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Proceedings Paper

Assessment of scanning model observers with hybrid SPECT images
Author(s): H. C. Gifford; P. H. Pretorius; M. A. King
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Paper Abstract

The purpose of this work was to test procedures for applying scanning model observers in order to predict human-observer lesion-detection performance with hybrid images. Hybrid images consist of clinical backgrounds with simulated abnormalities. The basis for this investigation was detection and localization of solitary pulmonary nodules (SPN) in SPECT lung images, and our overall goal has been to determine the extent to which detection of SPN could be improved by proper modeling of the acquisition physics during the iterative reconstruction process. Towards this end, we conducted human-observer localization ROC (LROC) studies to optimize the number of iterations and the postfiltering of four rescaled block-iterative (RBI) reconstruction strategies with various combinations of attenuation correction (AC), scatter correction (SC), and system-resolution correction (RC). This observer data was then used to evaluate a scanning channelized nonprewhitening model observer. A standard "background-known-exactly" (BKE) task formulation overstated the prior knowledge and training that human observers had about the hybrid images. Results from a quasi-BKE task that preserved some degree of structural noise in the detection task demonstrated better agreement with the humans.

Paper Details

Date Published: 6 March 2008
PDF: 8 pages
Proc. SPIE 6917, Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment, 69171C (6 March 2008); doi: 10.1117/12.770994
Show Author Affiliations
H. C. Gifford, Univ. of Massachusetts Medical School (United States)
P. H. Pretorius, Univ. of Massachusetts Medical School (United States)
M. A. King, Univ. of Massachusetts Medical School (United States)

Published in SPIE Proceedings Vol. 6917:
Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment
Berkman Sahiner; David J. Manning, Editor(s)

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