  Image reconstruction from radio-frequency data is pivotal in ultrafast plane
wave ultrasound imaging. Unlike the conventional delay-and-sum (DAS) technique,
which relies on somewhat imprecise assumptions, deep learning-based methods
perform image reconstruction by training on paired data, leading to a notable
enhancement in image quality. Nevertheless, these strategies often exhibit
limited generalization capabilities. Recently, denoising diffusion models have
become the preferred paradigm for image reconstruction tasks. However, their
reliance on an iterative sampling procedure results in prolonged generation
time. In this paper, we propose a novel sampling framework that concurrently
enforces data consistency of ultrasound signals and data-driven priors. By
leveraging the advanced diffusion model, the generation of high-quality images
is substantially expedited. Experimental evaluations on an in-vivo dataset
indicate that our approach with a single plane wave surpasses DAS with spatial
coherent compounding of 75 plane waves.
