## Abstract

We consider electronic noise modeling in tomographic image reconstruction when the measured signal is the sum of a Gaussian distributed electronic noise component and another random variable whose log-likelihood function satisfies a certain linearity condition. Examples of such likelihood functions include the Poisson distribution and an exponential dispersion (ED) model that can approximate the signal statistics in integration mode X-ray detectors. We formulate the image reconstruction problem as a maximum-likelihood estimation problem. Using an expectation-maximization approach, we demonstrate that a reconstruction algorithm can be obtained following a simple substitution rule from the one previously derived without electronic noise considerations. To illustrate the applicability of the substitution rule, we present examples of a fully iterative reconstruction algorithm and a sinogram smoothing algorithm both in transmission CT reconstruction when the measured signal contains additive electronic noise. Our simulation studies show the potential usefulness of accurate electronic noise modeling in low-dose CT applications.

Original language | English (US) |
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Pages (from-to) | 1228-1238 |

Number of pages | 11 |

Journal | IEEE Transactions on Image Processing |

Volume | 18 |

Issue number | 6 |

DOIs | |

State | Published - 2009 |

## Keywords

- Compound Poisson distribution
- Electronic noise
- Low dose X-ray CT
- Sinogram restoration
- Statistical image reconstruction

## ASJC Scopus subject areas

- Software
- Computer Graphics and Computer-Aided Design