Inverse Problem of Ultrasound Beamforming With Non-Local Structure Tensor Total Variation

The inverse problem of ultrasound beamforming has recently emerged as a novel imaging method, attracting growing interest from the research community. Inverse problems in ultrasound beamforming hypothesize that the received signal is linearly correlated with the beamformed image. Based on this assumption, a minimization problem comprises a fidelity term derived from the linear model and a regularization term that considers prior information about the underlying image. This study introduces the non-local structure tensor total variation as the regularization term to exploit the image’s local geometry and non-local self-similarity properties. Such regularization can also preserve the edge or orientation of an image. The simulation results demonstrate that using a single plane wave, the proposed method can achieve a spatial resolution of 0.34 mm full-width at half-maximum, a contrast-to-noise ratio of 16.82 dB, and a generalized contrast-to-noise ratio of 0.99.