Constrained least squares filtering in high resolution PET and SPECT imaging

Gary D. Hutchins, W. Leslie Rogers, Ping Chiao, Raymond R. Raylman, Brian W. Murphy

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Constrained least squares filtering is a technique which employs apriori tomograph response information for the filtering of projection data prior to the filtered backprojection of radionuclide distribution images. This paper describes a simulation study which evaluates the performance of this algorithm as a function of the sampling density within each projection of the radon transform. The results of this evaluation demonstrate that the efficient application of this algorithm requires higher sampling densities than are typically employed in high resolution PET and SPECT. Therefore, application of this algorithm requires software and/or hardware modification of the data acquisition sampling schemes employed in tomographic systems.

Original languageEnglish (US)
Pages (from-to)647-651
Number of pages5
JournalIEEE Transactions on Nuclear Science
Issue number2
StatePublished - Apr 1990
Externally publishedYes

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Constrained least squares filtering in high resolution PET and SPECT imaging'. Together they form a unique fingerprint.

Cite this