### Abstract

In pt.I the authors derived a theoretical formulation for estimating the statistical properties of images reconstructed using the iterative maximum-likelihood expectation-maximization (ML-EM) algorithm. To gain insight into this complex problem, two levels of approximation were considered in the theory. These techniques revealed the dependence of the variance and covariance of the reconstructed image noise on the source distribution, imaging system transfer function, and iteration number. Here, a Monte Carlo approach was taken to study the noise properties of the ML-EM algorithm and to test the predictions of the theory. The study also served to evaluate the approximations used in the theory. Simulated data from phantoms were used in the Monte Carlo experiments. The ML-EM statistical properties were calculated from sample averages of a large number of images with different noise realizations. The agreement between the more exact form of the theoretical formulation and the Monte Carlo formulation was better than 10% in most cases examined, and for many situations the agreement was within the expected error of the Monte Carlo experiments. Results from the studies provide valuable information about the noise characteristics of ML-EM reconstructed images. Furthermore, the studies demonstrate the power of the theoretical and Monte Carlo approaches for investigating noise properties of statistical reconstruction algorithms.

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

Article number | 005 |

Pages (from-to) | 847-871 |

Number of pages | 25 |

Journal | Physics in Medicine and Biology |

Volume | 39 |

Issue number | 5 |

DOIs | |

State | Published - 1994 |

Externally published | Yes |

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### ASJC Scopus subject areas

- Radiological and Ultrasound Technology
- Radiology Nuclear Medicine and imaging
- Physics and Astronomy (miscellaneous)
- Biomedical Engineering

### Cite this

*Physics in Medicine and Biology*,

*39*(5), 847-871. [005]. https://doi.org/10.1088/0031-9155/39/5/005

**Noise properties of the EM algorithm. II. Monte Carlo simulations.** / Wilson, D. W.; Tsui, B. M W; Barrett, Harrison H.

Research output: Contribution to journal › Article

*Physics in Medicine and Biology*, vol. 39, no. 5, 005, pp. 847-871. https://doi.org/10.1088/0031-9155/39/5/005

}

TY - JOUR

T1 - Noise properties of the EM algorithm. II. Monte Carlo simulations

AU - Wilson, D. W.

AU - Tsui, B. M W

AU - Barrett, Harrison H

PY - 1994

Y1 - 1994

N2 - In pt.I the authors derived a theoretical formulation for estimating the statistical properties of images reconstructed using the iterative maximum-likelihood expectation-maximization (ML-EM) algorithm. To gain insight into this complex problem, two levels of approximation were considered in the theory. These techniques revealed the dependence of the variance and covariance of the reconstructed image noise on the source distribution, imaging system transfer function, and iteration number. Here, a Monte Carlo approach was taken to study the noise properties of the ML-EM algorithm and to test the predictions of the theory. The study also served to evaluate the approximations used in the theory. Simulated data from phantoms were used in the Monte Carlo experiments. The ML-EM statistical properties were calculated from sample averages of a large number of images with different noise realizations. The agreement between the more exact form of the theoretical formulation and the Monte Carlo formulation was better than 10% in most cases examined, and for many situations the agreement was within the expected error of the Monte Carlo experiments. Results from the studies provide valuable information about the noise characteristics of ML-EM reconstructed images. Furthermore, the studies demonstrate the power of the theoretical and Monte Carlo approaches for investigating noise properties of statistical reconstruction algorithms.

AB - In pt.I the authors derived a theoretical formulation for estimating the statistical properties of images reconstructed using the iterative maximum-likelihood expectation-maximization (ML-EM) algorithm. To gain insight into this complex problem, two levels of approximation were considered in the theory. These techniques revealed the dependence of the variance and covariance of the reconstructed image noise on the source distribution, imaging system transfer function, and iteration number. Here, a Monte Carlo approach was taken to study the noise properties of the ML-EM algorithm and to test the predictions of the theory. The study also served to evaluate the approximations used in the theory. Simulated data from phantoms were used in the Monte Carlo experiments. The ML-EM statistical properties were calculated from sample averages of a large number of images with different noise realizations. The agreement between the more exact form of the theoretical formulation and the Monte Carlo formulation was better than 10% in most cases examined, and for many situations the agreement was within the expected error of the Monte Carlo experiments. Results from the studies provide valuable information about the noise characteristics of ML-EM reconstructed images. Furthermore, the studies demonstrate the power of the theoretical and Monte Carlo approaches for investigating noise properties of statistical reconstruction algorithms.

UR - http://www.scopus.com/inward/record.url?scp=0028309681&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0028309681&partnerID=8YFLogxK

U2 - 10.1088/0031-9155/39/5/005

DO - 10.1088/0031-9155/39/5/005

M3 - Article

C2 - 15552089

AN - SCOPUS:0028309681

VL - 39

SP - 847

EP - 871

JO - Physics in Medicine and Biology

JF - Physics in Medicine and Biology

SN - 0031-9155

IS - 5

M1 - 005

ER -