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Journal of Medical Imaging

Correlation between human detection accuracy and observer model-based image quality metrics in computed tomography
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Paper Abstract

The purpose of this study was to compare computed tomography (CT) low-contrast detectability from human readers with observer model-based surrogates of image quality. A phantom with a range of low-contrast signals (five contrasts, three sizes) was imaged on a state-of-the-art CT scanner (Siemens’ force). Images were reconstructed using filtered back projection and advanced modeled iterative reconstruction and were assessed by 11 readers using a two alternative forced choice method. Concurrently, contrast-to-noise ratio (CNR), area-weighted CNR (CNRA), and observer model-based metrics were estimated, including nonprewhitening (NPW) matched filter, NPW with eye filter (NPWE), NPW with internal noise, NPW with an eye filter and internal noise (NPWEi), channelized Hotelling observer (CHO), and CHO with internal noise (CHOi). The correlation coefficients (Pearson and Spearman), linear discriminator error, E, and magnitude of confidence intervals, |CI95%|, were used to determine correlation, proper characterization of the reconstruction algorithms, and model precision, respectively. Pearson (Spearman) correlation was 0.36 (0.33), 0.83 (0.84), 0.84 (0.86), 0.86 (0.88), 0.86 (0.91), 0.88 (0.90), 0.85 (0.89), and 0.87 (0.84), E was 0.25, 0.15, 0.2, 0.25, 0.3, 0.25, 0.4, and 0.45, and |CI95%| was 2.84×10−3, 5.29×10−3, 4.91×10−3, 4.55×10−3, 2.16×10−3, 1.24×10−3, 4.58×10−2, and 7.95×10−2 for CNR, CNRA, NPW, NPWE, NPWi, NPWEi, CHO, and CHOi, respectively.

Paper Details

Date Published: 22 September 2016
PDF: 12 pages
J. Med. Imag. 3(3) 035506 doi: 10.1117/1.JMI.3.3.035506
Published in: Journal of Medical Imaging Volume 3, Issue 3
Show Author Affiliations
Justin B. Solomon, Carl E. Ravin Advanced Imaging Labs. (United State)
Duke Univ. Health System (United States)
Ehsan Samei, Duke Univ. Health System (United State)
Duke University Medical Ctr. (United States)
Duke Univ. (United States)

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