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Image Reconstruction

  • Gopal B. Saha
Chapter

Abstract

Projection data acquired in two-dimensional (2D) mode or three-dimensional (3D) mode are stored in sinograms that consist of rows and columns representing angular and radial samplings, respectively. Acquired data in each row are compressed (summed) along the depth of the object and must be unfolded to provide information along this direction. Such unfolding is performed by reconstruction of images using acquired data. The 3D data are somewhat more complex than the 2D data and usually rebinned into 2D format for reconstruction. After correction for the factors discussed in Chap. 3, the data are used to reconstruct transaxial (transverse) images from which vertical long axis (coronal) and horizontal long axis (sagittal) images are formed. Reconstruction of images is made by two methods: filtered backprojection and iterative methods. Both methods are described below.

Keywords

Count Density Nyquist Frequency Recovery Coefficient Reconstruction Matrix Detector Pair 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References and Suggested Reading

  1. Bacharach SL (1995). Image analysis. In: Wagner HN Jr, Szabo Z, Buchanon JW (eds) Principles of nuclear medicine. W.B. Saunders, Philadelphia, PA, pp 393–404Google Scholar
  2. Cherry SR, Dahlbom M (2004). PET; Physics, instrumentation, and scanners. In: Phelps ME (ed) PET; Molecular imaging and its biological applications. Springer, New YorkGoogle Scholar
  3. Cherry SR, Sorensen JA, Phelps ME (2003). Physics in nuclear medicine, 3rd ed. W.B. Saunders, Philadelphia, PAGoogle Scholar
  4. Defrise M, Kinahan PE, Christian M (2003). Image reconstruction algorithms in PET. In: Valk PE, Bailey DL, Townsend DW, Maisey MN (eds) Positron emission tomography. Springer, New YorkGoogle Scholar
  5. Defrise M, Townsend DW, Clack R (1989). Three-dimension image reconstruction from complete projections. Phys Med Biol 34:573PubMedCrossRefGoogle Scholar
  6. Dreyer KJ, Mehta A, Thrall JH (2002). PACS: A guide to the digital revolution. Springer, New YorkGoogle Scholar
  7. Hoffman EJ, Phelps ME (1986). Positron emission tomography; principles and quantitation. In: Phelps ME, Mazziotta J, Schelbert H (eds) Positron emission tomography and autoradiography: principles and application for the brain and heart. Raven Press, New York, p 237Google Scholar
  8. Hudson HM, Larkin RS (1994). Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans Med Imaging 13:601PubMedCrossRefGoogle Scholar
  9. Shepp LA, Vardi Y (1982). Maximum likelihood reconstruction for emission tomography. IEEE Trans Med Imaging MI-1:113Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Gopal B. Saha
    • 1
  1. 1.Department of Nuclear MedicineThe Cleveland Clinic FoundationClevelandUSA

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