## Abstract

Positron emission tomography is a medical imaging modality for producing 3D images of the spatial distribution of biochemical tracers within the human body. The images are reconstructed from data formed through detection of radiation resulting from the emission of positrons from radioisotopes tagged onto the tracer of interest. These measurements are approximate line integrals from which the image can be reconstructed using analytical inversion formulae. However these direct methods do not allow accurate modeling either of the detector system or of the inherent statistical fluctuations in the data. Here we review recent progress in developing statistical approaches to image estimation that can overcome these limitations. We describe the various components of the physical model and review different formulations of the inverse problem. The wide range of numerical procedures for solving these problems are then reviewed. Finally, we describe recent work aimed at quantifying the quality of the resulting images, both in terms of classical measures of estimator bias and variance, and also using measures that are of more direct clinical relevance.

Original language | English (US) |
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Pages (from-to) | 147-165 |

Number of pages | 19 |

Journal | Statistics and Computing |

Volume | 10 |

Issue number | 2 |

State | Published - 2000 |

Externally published | Yes |

## Keywords

- Bayesian imaging
- Computed tomography
- Image reconstruction
- Maximum likelihood estimation
- Positron emission tomography

## ASJC Scopus subject areas

- Computational Theory and Mathematics
- Statistics and Probability
- Theoretical Computer Science