• Record: found
  • Abstract: found
  • Article: not found

Interior tomography with continuous singular value decomposition.

Read this article at

      There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.


      The long-standing interior problem has important mathematical and practical implications. The recently developed interior tomography methods have produced encouraging results. A particular scenario for theoretically exact interior reconstruction from truncated projections is that there is a known sub-region in the ROI. In this paper, we improve a novel continuous singular value decomposition (SVD) method for interior reconstruction assuming a known sub-region. First, two sets of orthogonal eigen-functions are calculated for the Hilbert and image spaces respectively. Then, after the interior Hilbert data are calculated from projection data through the ROI, they are projected onto the eigen-functions in the Hilbert space, and an interior image is recovered by a linear combination of the eigen-functions with the resulting coefficients. Finally, the interior image is compensated for the ambiguity due to the null space utilizing the prior sub-region knowledge. Experiments with simulated and real data demonstrate the advantages of our approach relative to the POCS type interior reconstructions.

      Related collections

      Author and article information

      IEEE Trans Med Imaging
      IEEE transactions on medical imaging
      Institute of Electrical and Electronics Engineers (IEEE)
      Nov 2012
      : 31
      : 11
      22907966 10.1109/TMI.2012.2213304 3773972 NIHMS509580


      Comment on this article