  Reconstruction of ultrasound (US) images from radio-frequency data can be
conceptualized as a linear inverse problem. Traditional deep learning
approaches, which aim to improve the quality of US images by directly learning
priors, often encounter challenges in generalization. Recently, diffusion-based
generative models have received significant attention within the research
community due to their robust performance in image reconstruction tasks.
However, a limitation of these models is their inherent low speed in generating
image samples from pure Gaussian noise progressively. In this study, we exploit
the inherent similarity between the US images reconstructed from a single plane
wave (PW) and PW compounding PWC). We hypothesize that a single PW can take a
shortcut to reach the diffusion trajectory of PWC, removing the need to begin
with Gaussian noise. By employing an advanced diffusion model, we demonstrate
its effectiveness in US image reconstruction, achieving a substantial reduction
in sampling steps. In-vivo experimental results indicate that our approach can
reduce sampling steps by 60%, while preserving comparable performance metrics
with the conventional diffusion model.
