A low complexity data-dependent beamformer

The classical problem of choosing apodization functions for a beamformer involves a trade-off between mainlobe width and sidelobe level, i.e. a trade-off between resolution and contrast. To avoid this trade-off it has been suggested to apply adaptive methods, such as minimum variance beamforming, to medical ultrasound imaging. This has been an active topic of research in the recent years, and several authors have demonstrated significant improvements in image resolution. However, the improvement comes at a considerable cost. Where the complexity of a conventional beamformer is linear with the number of elements (O(M)), the complexity of the minimum variance beamformer is up to O(M³). In this paper we have applied a method-based on an idea by Vignon and Burcher-which is data-adaptive, but selects the apodization function between a number of predefined windows, giving linear complexity.