NGPS：基于邻域引导的补丁采样的结构保持自监督去噪
NGPS: Structure-Preserving Self-Supervised Denoising via Neighbor-Guided Patch Sampling
AI总结：
提出NGPS框架，通过邻域引导的补丁采样在局部错位下构建自监督去噪目标，无需显式配准，在CT和MRI上提升保真度和结构敏感指标。
摘要：
相邻切片自监督去噪对于体积医学成像具有吸引力，但切片间错位破坏了解剖对应关系，当相邻切片被简单用作目标时，常产生重影和模糊边缘。我们提出邻域引导补丁采样（NGPS），一种轻量级框架，在局部切片间错位下构建相邻监督，无需显式配准。为避免从误导性目标中学习，先前方法通常掩盖差异区域，但这稳定了训练，却以留下大量相邻证据未被利用为代价，尤其是在高频解剖边界附近。NGPS通过将结构匹配与信号检索解耦来解决这一问题：对于每个被掩盖的位置，它在局部邻域中使用简单的引导图像（例如快速双边滤波）搜索结构相似的候选补丁，同时从匹配坐标处的原始噪声相邻切片直接检索监督信号。通过在噪声衰减的引导上进行匹配，同时从相邻切片检索原始值，NGPS无需学习配准模块即可构建局部伪目标。在评估的CT和合成Rician MRI设置中，NGPS改善了保真度和结构敏感指标。代码可在该URL获取。
英文摘要：
Neighboring-slice self-supervised denoising is attractive for volumetric medical imaging, yet inter-slice misalignment breaks anatomical correspondence and often yields ghosting and blurred margins when adjacent slices are used naively as targets. We propose Neighbor-Guided Patch Sampling (NGPS), a lightweight framework that constructs neighboring supervision under local inter-slice misalignment without explicit registration. To avoid learning from misleading targets, prior methods commonly mask discrepant regions, but this stabilizes training at the cost of leaving a non-trivial portion of neighboring evidence unexploited, particularly around high-frequency anatomical boundaries. NGPS addresses this by decoupling structure matching from signal retrieval: for each masked location, it searches a local neighborhood for structurally similar candidate patches using a simple guide image (e.g., fast bilateral filtering), while retrieving the supervision signal directly from the raw noisy neighbor at the matched coordinates. By matching on a noise-attenuated guide while retrieving raw values from neighboring slices, NGPS constructs local pseudo targets without a learned registration module. Across the evaluated CT and synthetic-Rician MRI settings, NGPS improves fidelity and structure-sensitive metrics. Code is available at https://github.com/cv-cho/NGPS .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
