Adaptive SPECT for tumor necrosis detection

Luca Caucci, Matthew A. Kupinski, Melanie Freed, Lars R. Furenlid, Donald W. Wilson, Harrison H. Barrett

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

6 Scopus citations


In this paper, we consider a prototype of an adaptive SPECT system, and we use simulation to objectively assess the system's performance with respect to a conventional, non-adaptive SPECT system. Objective performance assessment is investigated for a clinically relevant task: the detection of tumor necrosis at a known location and in a random lumpy background. The iterative maximum-likelihood expectation-maximization (MLEM) algorithm is used to perform image reconstruction. We carried out human observer studies on the reconstructed images and compared the probability of correct detection when the data are generated with the adaptive system as opposed to the non-adaptive system. Task performance is also assessed by using a channelized Hotelling observer, and the area under the receiver operating characteristic curve is the figure of merit for the detection task. Our results show a large performance improvement of adaptive systems versus non-adaptive systems and motivate further research in adaptive medical imaging.

Original languageEnglish (US)
Title of host publication2008 IEEE Nuclear Science Symposium Conference Record, NSS/MIC 2008
Number of pages4
StatePublished - 2008
Event2008 IEEE Nuclear Science Symposium Conference Record, NSS/MIC 2008 - Dresden, Germany
Duration: Oct 19 2008Oct 25 2008

Publication series

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


Other2008 IEEE Nuclear Science Symposium Conference Record, NSS/MIC 2008


  • Adaptive imaging
  • Assessment of image quality
  • Channelized hotelling observer
  • Detection
  • Human observer
  • MLEM reconstruction
  • Multimodality imaging
  • ROC curve
  • Tumor necrosis

ASJC Scopus subject areas

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


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