Beamforming designs for breast sonography

The ideal observer (IO) methodology enables designers to derive optimal beamforming strategies for specific diagnostic tasks or general classes of tasks, although fast implementation requires approximations. We explore this approach in an effort to assess beamforming performance for a range of lesion-feature discrimination challenges. We show that matched-filter (MF), linearly-constrained minimum-variance (MV) with low-rank approximation, and Wiener-filter (WF) beamformers each emerge as approximations to the ideal-observer strategy for low-contrast lesion discrimination. For high-contrast tasks, we evaluate an iterative Wiener-filter (IWF) beamformer as a computationally-intense method for minimizing contrast assumptions when prior information is available.