Despite its wide use in medicine, ultrasound imaging faces several challenges related to its poor signal-to-noise ratio and several sources of noise and artefacts. Enhancing ultrasound image quality involves balancing concurrent factors like contrast, resolution, and speckle preservation. In recent years, there has been progress both in model-based and learning-based approaches to improve ultrasound image reconstruction. Bringing the best from both worlds, we propose a hybrid approach leveraging advances in diffusion models. To this end, we adapt Denoising Diffusion Restoration Models (DDRM) to incorporate ultrasound physics through a linear direct model and an unsupervised fine-tuning of the prior diffusion model. We conduct comprehensive experiments on simulated, in-vitro, and in-vivo data, demonstrating the efficacy of our approach in achieving high-quality image reconstructions from a single plane wave input and in comparison to state-of-the-art methods. Finally, given the stochastic nature of the method, we analyse in depth the statistical properties of single and multiple-sample reconstructions, experimentally show the informativeness of their variance, and provide an empirical model relating this behaviour to speckle noise. The code and data are available at: (upon acceptance).

翻译：尽管超声成像在医学中广泛应用，但其仍面临信噪比低、噪声和伪影来源多样等诸多挑战。提升超声图像质量需要平衡对比度、分辨率和散斑保留等多重因素。近年来，基于模型驱动与数据驱动的方法在改善超声图像重建方面均取得进展。为融合两者优势，我们提出一种混合方法，利用扩散模型的先进技术。具体而言，我们通过线性正向模型和先验扩散模型的无监督微调，将超声物理特性融入去噪扩散恢复模型（DDRM）中。基于模拟、体外和体内数据开展的全面实验表明，该方法在仅需单次平面波输入的情况下，能够实现与现有最优方法相媲美的高质量图像重建。最后，鉴于该方法具有随机性特征，我们深入分析了单样本与多样本重建的统计特性，通过实验验证了方差的信息价值，并建立了描述该行为与散斑噪声关系的经验模型。相关代码与数据将于论文接受后提供。