Ultrasound images are widespread in medical diagnosis for musculoskeletal, cardiac, and obstetrical imaging due to the efficiency and non-invasiveness of the acquisition methodology. However, the acquired images are degraded by acoustic (e.g. reverberation and clutter) and electronic sources of noise. To improve the Peak Signal to Noise Ratio (PSNR) of the images, previous denoising methods often remove the speckles, which could be informative for radiologists and also for quantitative ultrasound. Herein, a method based on the recent Denoising Diffusion Probabilistic Models (DDPM) is proposed. It iteratively enhances the image quality by eliminating the noise while preserving the speckle texture. It is worth noting that the proposed method is trained in a completely unsupervised manner, and no annotated data is required. The experimental blind test results show that our method outperforms the previous nonlocal means denoising methods in terms of PSNR and Generalized Contrast to Noise Ratio (GCNR) while preserving speckles.

翻译：超声图像因其获取方法的高效性和非侵入性，广泛应用于肌肉骨骼、心脏及产科等医学诊断领域。然而，所获取的图像会因声学（如混响和杂波）及电子噪声源而退化。为了提升图像的峰值信噪比（PSNR），以往的去噪方法往往去除散斑，而这些散斑对放射科医生及定量超声分析可能具有信息价值。本文提出一种基于最新去噪扩散概率模型（DDPM）的方法，通过迭代抑制噪声同时保留散斑纹理来增强图像质量。值得注意的是，该方法完全以无监督方式训练，无需任何标注数据。实验盲测结果表明，与以往的非局部均值去噪方法相比，本方法在保持散斑的同时，在PSNR和广义对比噪声比（GCNR）指标上表现更优。