Maximum likelihood event estimation and list-mode image reconstruction on GPU hardware

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

The scintillation detectors commonly used in SPECT and PET imaging and in Compton cameras require estimation of the position and energy of each gamma ray interaction. Ideally, this process would yield images with no spatial distortion and the best possible spatial resolution. In addition, especially for Compton cameras, the computation must yield the best possible estimate of the energy of each interacting gamma ray. These goals can be achieved by use of maximum-likelihood (ML) estimation of the event parameters, but in the past the search for an ML estimate has not been computationally feasible. Now, however, graphics processing units (GPUs) make it possible to produce optimal, real-time estimates of position and energy, even from scintillation cameras with a large number of photodetectors. In addition, the mathematical properties of ML estimates make them very attractive for use as list entries in list-mode ML image reconstruction. This two-step ML process - using ML estimation once to get the list data and again to reconstruct the object - allows accurate modeling of the detector blur and, potentially, considerable improvement in reconstructed spatial resolution.

Original languageEnglish (US)
Title of host publication2009 IEEE Nuclear Science Symposium Conference Record, NSS/MIC 2009
Pages4072-4076
Number of pages5
DOIs
StatePublished - Dec 1 2009
Event2009 IEEE Nuclear Science Symposium Conference Record, NSS/MIC 2009 - Orlando, FL, United States
Duration: Oct 25 2009Oct 31 2009

Publication series

NameIEEE Nuclear Science Symposium Conference Record
ISSN (Print)1095-7863

Other

Other2009 IEEE Nuclear Science Symposium Conference Record, NSS/MIC 2009
CountryUnited States
CityOrlando, FL
Period10/25/0910/31/09

ASJC Scopus subject areas

  • Radiation
  • Nuclear and High Energy Physics
  • Radiology Nuclear Medicine and imaging

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