Impact of Number of Repeated Scans on Model Observer Performance for a Low-contrast Detection Task in CT.

Abstract

Channelized Hotelling observer (CHO) has been validated against human observers for detection/classification tasks in clinical CT and shows encouraging correlations. However, the goodness of correlations depends on the number of repeated scans used in CHO to estimate the template and covariance matrices. The purpose of this study is to investigate how the number of repeated scans affects the CHO performance in predicting human observers. A phantom containing 21 low-contrast objects (3 contrast levels and 7 sizes) was scanned on a 128-slice CT scanner at three dose levels. Each scan was repeated 100 times. Images were reconstructed using a filtered-backprojection kernel and a commercial iterative reconstruction method. For each dose level and reconstruction setting, the low-contrast detectability, quantified with the area under receiver operating characteristic curve (Az), was calculated using a previously validated CHO. To determine the dependency of CHO performance on the number of repeated scans, the Az value was calculated for each object and dose/reconstruction setting 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 from a smaller number of scans was determined. The minimum necessary number of scans was subsequently derived. For the studied signal-known-exactly detection task, results demonstrated that, the minimal number of scans required to accurately predict human observer performance depends on dose level, object size and contrast level, and channel filters.