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Sampling Theory and Parallel-Beam Tomography

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Sampling, Wavelets, and Tomography

Part of the book series: Applied and Numerical Harmonic Analysis ((ANHA))

Abstract

This article contains a tutorial on the interaction between sampling theory and tomography as well as some new results. We explore how sampling theorems are used in tomography to identify efficient data acquisition schemes, facilitate an error analysis for reconstruction algorithms, and provide a qualitative understanding of some image artifacts. In turn, applications in tomography have stimulated research on new estimates for the aliasing error and in non-uniform sampling theory. New results are included in an analysis of artifacts caused by undersampling.

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References

  1. M. Abramowitz and I. A. StegunHandbook of Mathematical FunctionsU.S. Dept. of Commerce, Washington, DC, 1972.

    Google Scholar 

  2. H. Behmard and A. Faridani, Sampling of bandlimited functions on unions of shifted latticesJ. Fourier Anal. Appl.8:43–58, 2002.

    Article  MathSciNet  MATH  Google Scholar 

  3. A.M. Cormack, Sampling the Radon transform with beams of finite widthPhys. Med. Biol.23:1141–1148, 1978.

    Article  Google Scholar 

  4. L. Desbat, Efficient sampling on coarse grids in tomographyInverse Problems9:251–269, 1993.

    Article  MathSciNet  MATH  Google Scholar 

  5. L. Desbat, Echantillonnage paralléle efficace en tomographie 3DCR. Acad. Sci. ParisSerie I,324:1193–1199, 1997.

    MathSciNet  MATH  Google Scholar 

  6. L. Desbat, Interpolation of lacking data in tomographyConference on Sampling Theory and Applications Proceedings of Samp T Intern. Proceedings of Samp T InternMay 2001Orlando, FL, pp. 123–128,2001.

    Google Scholar 

  7. A. Faridani, An application of a multidimensional sampling theorem to computed tomographyContemporary Mathematics113:65–80, 1990.

    Article  MathSciNet  Google Scholar 

  8. A. Faridani, Reconstructing from efficiently sampled data in parallel-beam computed tomographyInverse Problems and ImagingG. F. Roach (ed.), Pitman Research Notes in Mathematics Series, Vol. 245, Longman, 1991, pp. 68–102.

    Google Scholar 

  9. A. Faridani A generalized sampling theorem for locally compact abelian groupsMath. Com.63:307–327, 1994.

    Article  MathSciNet  MATH  Google Scholar 

  10. A. Faridani, Results, old and new, in computed tomographyInverse Problems in Wave PropagationG. Chavent et al. (editors), The IMA Volumes in Mathematics and its Applications, Vol. 90, Springer-Verlag, New York, 1997, pp. 167–193.

    Chapter  Google Scholar 

  11. A. Faridani, Sampling in parallel-beam tomography, Inverse Problems, Tomography, and Image Processing, A. G. Ramm (editor), Plenum, New York, 1998, pp. 33–53.

    Google Scholar 

  12. A. Faridani and E. L. Ritman, High-resolution computed tomography from efficient samplingInverse Problems16:635–650,2000.

    Article  MathSciNet  MATH  Google Scholar 

  13. A. Faridani, K. A. Buglione, P. Huabsomboon, O. D. Iancu, and J. McGrath, Introduction to local tomographyContemporary Mathematics278:29–47, 2001.

    Article  MathSciNet  Google Scholar 

  14. G. B. FollandReal AnalysisWiley, New York, 1984.

    MATH  Google Scholar 

  15. K. Gröchenig, Aspects of Gabor analysis on locally compact abelian groupsGabor Analysis and AlgorithmsH. G. Feichtinger and T. Strohmer (editors), Birkhäuser, Boston, MA, 1998, pp. 211–231.

    Chapter  Google Scholar 

  16. H. Kruse, Resolution of reconstruction methods in computerized tomographySIAM J. Sci. Stat. Comput.10:447–474, 1989.

    Article  MathSciNet  MATH  Google Scholar 

  17. A. G. Lindgren and P. A. Rattey, The inverse discrete Radon transform with applications to tomographic imaging using projection dataAdvances in Electronics and Electron Physics56:359–410, 1981.

    Article  Google Scholar 

  18. F. NattererThe Mathematics of Computerized TomographyWiley, New York, 1986.

    MATH  Google Scholar 

  19. F. Natterer, Sampling in fan-beam tomographySIAM J. Appl. Math.53:358–380, 1993.

    Article  MathSciNet  MATH  Google Scholar 

  20. F. Natterer, Recent developments in x-ray tomographyTomography, Impedance Imag­ing, and Integral GeometryE. T; Quinto, M. Cheney, and P. Kuchment (eds.), Lectures in Applied Mathematics, Vol. 30, Amer. Math. Soc, Providence, RI, 1994, pp. 177–198.

    Google Scholar 

  21. F. Natterer, Resolution and reconstruction for a helical CT scannerTechnical Report 20/96-NMathematics Dept., University of MĂĽnster, Germany.

    Google Scholar 

  22. F. Natterer and F. WuebbelingMathematical Methods in Image ReconstructionSIAM, Philadelphia, 2001.

    Book  MATH  Google Scholar 

  23. D. P. Petersen and D. Middleton, Sampling and reconstruction of wave-number-limited functions in N-dimensional euclidean space.Inf. Control5:279–323, 1962.

    Article  MathSciNet  Google Scholar 

  24. V. P. Palamodov, Localization of harmonic decomposition of the Radon transformIn­verse Problems11:1025–1030, 1995.

    Article  MathSciNet  MATH  Google Scholar 

  25. P. A. Rattey and A. G. Lindgren, Sampling the 2-D Radon transformIEEE Trans. Acoust. Speech Signal Processing29:994–1002, 1981.

    Article  MathSciNet  Google Scholar 

  26. A. Rieder and A. Faridani, The semidiscrete filtered backprojection algorithm is optimal for tomographic inversionSIAM J. Num. Anal.41:869–892, 2003.

    Article  MathSciNet  MATH  Google Scholar 

  27. S. W. Rowland, Computer implementation of image reconstruction formulas, Image Reconstruction from Projections: Implementation and Applications, G. T. Herman (ed.), Springer, Berlin, 1979.

    Google Scholar 

  28. D. Walnut, Nonperiodic sampling of bandlimited functions on unions of rectangular latticesJ. Fourier Anal. Appl.2:435–452, 1996.

    Article  MathSciNet  MATH  Google Scholar 

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Faridani, A. (2004). Sampling Theory and Parallel-Beam Tomography. In: Benedetto, J.J., Zayed, A.I. (eds) Sampling, Wavelets, and Tomography. Applied and Numerical Harmonic Analysis. Birkhäuser, Boston, MA. https://doi.org/10.1007/978-0-8176-8212-5_9

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  • DOI: https://doi.org/10.1007/978-0-8176-8212-5_9

  • Publisher Name: Birkhäuser, Boston, MA

  • Print ISBN: 978-1-4612-6495-8

  • Online ISBN: 978-0-8176-8212-5

  • eBook Packages: Springer Book Archive

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