## Abstract

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(N^{2} 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 language | English (US) |
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Pages (from-to) | 2295-2298 |

Number of pages | 4 |

Journal | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |

Volume | 4 |

State | Published - 1995 |

## ASJC Scopus subject areas

- Software
- Signal Processing
- Electrical and Electronic Engineering