Despite today's prevalence of ultrasound imaging in medicine, ultrasound signal-to-noise ratio is still affected by several sources of noise and artefacts. Moreover, enhancing ultrasound image quality involves balancing concurrent factors like contrast, resolution, and speckle preservation. Recently, there has been progress in both model-based and learning-based approaches addressing the problem of ultrasound image reconstruction. Bringing the best from both worlds, we propose a hybrid reconstruction method combining an ultrasound linear direct model with a learning-based prior coming from a generative Denoising Diffusion model. More specifically, we rely on the unsupervised fine-tuning of a pre-trained Denoising Diffusion Restoration Model (DDRM). Given the nature of multiplicative noise inherent to ultrasound, this paper proposes an empirical model to characterize the stochasticity of diffusion reconstruction of ultrasound images, and shows the interest of its variance as an echogenicity map estimator. We conduct experiments on synthetic, in-vitro, and in-vivo data, demonstrating the efficacy of our variance imaging approach in achieving high-quality image reconstructions from single plane-wave acquisitions and in comparison to state-of-the-art methods. The code is available at: https://github.com/Yuxin-Zhang-Jasmine/DRUSvar

翻译：尽管超声成像在当今医学中已广泛应用，但超声信噪比仍受到多种噪声和伪影源的影响。此外，提升超声图像质量需要权衡对比度、分辨率和散斑保持等多个相互制约的因素。近年来，基于模型和基于学习的方法在超声图像重建问题上均取得了进展。为融合两类方法的优势，我们提出一种混合重建方法，将超声线性直接模型与基于生成式去噪扩散模型的学习先验相结合。具体而言，我们采用对预训练去噪扩散恢复模型（DDRM）进行无监督微调的策略。鉴于超声图像固有的乘性噪声特性，本文提出一个经验模型来刻画超声图像扩散重建的随机性，并证明其方差作为回声强度图估计器的价值。我们在合成数据、体外数据和体内数据上进行了实验，结果表明：无论是从单次平面波采集实现高质量图像重建，还是与现有先进方法相比，我们的方差成像方法均展现出卓越性能。代码发布于：https://github.com/Yuxin-Zhang-Jasmine/DRUSvar