DiffPWI: A degradation-aware diffusion model for ultrasound plane-wave imaging.

Abstract

Ultrafast plane-wave imaging (PWI) trades image quality for frame rate under sparse acquisitions. Existing deep learning-based PWI reconstruction methods are limited by the lack of physics-guided degradation modeling, leading to distortions in anatomical structures and speckle statistics. In addition, an inherent low-frequency spectral bias attenuates high-frequency components, further exacerbating detail loss and amplifying reconstruction artifacts. In this study, we propose DiffPWI, a degradation-aware reconstruction framework. DiffPWI employs a latent diffusion model (LDM) to learn the distribution of physically informed degradations in a compressed latent space, providing a physics-informed prior for reconstruction. Furthermore, we introduce a frequency decoupling attention module (FDAM) that adaptively decomposes feature representations into hierarchical frequency sub-bands, enabling preservation of high-frequency anatomical details while suppressing acquisition artifacts. Extensive evaluations on phantom, multi-organ in vivo, and zero-shot datasets demonstrate that DiffPWI achieves improved spatial resolution, contrast recovery, and speckle fidelity compared with state-of-the-art methods, advancing high-fidelity ultrafast ultrasound imaging.