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Proceedings Paper

What observer models best reflect low-contrast detectability in CT?
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Paper Abstract

The purpose of this work was to compare CT low-contrast detectability as measured via human perception experiments with observer model surrogates of image quality measured directly from the images. A phantom was designed with a range of low-contrast circular inserts representing 5 contrast levels and 3 sizes. The phantom was imaged repeatedly (20 times) on a third-generation dual-source CT scanner (SOMATOM Definition Force, Siemens Healthcare). Images were reconstructed at 0.6 mm slice thickness using filtered back projection (FBP) and advanced modeled iterative reconstruction (ADMIRE) and were assessed by eleven blinded and independent readers using a two alternative forced choice (2AFC) detection experiment. The human scores were taken as the accuracy, averaged across observers. The predicted performance was computed directly from the images for several traditional image quality metrics and model observers including contrast to noise ratio (CNR), area weighted CNR (CNRa), non-prewhitening matched filter (NPW), non-prewhitening matched filter with an eye filter (NPWE), channelized Hotelling observer (CHO), and channelized Hotelling observer with internal noise (CHOi). The correlation between model observer predictions and human performance was assessed using linear regression analysis. The coefficient of determination (R2) was used as goodness-of-fit metric to determine how well each model observer predicts human performance. R2 was 0.11, 0.71, 0.73, 0.77, 0.60, and 0.72 for CNR, CNRa, NPW, NPWE, CHO, and CHOi, respectively. The findings demonstrate NPW, NPWE, and CHOi all to have strong correlation with human performance and could be used to optimize scan and reconstruction settings.

Paper Details

Date Published: 17 March 2015
PDF: 8 pages
Proc. SPIE 9416, Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment, 94160I (17 March 2015); doi: 10.1117/12.2081655
Show Author Affiliations
Justin Solomon, Duke Univ. (United States)
Ehsan Samei, Duke Univ. (United States)


Published in SPIE Proceedings Vol. 9416:
Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment
Claudia R. Mello-Thoms; Matthew A. Kupinski, Editor(s)

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