Quantitative comparison of FBP, EM, and Bayesian reconstruction algorithms, including the impact of accurate system modeling, for the IndyPET scanner

Thomas Frese, Ned C. Rouze, Charles A. Bouman, Ken Sauer, Gary D. Hutchins

Research output: Contribution to conferencePaper

Abstract

We quantitatively compare filtered backprojection (FBP), expectation maximization (EM), and Bayesian reconstruction algorithms as applied to the IndyPET scanner, a small to intermediate field of view dedicated research scanner. A key feature of our investigation is the use of an empirical system kernel determined from scans of line source phantoms. This kernel is incorporated into the forward operator of EM and the Bayesian reconstruction algorithms. Our results indicate that, particularly when an accurate system kernel is used, Bayesian methods can significantly improve reconstruction quality over FBP and EM.

Original languageEnglish (US)
Pages1806-1810
Number of pages5
StatePublished - Dec 1 2001
Event2001 IEEE Nuclear Science Symposium Conference Record - San Diego, CA, United States
Duration: Nov 4 2001Nov 10 2001

Other

Other2001 IEEE Nuclear Science Symposium Conference Record
CountryUnited States
CitySan Diego, CA
Period11/4/0111/10/01

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Industrial and Manufacturing Engineering

Cite this

Frese, T., Rouze, N. C., Bouman, C. A., Sauer, K., & Hutchins, G. D. (2001). Quantitative comparison of FBP, EM, and Bayesian reconstruction algorithms, including the impact of accurate system modeling, for the IndyPET scanner. 1806-1810. Paper presented at 2001 IEEE Nuclear Science Symposium Conference Record, San Diego, CA, United States.