Low contrast detectability in CT for human and model observers in multi-slice data sets

Task-based medical image quality is often assessed by model observers for single slice images. The goal of the study was to determine if model observers can predict human detection performance of low contrast signals in CT for clinical multi-slice (ms) images. We collected 24 different data subsets from a low contrast phantom: 3 dose levels (40, 90, 150 mAs), 4 signals (6 and 8 mm diameter; 10 and 20 HU at 120kV) and 2 reconstruction algorithms (FBP and iterative (IR)). Images were assessed by human and model observers in 4-alternative forced choice (4AFC) experiments with ms data set in a signal-known-exactly (SKE) paradigm. Model observers with single (msCHOa) and multiple (msCHOb) templates were implemented in a train and test method analysis with Dense Difference of Gaussian (DDoG) and Gabor spatial channels. For human observers, we found that percent correct increased with the dose and was higher for iterative reconstructed images than FBP in all investigated conditions. All model observers implemented overestimated human performance in any condition except one case (6mm and 10HU) for msCHOa and msCHOb with Gabor channels. Internal noise could be implemented and a good agreement was found but necessitates independent fits according to the reconstruction method. Generally msCHOb shows higher detection performance than msCHOa with both types of channels. Gabor channels were less efficient than DDoG in this context. These results allow further developments in 3D analysis technique for low contrast CT.