用于超声散斑抑制的自适应不确定性引导融合的零空间扩散恢复
Null-Space Diffusion Restoration with Adaptive Uncertainty-Guided Fusion for Ultrasound Speckle Reduction
- Hankuk University of Foreign Studies（韩国外国语大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对超声B型成像的散斑噪声抑制问题，提出不确定性引导零空间扩散框架，在PICMUS等数据集上实现了散斑抑制与结构保留的平衡，达到了具竞争力的gCNR值。
AI中文摘要：
超声B型成像通常受散斑噪声和伪影影响，需要在对比度、分辨率和解剖结构保留之间实现精细平衡。尽管近期的散斑抑制方法取得了一定进展，但监督学习方法仍受限于“真值悖论”，该问题源于体内场景中无无噪声真值参考图像。现有基于扩散的无监督方法通常在非线性对数压缩域直接执行数据一致性，当映射回包络域时，可能会不成比例地放大背景伪影。为克服这些局限，我们提出了不确定性引导零空间扩散（UGNS）框架，这是一种新型无标签解决方案，通过逆对数压缩获得稳定的正包络代理，对其执行一致性校正。所提出的UGNS引入了多项技术创新：(a) 在稳定包络域提取结构先验，以生成保留解剖结构的鲁棒信号包络；(b) 开发自适应范围-零空间重构机制，利用自适应权重掩码通过范围空间投影保留组织区域；(c) 引入自适应的不确定性引导融合以缓解采样变异性。我们使用PICMUS基准和体内数据集开展了大量对比实验，结果表明，UGNS在不同数据集上均达到了具有竞争力的广义对比度-噪声比（gCNR）值，此外，已成功验证UGNS可在有效抑制散斑噪声的同时保留精细空间分辨率。代码可在该https URL获取。
英文摘要：
Ultrasound B-mode imaging commonly suffers from speckle noise and artifacts, requiring a delicate balance between contrast, resolution, and preservation of anatomical structures. Although recently developed despeckling methods have achieved some progress, supervised learning approaches remain fundamentally limited by the ground truth paradox, which arises from the absence of noise-free, ground truth reference images in in vivo scenarios. Existing unsupervised diffusion-based methods typically enforce data consistency directly in the nonlinear log-compressed domain, which can disproportionately amplify background artifacts when mapped back to the envelope domain. To overcome these limitations, we propose an uncertainty-guided null-space diffusion (UGNS) framework, a novel label-free solution that enforces consistency correction on a stabilized positive-envelope proxy obtained via inverse log compression. The proposed UGNS introduces several technical novelties: (a) extraction of a structural prior in the stabilized envelope domain to produce a robust signal envelope that preserves anatomical structure, (b) development of an adaptive range-null reconstruction mechanism that uses an adaptive weight mask to preserve tissue regions via range-space projection, and (c) introduction of uncertainty-guided fusion in an adaptive way to mitigate sampling variability. Extensive and comparative experiments were conducted using the PICMUS benchmark and in vivo datasets. The results demonstrate that UGNS achieves competitive generalized contrast-to-noise ratio (gCNR) values across diverse datasets. In addition, it is successfully validated that UGNS effectively suppresses speckle noise while preserving fine spatial resolution. Code is available at https://github.com/yousirong/UGNS.git.
