Ultrasonography offers an inexpensive, widely-accessible and compact medical imaging solution. However, compared to other imaging modalities such as CT and MRI, ultrasound images notoriously suffer from strong speckle noise, which originates from the random interference of sub-wavelength scattering. This deteriorates ultrasound image quality and makes interpretation challenging. We here propose a new unsupervised ultrasound speckle reduction and image denoising method based on maximum-a-posteriori estimation with deep generative priors that are learned from high-quality MRI images. To model the generative tissue reflectivity prior, we exploit normalizing flows, which in recent years have shown to be very powerful in modeling signal priors across a variety of applications. To facilitate generaliation, we factorize the prior and train our flow model on patches from the NYU fastMRI (fully-sampled) dataset. This prior is then used for inference in an iterative denoising scheme. We first validate the utility of our learned priors on noisy MRI data (no prior domain shift), and then turn to evaluating performance on both simulated and in-vivo ultrasound images from the PICMUS and CUBDL datasets. The results show that the method outperforms other (unsupervised) ultrasound denoising methods (NLM and OBNLM) both quantitatively and qualitatively.
翻译:超声波分析提供了一种廉价、广泛和紧凑的医疗成像解决方案。然而,与CT和MRI等其他成像模式相比,超声波图像臭名昭著地受到来自亚波长散射随机干扰的强烈分光噪音的影响,这导致超声波图像质量恶化,导致解释困难。我们在此提出一种新的未经监督的超声波分谱减少和图像分解方法,其依据是从高质量MRI图像中学习到的、具有深层基因分解前端的最大化超声波估计。对于之前的基因分解组织反射率进行模拟,我们利用了正常流,近年来在对各种应用的先前信号进行建模时显示非常强大的。为了便于概括,我们将先前的图像分化成一个因素,并在NU UM 快速(全印版) 数据集中对流动模型进行补差,然后用来推断我们以前学到的MRI数据的效用。我们首先验证了MRI数据(不是以往的超域变换)数据,然后转向对MU(ML) 和MU 模拟和超频方法显示其他数据结果。