Estimating breast tomosynthesis performance in detection tasks with variable-background phantoms

Abstract

Digital breast tomosynthesis (DBT) shows potential for improving breast cancer detection. However, this technique has not yet been fully characterized with consideration of the various uncertainties in the imaging chain and optimized with respect to system acquisition parameters. To obtain maximum diagnostic information in DBT, system optimization needs to be performed across a range of patients and acquisition parameters to quantify their impact on tumor detection performance. In addition, a balance must be achieved between x-ray dose and image quality to minimize risk to the patient while maximizing the system's detection performance. To date, researchers have applied a task-based approach to the optimization of DBT with use of mathematical observers for tasks in the signal-known-exactly background-known-exactly (SKE/BKE) and signal-known-exactly background-known statistically (SKE/BKS) paradigms1-3. However, previous observer models provided insufficient treatment of the spatial correlations between multi-angle DBT projections, so we incorporated this correlation information into the modeling methodology. We developed a computational approach that includes three-dimensional variable background phantoms for incorporating background variability, accurate ray-tracing and Poisson distributions for generating noise-free and noisy projections of the phantoms, and a channelized-Hotelling observer4 (CHO) for estimating performance in DBT. We demonstrated our method for a DBT acquisition geometry and calculated the performance of the CHO with Laguerre-Gauss channels as a function of the angular span of the system. Preliminary results indicate that the implementation of a CHO model that incorporates correlations between multi-angle projections gives different performance predictions than a CHO model that ignores multi-angle correlations. With improvement of the observer design, we anticipate more accurate investigations into the impact of multi-angle correlations and background variability on the performance of DBT.