Evaluating an image restoration pipeline for digital mammography across varied radiation exposures and microcalcification sizes using model observer analysis

In this study, we assess the impact of an image restoration pipeline, designed for digital mammography, on the detectability of microcalcifications of different sizes across varied radiation exposures. The restoration pipeline first removes the noise of the image considering a Poisson-Gaussian noise model that incorporates quantum and electronic noise. Then, it appropriately merges the noisy and denoised images to achieve a signal-to-noise ratio (SNR) comparable to an image obtained at a higher radiation dose. We created a database of mammographic images acquired at radiation doses between 50% and 200% of the automatic exposure control (AEC) using a physical anthropomorphic breast phantom. Clustered microcalcifications with diameters ranging from 190 μm to 390 μm were artificially inserted into the phantom images in regions with increased density. The Channelized Hotelling Observer (CHO) was employed as the model observer (MO) to evaluate the detectability of microcalcifications. A pilot study was conducted to adjust the percentage of correct detection to approximately 75% for microcalcifications with a diameter of 270 μm at the AEC dose. We applied the restoration pipeline to the image dataset and calculated the percentage of correctly detected signals (PC) using the MO in a four-alternative forced choice (4-AFC) study. The results indicated a PC enhancement of up to 10% when applying restoration to simulate acquisitions with twice the AEC dose. Additionally, for images acquired with radiation doses below the AEC, our results demonstrated a potential dose reduction of up to 22.4% without compromising microcalcification detectability. The detection of microcalcifications with a diameter of 390 μm remained unaffected by variations in radiation dose.