Impact of number of repeated scans on model observer performance for a low-contrast detection task in CT

In previous investigations on CT image quality, channelized Hotelling observer (CHO) models have been shown to well represent human observer performance in several phantom-based detection/discrimination tasks. In these studies, a large number of independent images was necessary to estimate the expectation images and covariance matrices for each test condition. The purpose of this study is to investigate how the number of repeated scans affects the precision and accuracy of the CHO’s performance in a signal-known-exactly detection task. A phantom containing 21 low-contrast objects (3 contrast levels and 7 sizes) was scanned with a 128-slice CT scanner at three dose levels. For each dose level, 100 independent images were acquired for each test condition. All images were reconstructed using filtered-backprojection (FBP) and a commercial iterative reconstruction algorithm. For each combination of dose level and reconstruction method, the low-contrast detectability, quantified with the area under receiver operating characteristic curve (Az), was calculated using a previously validated CHO model. To determine the dependency of CHO performance on the number of repeated scans, the Az value was calculated for different number of channel filters, for each object size and contrast, and for different dose/reconstruction settings using all 100 repeated scans. The Az values were also calculated using randomly selected subsets of the scans (from 10 to 90 scans with an increment of 10 scans). Using the Az from the 100 scans as the reference, the accuracy of Az values calculated from a fewer number of scans was determined and the minimal number of scans was subsequently derived. For the studied signal-known-exactly detection task, results demonstrated that, the minimal number of scans depends on dose level, object size and contrast level, and channel filters.