A geometric projection-space reconstruction algorithm

Jerry L. Prince, Alan S. Willsky

Research output: Contribution to journalArticlepeer-review


We present a method to reconstruct images from finite sets of noisy projections that may be available only over limited or sparse angles. The algorithm calculates the maximum a posteriori (MAP) estimate of the full sinogram (which is an image of the 2-D Radon transform of the object) from the available data. It is implemented using a primal-dual constrained optimization procedure that solves a partial differential equation in the primal phase with an efficient local relaxation algorithm and uses a simple Lagrange-multiplier update in the dual phase. The sinogram prior probability is given by a Markov random field (MRF) that includes information about the mass, center of mass, and convex hull of the object, and about the smoothness, fundamental constraints, and periodicity of the 2-D Radon transform. The object is reconstructed using convolution backprojection applied to the estimated sinogram. We show several reconstructed objects which are obtained from simulated limited- and sparse-angle data using the described algorithm, and compare these results with images obtained using convolution backprojection directly.

Original languageEnglish (US)
Pages (from-to)151-191
Number of pages41
JournalLinear Algebra and Its Applications
Issue numberC
StatePublished - Mar 1990
Externally publishedYes

ASJC Scopus subject areas

  • Algebra and Number Theory
  • Numerical Analysis
  • Geometry and Topology
  • Discrete Mathematics and Combinatorics


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