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.