Reduced rank formulation for increased computational efficiency in medical ultrasound model-based beamforming

Abstract

Recently, we have developed a model-based adaptive beamforming algorithm, entitled dTONE, which significantly increases both image contrast and resolution by conducting a global optimization based on a model of a sparse set of hypothetical source locations. Due to the global nature of this optimization, a single bright source from an un-modeled location can cause significant degradation of the resulting image. As a result, the entire space from which signal may be received must be finely sampled, requiring a model of very large scale and computational complexity. We have developed a method that uses a reduced rank formulation of a subset of the hypothetical source locations to reduce the computational complexity of dTONE by several orders of magnitude with minimal degradation in image quality. Computation times were reduced by anywhere from 3.7 to 18.3 times while maintaining an image contrast and resolution far superior to that of conventional beamforming.