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An automatic threshold-based scaling method for enhancing the usefulness of Tc-HMPAO SPECT in the diagnosis of Alzheimer’s disease

  • Pankaj Saxena
  • Dan G. Pavel
  • Juan Carlos Quintana
  • Barry Horwitz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1496)

Abstract

Functional imaging of the brain can aid in the diagnosis of Alzheimer’s disease. Tc-HMPAO SPECT is widely available and relatively inexpensive to use. Combined with computer-based analysis of images, SPECT is a powerful tool in detecting decreases in brain perfusion caused by Alzheimer’s disease. However, analysis can falsely elevate the perfusion of normal areas and diminish the perfusion of atrophic areas in the Alzheimer’s brain when used with conventional scaling methods. In this paper, we present a technique for scaling images that overcomes the problems associated with conventional scaling methods. Our technique was successful in eliminating or attenuating the false increases in perfusion shown in probable Alzheimer’s patients in over 90% of cases (n=17), and in enhancing the sensitivity of detection of degenerative changes by Statistical Parametric Mapping.

Keywords

Positron Emission Tomography Single Photon Emission Compute Tomography Single Photon Emission Compute Tomography Image Brain Single Photon Emission Compute Tomography Proportional Scaling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Pankaj Saxena
    • 1
  • Dan G. Pavel
    • 2
  • Juan Carlos Quintana
    • 3
  • Barry Horwitz
    • 4
  1. 1.Biomedical Visualization Laboratory, Department of NeurosurgeryUniversity of Illinois at ChicagoChicago
  2. 2.Nuclear Medicine, Department of RadiologyUniversity of Illinois at ChicagoUSA
  3. 3.Catholic UniversitySantiagoChile
  4. 4.Laboratory of Neurosciences, National Institute of AgingNIHBethesda

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