FAST GRADIENT-BASED METHODS FOR BAYESIAN RECONSTRUCTION OF TRANSMISSION AND EMISSION PET IMAGES

Date

1994-12-01

Author

Mumcuoğlu, Ünal Erkan
CHERRY, SR
ZHOU, ZHENYU

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We describe conjugate gradient algorithms for reconstruction of transmission and emission PET images. The reconstructions are based on a Bayesian formulation, where the data are modeled as a collection of independent Poisson random variables and the image is modeled using a Markov random field. A conjugate gradient algorithm is used to compute a maximum a posteriori (MAP) estimate of the image by maximizing over the posterior density. To ensure nonnegativity of the solution, a penalty function is used to convert the problem to one of unconstrained optimization. Preconditioners are used to enhance convergence rates, These methods generally achieve effective convergence in 15-25 iterations. Reconstructions are presented of an (18)FDG whole body scan from data collected using a Siemens/CTI ECAT931 whole body system. These results indicate significant improvements in emission image quality using the Bayesian approach, in comparison to filtered backprojection, particularly when reprojections of the MAP transmission image are used in place of the standard attenuation correction factors.

Subject Keywords

Attenuation, Tomography, Em Algorithm, Maximum-Likelihood-Estimation

URI

https://hdl.handle.net/11511/32094

Journal

IEEE TRANSACTIONS ON MEDICAL IMAGING

DOI

https://doi.org/10.1109/42.363099

Collections

Graduate School of Informatics, Article

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Citation Formats

Ü. E. Mumcuoğlu, S. CHERRY, and Z. ZHOU, “FAST GRADIENT-BASED METHODS FOR BAYESIAN RECONSTRUCTION OF TRANSMISSION AND EMISSION PET IMAGES,” IEEE TRANSACTIONS ON MEDICAL IMAGING, pp. 687–701, 1994, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/32094.