Accurate estimation of the Fisher information matrix for the PET image reconstruction problem

Quanzheng Li; Evren Asma; Jinyi Qi; James R. Bading; Richard M. Leahy

Accurate estimation of the Fisher information matrix for the PET image reconstruction problem

Quanzheng Li, Evren Asma, Jinyi Qi, James R. Bading, Richard M. Leahy

Biomedical Engineering

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

Abstract

The Fisher Information Matrix (FIM) plays a key role in the analysis and applications of statistical image recon-struction methods based on Poisson data models. The elements of the FIM are a function of the reciprocal of the mean values of sinogram elements. Conventional plug-in FIM estimation methods do not work well at low counts, where the FIM estimate is highly sensitive to the reciprocal mean estimates at individual detector pairs. A generalized error look-up table (GELT) method is developed to estimate the reciprocal of the mean of the sinogram data, which achieves a bias of less than 2% for mean sinogram values greater than 0.7. This approach is also extended to randoms precorrected data. Based on these techniques, an accurate FIM estimate is obtained for both Poisson and randoms precorrected data. As an application, the new GELT method is used to improve resolution uniformity and achieve near-uniform reconstructed image resolution in both high and low count situations.

Original language	English (US)
Title of host publication	IEEE Nuclear Science Symposium Conference Record
Editors	S.D. Metzler
Pages	2012-2016
Number of pages	5
Volume	3
Publication status	Published - 2003
Event	2003 IEEE Nuclear Science Symposium Conference Record - Nuclear Science Symposium, Medical Imaging Conference - Portland, OR, United States Duration: Oct 19 2003 → Oct 25 2003

Other

Other	2003 IEEE Nuclear Science Symposium Conference Record - Nuclear Science Symposium, Medical Imaging Conference
Country	United States
City	Portland, OR
Period	10/19/03 → 10/25/03

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Keywords

Fisher Information Matrix
Hyperparameters
Uniform Resolution

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Industrial and Manufacturing Engineering

Cite this

Li, Q., Asma, E., Qi, J., Bading, J. R., & Leahy, R. M. (2003). Accurate estimation of the Fisher information matrix for the PET image reconstruction problem. In S. D. Metzler (Ed.), IEEE Nuclear Science Symposium Conference Record (Vol. 3, pp. 2012-2016). [M5-3]

Li, Q, Asma, E, Qi, J, Bading, JR & Leahy, RM 2003, Accurate estimation of the Fisher information matrix for the PET image reconstruction problem. in SD Metzler (ed.), IEEE Nuclear Science Symposium Conference Record. vol. 3, M5-3, pp. 2012-2016, 2003 IEEE Nuclear Science Symposium Conference Record - Nuclear Science Symposium, Medical Imaging Conference, Portland, OR, United States, 10/19/03.

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title = "Accurate estimation of the Fisher information matrix for the PET image reconstruction problem",

abstract = "The Fisher Information Matrix (FIM) plays a key role in the analysis and applications of statistical image recon-struction methods based on Poisson data models. The elements of the FIM are a function of the reciprocal of the mean values of sinogram elements. Conventional plug-in FIM estimation methods do not work well at low counts, where the FIM estimate is highly sensitive to the reciprocal mean estimates at individual detector pairs. A generalized error look-up table (GELT) method is developed to estimate the reciprocal of the mean of the sinogram data, which achieves a bias of less than 2{\%} for mean sinogram values greater than 0.7. This approach is also extended to randoms precorrected data. Based on these techniques, an accurate FIM estimate is obtained for both Poisson and randoms precorrected data. As an application, the new GELT method is used to improve resolution uniformity and achieve near-uniform reconstructed image resolution in both high and low count situations.",

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N2 - The Fisher Information Matrix (FIM) plays a key role in the analysis and applications of statistical image recon-struction methods based on Poisson data models. The elements of the FIM are a function of the reciprocal of the mean values of sinogram elements. Conventional plug-in FIM estimation methods do not work well at low counts, where the FIM estimate is highly sensitive to the reciprocal mean estimates at individual detector pairs. A generalized error look-up table (GELT) method is developed to estimate the reciprocal of the mean of the sinogram data, which achieves a bias of less than 2% for mean sinogram values greater than 0.7. This approach is also extended to randoms precorrected data. Based on these techniques, an accurate FIM estimate is obtained for both Poisson and randoms precorrected data. As an application, the new GELT method is used to improve resolution uniformity and achieve near-uniform reconstructed image resolution in both high and low count situations.

AB - The Fisher Information Matrix (FIM) plays a key role in the analysis and applications of statistical image recon-struction methods based on Poisson data models. The elements of the FIM are a function of the reciprocal of the mean values of sinogram elements. Conventional plug-in FIM estimation methods do not work well at low counts, where the FIM estimate is highly sensitive to the reciprocal mean estimates at individual detector pairs. A generalized error look-up table (GELT) method is developed to estimate the reciprocal of the mean of the sinogram data, which achieves a bias of less than 2% for mean sinogram values greater than 0.7. This approach is also extended to randoms precorrected data. Based on these techniques, an accurate FIM estimate is obtained for both Poisson and randoms precorrected data. As an application, the new GELT method is used to improve resolution uniformity and achieve near-uniform reconstructed image resolution in both high and low count situations.

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KW - Hyperparameters

KW - Uniform Resolution

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Y2 - 19 October 2003 through 25 October 2003

ER -

Accurate estimation of the Fisher information matrix for the PET image reconstruction problem

Abstract

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Keywords

ASJC Scopus subject areas

Cite this

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