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Proceedings Paper

Tests of scanning model observers for myocardial SPECT imaging
Author(s): H. C. Gifford; P. H. Pretorius; J. G. Brankov
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Paper Abstract

Many researchers have tested and applied human-model observers as part of their evaluations of reconstruction methods for SPECT perfusion imaging. However, these model observers have generally been limited to signal-known- exactly (SKE) detection tasks. Our objective is to formulate and test scanning model observers that emulate humans in detection-localization tasks involving perfusion defects. Herein, we compare several models based on the channelized nonprewhitening (CNPW) observer. Simulated Tc-99m images of the heart with and without defects were created using a mathematical anthropomorphic phantom. Reconstructions were performed with an iterative algorithm and postsmoothed with a 3D Gaussian filter. Human and model-observer studies were conducted to assess the optimal number of iterations and the smoothing level of the filter. The human-observer study was a multiple-alternative forced-choice (MAFC) study with five defects. The CNPW observer performed the MAFC study, but also performed an SKE-but-variable (SKEV) study and a localization ROC (LROC) study. A separate LROC study applied an observer based on models of human search in mammograms. The amount of prior knowledge about the possible defects differed for these four model-observer studies. The trend was towards improved agreement with the human observers as prior knowledge decreased.

Paper Details

Date Published: 12 March 2009
PDF: 8 pages
Proc. SPIE 7263, Medical Imaging 2009: Image Perception, Observer Performance, and Technology Assessment, 72630R (12 March 2009); doi: 10.1117/12.813906
Show Author Affiliations
H. C. Gifford, Univ. of Massachusetts Medical School (United States)
P. H. Pretorius, Univ. of Massachusetts Medical School (United States)
J. G. Brankov, Illinois Institute of Technology (United States)

Published in SPIE Proceedings Vol. 7263:
Medical Imaging 2009: Image Perception, Observer Performance, and Technology Assessment
Berkman Sahiner; David J. Manning, Editor(s)

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