans. Med. Imaging 28, 250–260 (2009). [CrossRef] [PubMed]
  18. E. Y. Sidky and X. Pan, “Image reconstruction in circular cone-beam computed tomography by constrained total-variation minimization,” Phys. Med. Biol. 53, 4777–4807 (2008). [CrossRef] [PubMed]
  19. X. Duan, L. Zhang, Y. Xing, Z. Chen, and J. Cheng, “Few-view projection reconstruction with an iterative reconstruction re-projection algorithm a TV constraint,” IEEE Trans. Nucl. Sci. 56, 1377–1382 (2009). [CrossRef]
  20. L. Ritschl, F. Bergner, and M. Kachelriess, “A new approach to limited angle tomography using the compressed sensing framework,” Proc. SPIE 7622, 76222H (2010). [CrossRef]
  21. J. Tang, B. Nett, and G. Chen, “Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms,” Phys. Med. Biol. 54, 5781–5804 (2009). [CrossRef] [PubMed]
  22. A. Kharlamov and V. Podlozhnyuk, “Image denoising,” Tech. Rep. (NVIDIA, Inc., 2007).
  23. NVIDIA CUDA Programming Guide (Version 3.0).
  24. “Accelerating MATLAB with CUDA using MEX files” (White Paper), http://developer.nvidia.com/object/matlab cuda.html.
  25. “GPU Acceleration in MATLAB,” http://arch.eece.maine.edu/superme/images/8/8c/Final report.pdf.
  26. U. Kothe, “Edge and junction detection with an improved structure tensor,” Lect. Notes Comput. Sci. 2781, 25–32(2003). [CrossRef]
  27. D. Kincaid and W. Cheney, Numerical Analysis: Mathematics of Scientific Computing, 3rd ed. (American Mathematical Society, 2002).

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