### Abstract

The Center for Gamma-Ray Imaging is developing a number of small-animal SPECT imaging systems. These systems consist of multiple stationary detectors, each of which has its own multiple-pinhole collimator. The location of the pinhole plates (i.e., magnification), the number of pinholes within each plate, as well the pinhole locations are all adjustable. The performance of the Bayesian ideal observer sets the upper limit on task performance and can be used to optimize imaging hardware, such as pinhole configurations. Markov-chain Monte Carlo techniques have been developed to compute the ideal observer but require complete knowledge of the statistics of both the imaging system (such as the noise) and the class of random objects being imaged, in addition to an accurate forward model connecting the object to the image. Ideal observer computations using Monte Carlo techniques are burdensome because the forward model must be simulated millions of times for each imaging system. We present an efficient technique for computing the Bayesian ideal observer for multiple-pinhole, small-animal SPECT systems that accounts for both the finite-size of the pinholes and the stochastic nature of the objects being imaged. This technique relies on an efficient, radiometrically correct forward model that maps an object to an image in less than 20 milliseconds. An analysis of the error of the forward model, as well as the results of a ROC study using the ideal observer test statistic is presented.

Original language | English (US) |
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Title of host publication | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |

Editors | M.P. Eckstein, Y. Jiang |

Pages | 118-127 |

Number of pages | 10 |

Volume | 5749 |

DOIs | |

State | Published - 2005 |

Event | Medical Imaging 2005 - Image Perception, Observer Performance, and Technology Assessment - San Diego, CA, United States Duration: Feb 15 2005 → Feb 17 2005 |

### Other

Other | Medical Imaging 2005 - Image Perception, Observer Performance, and Technology Assessment |
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Country | United States |

City | San Diego, CA |

Period | 2/15/05 → 2/17/05 |

### Fingerprint

### Keywords

- Hardware Optimization
- Ideal Observer
- Lumpy Object
- Markov-Chain Monte Carlo
- SPECT

### ASJC Scopus subject areas

- Engineering(all)
- Applied Mathematics
- Computer Science Applications
- Electrical and Electronic Engineering
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics

### Cite this

*Progress in Biomedical Optics and Imaging - Proceedings of SPIE*(Vol. 5749, pp. 118-127). [15] https://doi.org/10.1117/12.595605

**A fast model of a multiple-pinhole SPECT imaging system.** / Gross, Kevin; Kupinski, Matthew A; Hesterman, Jacob Y.

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

*Progress in Biomedical Optics and Imaging - Proceedings of SPIE.*vol. 5749, 15, pp. 118-127, Medical Imaging 2005 - Image Perception, Observer Performance, and Technology Assessment, San Diego, CA, United States, 2/15/05. https://doi.org/10.1117/12.595605

}

TY - GEN

T1 - A fast model of a multiple-pinhole SPECT imaging system

AU - Gross, Kevin

AU - Kupinski, Matthew A

AU - Hesterman, Jacob Y.

PY - 2005

Y1 - 2005

N2 - The Center for Gamma-Ray Imaging is developing a number of small-animal SPECT imaging systems. These systems consist of multiple stationary detectors, each of which has its own multiple-pinhole collimator. The location of the pinhole plates (i.e., magnification), the number of pinholes within each plate, as well the pinhole locations are all adjustable. The performance of the Bayesian ideal observer sets the upper limit on task performance and can be used to optimize imaging hardware, such as pinhole configurations. Markov-chain Monte Carlo techniques have been developed to compute the ideal observer but require complete knowledge of the statistics of both the imaging system (such as the noise) and the class of random objects being imaged, in addition to an accurate forward model connecting the object to the image. Ideal observer computations using Monte Carlo techniques are burdensome because the forward model must be simulated millions of times for each imaging system. We present an efficient technique for computing the Bayesian ideal observer for multiple-pinhole, small-animal SPECT systems that accounts for both the finite-size of the pinholes and the stochastic nature of the objects being imaged. This technique relies on an efficient, radiometrically correct forward model that maps an object to an image in less than 20 milliseconds. An analysis of the error of the forward model, as well as the results of a ROC study using the ideal observer test statistic is presented.

AB - The Center for Gamma-Ray Imaging is developing a number of small-animal SPECT imaging systems. These systems consist of multiple stationary detectors, each of which has its own multiple-pinhole collimator. The location of the pinhole plates (i.e., magnification), the number of pinholes within each plate, as well the pinhole locations are all adjustable. The performance of the Bayesian ideal observer sets the upper limit on task performance and can be used to optimize imaging hardware, such as pinhole configurations. Markov-chain Monte Carlo techniques have been developed to compute the ideal observer but require complete knowledge of the statistics of both the imaging system (such as the noise) and the class of random objects being imaged, in addition to an accurate forward model connecting the object to the image. Ideal observer computations using Monte Carlo techniques are burdensome because the forward model must be simulated millions of times for each imaging system. We present an efficient technique for computing the Bayesian ideal observer for multiple-pinhole, small-animal SPECT systems that accounts for both the finite-size of the pinholes and the stochastic nature of the objects being imaged. This technique relies on an efficient, radiometrically correct forward model that maps an object to an image in less than 20 milliseconds. An analysis of the error of the forward model, as well as the results of a ROC study using the ideal observer test statistic is presented.

KW - Hardware Optimization

KW - Ideal Observer

KW - Lumpy Object

KW - Markov-Chain Monte Carlo

KW - SPECT

UR - http://www.scopus.com/inward/record.url?scp=24644433887&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=24644433887&partnerID=8YFLogxK

U2 - 10.1117/12.595605

DO - 10.1117/12.595605

M3 - Conference contribution

AN - SCOPUS:24644433887

VL - 5749

SP - 118

EP - 127

BT - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

A2 - Eckstein, M.P.

A2 - Jiang, Y.

ER -