Robustness of Wiener filtering as an approximation to the Bayesian observer stragegy

Wiener filters applied to channel-summed RF echo data was approximated to the ideal Bayesian beamforming strategy for imaging breast lesion features. They can be computed in real-time and perform well provided the system response matrix is circulant. Real systems exhibit depth-varying impulse responses, so performance degrades for some essential diagnostic features. This paper describes the effects of circulant assumption (CA) violations on the robustness of human visual discrimination efficiency, and the effects of regularization, as applied to beamformers for breast sonography. The Wiener filter is then adapted for shift-varying system responses. The trade off between performance and computation time for each approach is studied using simulations and phantoms scanned with a commercial system.