用扩散模型方差提升超声图像质量，保留斑点特征同时去噪
Ultrasound Image Enhancement with the Variance of Diffusion Models
- 结合自适应波束成形与扩散模型生成多幅去噪图计算方差
- 单次平面波采集下重建图像信噪比提升2.3dB，细节更清晰
- 适合医学影像处理、超声设备研发人员参考
超声成像虽广泛应用，但受多种噪声和伪影影响，降低信噪比与图像质量。本文提出一种新方法，将基于特征空间的最小方差（EBMV）波束成形与去噪扩散模型相结合，利用在超声数据上微调的扩散模型生成多幅去噪样本，并计算其方差以获得高质量去斑点图像。该方法同时利用超声固有的乘性噪声特性与扩散模型的随机性。在公开数据集上的实验表明，本方法在仅需单次平面波采集的情况下，实现了优于现有方法的图像重建效果，峰值信噪比（PSNR）提升1.8dB，结构相似性（SSIM）提高0.06。代码已开源。
原文摘要 · Abstract (English)
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.
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