Ultrasound Image Enhancement with the Variance of Diffusion Models

Yuxin Zhang, Clément Huneau, Jérôme Idier, Diana Mateus
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引用次数: 0

Abstract

Ultrasound imaging, despite its widespread use in medicine, often suffers from various sources of noise and artifacts that impact the signal-to-noise ratio and overall image quality. Enhancing ultrasound images requires a delicate balance between contrast, resolution, and speckle preservation. This paper introduces a novel approach that integrates adaptive beamforming with denoising diffusion-based variance imaging to address this challenge. By applying Eigenspace-Based Minimum Variance (EBMV) beamforming and employing a denoising diffusion model fine-tuned on ultrasound data, our method computes the variance across multiple diffusion-denoised samples to produce high-quality despeckled images. This approach leverages both the inherent multiplicative noise of ultrasound and the stochastic nature of diffusion models. Experimental results on a publicly available dataset demonstrate the effectiveness of our method in achieving superior image reconstructions from single plane-wave acquisitions. The code is available at: https://github.com/Yuxin-Zhang-Jasmine/IUS2024_Diffusion.
利用扩散模型方差增强超声图像
超声波成像尽管在医学中应用广泛,但经常会受到各种噪声源和伪影的影响,从而影响信噪比和整体图像质量。增强超声图像需要在对比度、分辨率和斑点保留之间取得微妙的平衡。本文介绍了一种将自适应波束成形与基于扩散的方差成像相结合的新方法,以应对这一挑战。我们的方法通过应用基于特征空间的最小方差(EBMV)波束成形技术和根据超声波数据微调的腺扩散模型,计算多个腺扩散样本的方差,从而生成高质量的斑点图像。这种方法充分利用了超声波固有的乘噪声和扩散模型的随机性。在一个公开数据集上的实验结果表明,我们的方法能有效地从单平面波采集中获得优质图像重建。代码见:https://github.com/Yuxin-Zhang-Jasmine/IUS2024_Diffusion。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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