SPIE Journal Paper | 22 September 2016
KEYWORDS: Data modeling, Image quality, Model-based design, Performance modeling, Computed tomography, Statistical modeling, Eye models, Reconstruction algorithms, Medical imaging, Signal detection
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.
