Fast Sampling generative model for Ultrasound image reconstruction
This work addresses the problem of slow image reconstruction in ultrafast ultrasound imaging for medical applications, offering a significant speed improvement over existing methods.
The paper tackles the slow generation time of diffusion models for ultrasound image reconstruction by proposing a novel sampling framework that enforces data consistency and data-driven priors, achieving results where a single plane wave surpasses conventional methods using 75 plane waves.
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.