A task-based quality control metric for digital mammography

Abstract

A reader study was conducted to tune the parameters of an observer model used to predict the detectability index (dʹ ) of test objects as a task-based quality control (QC) metric for digital mammography. A simple test phantom was imaged to measure the model parameters, namely, noise power spectrum, modulation transfer function and test-object contrast. These are then used in a non-prewhitening observer model, incorporating an eye-filter and internal noise, to predict dʹ. The model was tuned by measuring dʹ of discs in a four-alternative forced choice reader study. For each disc diameter, dʹ was used to estimate the threshold thicknesses for detectability. Data were obtained for six types of digital mammography systems using varying detector technologies and x-ray spectra. A strong correlation was found between measured and modeled values of dʹ, with Pearson correlation coefficient of 0.96. Repeated measurements from separate images of the test phantom show an average coefficient of variation in dʹ for different systems between 0.07 and 0.10. Standard deviations in the threshold thickness ranged between 0.001 and 0.017 mm. The model is robust and the results are relatively system independent, suggesting that observer model dʹ shows promise as a cross platform QC metric for digital mammography.