Assessment of scanning model observers with hybrid SPECT images

H. C. Gifford; P. H. Pretorius; M. A. King

doi:10.1117/12.770994

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6 March 2008 Assessment of scanning model observers with hybrid SPECT images

H. C. Gifford, P. H. Pretorius, M. A. King

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Proceedings Volume 6917, Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment; 69171C (2008) https://doi.org/10.1117/12.770994
Event: Medical Imaging, 2008, San Diego, California, United States

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.

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H. C. Gifford, P. H. Pretorius, and M. A. King "Assessment of scanning model observers with hybrid SPECT images", Proc. SPIE 6917, Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment, 69171C (6 March 2008); https://doi.org/10.1117/12.770994

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