### Abstract

The expectation-maximization maximum-likelihood (EM-ML) algorithm for image reconstruction in positron emission tomography (PET) essentially solves a large linear system of equations. In this paper, we study computational aspects of a recently developed preprocessing scheme for focusing the attention, and thus the computational resources, on a subset of the equations and unknowns in order to reduce the storage, computation, and communication requirements of the EM-ML algorithm. The approach is completely data-driven and uses no prior anatomic knowledge. The experimental results are obtained from runs on a small network of workstations using simulated phantom data as well as data obtained from a clinical ECAT 921 PET scanner.

Original language | English (US) |
---|---|

Pages (from-to) | 1481-1497 |

Number of pages | 17 |

Journal | Parallel Computing |

Volume | 24 |

Issue number | 9-10 |

State | Published - Sep 1 1998 |

Externally published | Yes |

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### All Science Journal Classification (ASJC) codes

- Software
- Theoretical Computer Science
- Hardware and Architecture
- Computer Networks and Communications
- Computer Graphics and Computer-Aided Design
- Artificial Intelligence

### Cite this

*Parallel Computing*,

*24*(9-10), 1481-1497.

**A computational study of the focus-of-attention EM-ML algorithm for PET reconstruction.** / Gregor, Jens; Huff, Dean A.

Research output: Contribution to journal › Article

*Parallel Computing*, vol. 24, no. 9-10, pp. 1481-1497.

}

TY - JOUR

T1 - A computational study of the focus-of-attention EM-ML algorithm for PET reconstruction

AU - Gregor, Jens

AU - Huff, Dean A.

PY - 1998/9/1

Y1 - 1998/9/1

N2 - The expectation-maximization maximum-likelihood (EM-ML) algorithm for image reconstruction in positron emission tomography (PET) essentially solves a large linear system of equations. In this paper, we study computational aspects of a recently developed preprocessing scheme for focusing the attention, and thus the computational resources, on a subset of the equations and unknowns in order to reduce the storage, computation, and communication requirements of the EM-ML algorithm. The approach is completely data-driven and uses no prior anatomic knowledge. The experimental results are obtained from runs on a small network of workstations using simulated phantom data as well as data obtained from a clinical ECAT 921 PET scanner.

AB - The expectation-maximization maximum-likelihood (EM-ML) algorithm for image reconstruction in positron emission tomography (PET) essentially solves a large linear system of equations. In this paper, we study computational aspects of a recently developed preprocessing scheme for focusing the attention, and thus the computational resources, on a subset of the equations and unknowns in order to reduce the storage, computation, and communication requirements of the EM-ML algorithm. The approach is completely data-driven and uses no prior anatomic knowledge. The experimental results are obtained from runs on a small network of workstations using simulated phantom data as well as data obtained from a clinical ECAT 921 PET scanner.

UR - http://www.scopus.com/inward/record.url?scp=0032157682&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0032157682&partnerID=8YFLogxK

M3 - Article

VL - 24

SP - 1481

EP - 1497

JO - Parallel Computing

JF - Parallel Computing

SN - 0167-8191

IS - 9-10

ER -