Wiener filters applied to channel-summed RF echo data was approximated tothe ideal Bayesian beamforming strategy for imaging breast lesion features. Theycan be computed in real-time and perform well provided the system responsematrix is circulant. Real systems exhibit depth-varying impulse responses, soperformance degrades for some essential diagnostic features. This paperdescribes the effects of circulant assumption (CA) violations on the robustnessof human visual discrimination efficiency, and the effects of regularization, asapplied to beamformers for breast sonography. The Wiener filter is then adaptedfor shift-varying system responses. The trade off between performance andcomputation time for each approach is studied using simulations and phantomsscanned with a commercial system.