Fast iterative tomographic reconstruction algorithm

Alexander H. Delaney, Yoram Bresler

Research output: Contribution to journalConference articlepeer-review


We use a series-expansion approach and an operator framework to derive a new, fast and accurate, iterative tomographic reconstruction algorithm applicable for parallel-ray projections that have been collected at a finite number of arbitrary view angles and have been radially sampled at a rate high enough so that aliasing errors are small. We use the conjugate gradient algorithm to minimize a regularized least squares criterion, and we prove that the main step in each iteration is equivalent to a 2-D discrete convolution, which can be cheaply and exactly implemented via the FFT. The proposed algorithm requires qq(N2 log N) multiplies per iteration to reconstruct an N × N image from P view angles, and requires the storage of half of a 2N × 2N PSF.

Original languageEnglish (US)
Pages (from-to)2295-2298
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - 1995
EventProceedings of the 1995 20th International Conference on Acoustics, Speech, and Signal Processing. Part 2 (of 5) - Detroit, MI, USA
Duration: May 9 1995May 12 1995

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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