Impact on image noise of incorporating detector blurring into image reconstruction for a small animal PET scanner

Kisung Lee, Robert S. Miyaoka, Tom K. Lewellen, Adam M. Alessio, Paul E. Kinahan

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

We study the noise characteristics of an image reconstruction algorithm that incorporates a model of the non-stationary detector blurring (DB) for a mouse-imaging positron emission tomography (PET) scanner. The algorithm uses ordered subsets expectation maximization (OSEM) image reconstruction, which is used to suppress statistical noise. Including the non-stationary detector blurring in the reconstruction process [OSEM(DB)] has been shown to increase contrast in images reconstructed from measured data acquired on the fully-3D MiCES PET scanner developed at the University of Washington. As an extension, this study uses simulation studies with a fully-3D acquisition mode and our proposed FORE + OSEM(DB) reconstruction process to evaluate the volumetric contrast versus noise trade-offs of this approach. Multiple realizations were simulated to estimate the true noise properties of the algorithm. The results show that incorporation of detector blurring FORE + OSEM(DB) into the reconstruction process improves the contrast/noise trade-offs compared to FORE + OSEM in a radially dependent manner. Adding post reconstruction 3D Gaussian smoothing to FORE + OSEM and FORE + OSEM(DB) reduces the contrast versus noise advantages of FORE + OSEM(DB).

Original languageEnglish
Article number5280515
Pages (from-to)2769-2776
Number of pages8
JournalIEEE Transactions on Nuclear Science
Volume56
Issue number5
DOIs
Publication statusPublished - 2009 Oct

Keywords

  • Detector blurring
  • Fourier rebinning
  • Noise property
  • Ordered subsets expectation maximization (OSEM)
  • Positron emission tomography

ASJC Scopus subject areas

  • Nuclear and High Energy Physics
  • Nuclear Energy and Engineering
  • Electrical and Electronic Engineering

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