### Abstract

We describe a method for computing a continuous time estimate of dynamic changes in tracer density using list mode PET data. The tracer density in each voxel is modeled as an inhomogeneous Poisson process whose rate function can be represented using a cubic B-spline basis. An estimate of these rate functions is obtained by maximizing the likelihood of the arrival times of each detected photon pair over the control vertices of the spline. By resorting the list mode data into a standard sinogram plus a “timogram” that retains the arrival times of each of the events, we are able to perform efficient computation that exploits the symmetry inherent in the ordered sinogram. The maximum likelihood estimator uses quadratic temporal and spatial smoothness penalties and an additional penalty term to enforce non-negativity. Corrections for scatter and randoms are described and the results of studies using simulated and human data are included.

Original language | English (US) |
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Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

Publisher | Springer Verlag |

Pages | 98-111 |

Number of pages | 14 |

Volume | 1613 |

ISBN (Print) | 3540661670, 9783540661672 |

State | Published - 1999 |

Externally published | Yes |

Event | 16th International conference on Information Processing in Medical Imaging, IPMI 1999 - Visegrad, Hungary Duration: Jun 28 1999 → Jul 2 1999 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
---|---|

Volume | 1613 |

ISSN (Print) | 03029743 |

ISSN (Electronic) | 16113349 |

### Other

Other | 16th International conference on Information Processing in Medical Imaging, IPMI 1999 |
---|---|

Country | Hungary |

City | Visegrad |

Period | 6/28/99 → 7/2/99 |

### Fingerprint

### ASJC Scopus subject areas

- Computer Science(all)
- Theoretical Computer Science

### Cite this

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)*(Vol. 1613, pp. 98-111). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1613). Springer Verlag.

**Continuous time dynamic PET imaging using list mode data.** / Nichols, Thomas E.; Qi, Jinyi; Leahy, Richard M.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).*vol. 1613, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1613, Springer Verlag, pp. 98-111, 16th International conference on Information Processing in Medical Imaging, IPMI 1999, Visegrad, Hungary, 6/28/99.

}

TY - GEN

T1 - Continuous time dynamic PET imaging using list mode data

AU - Nichols, Thomas E.

AU - Qi, Jinyi

AU - Leahy, Richard M.

PY - 1999

Y1 - 1999

N2 - We describe a method for computing a continuous time estimate of dynamic changes in tracer density using list mode PET data. The tracer density in each voxel is modeled as an inhomogeneous Poisson process whose rate function can be represented using a cubic B-spline basis. An estimate of these rate functions is obtained by maximizing the likelihood of the arrival times of each detected photon pair over the control vertices of the spline. By resorting the list mode data into a standard sinogram plus a “timogram” that retains the arrival times of each of the events, we are able to perform efficient computation that exploits the symmetry inherent in the ordered sinogram. The maximum likelihood estimator uses quadratic temporal and spatial smoothness penalties and an additional penalty term to enforce non-negativity. Corrections for scatter and randoms are described and the results of studies using simulated and human data are included.

AB - We describe a method for computing a continuous time estimate of dynamic changes in tracer density using list mode PET data. The tracer density in each voxel is modeled as an inhomogeneous Poisson process whose rate function can be represented using a cubic B-spline basis. An estimate of these rate functions is obtained by maximizing the likelihood of the arrival times of each detected photon pair over the control vertices of the spline. By resorting the list mode data into a standard sinogram plus a “timogram” that retains the arrival times of each of the events, we are able to perform efficient computation that exploits the symmetry inherent in the ordered sinogram. The maximum likelihood estimator uses quadratic temporal and spatial smoothness penalties and an additional penalty term to enforce non-negativity. Corrections for scatter and randoms are described and the results of studies using simulated and human data are included.

UR - http://www.scopus.com/inward/record.url?scp=84947420493&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84947420493&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84947420493

SN - 3540661670

SN - 9783540661672

VL - 1613

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 98

EP - 111

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Verlag

ER -