发表机构
Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种无需先验模型的通用漂移校正方法，结合仿射与非刚性校正，适用于多维扫描显微镜，可显著提升处理速度并实现自动化校正。
AI 中文摘要
在扫描显微镜中，漂移导致样品被采样的位置偏离标称探针位置。这种位移改变了记录信号的空间分配，并使二维成像、通道分辨光谱映射和扫描位置分辨衍射分析中的定量测量产生偏差。在此，我们将正交扫描漂移校正从二维图像扩展到光谱图像和衍射数据集。我们展示了如何使用不同方向的多维扫描或结构参考图像来恢复探针位置。恢复的位置既可用于将多维数据重采样到规则网格上，也可用于将每个记录信号分配到其校正后的坐标。我们的方法结合了仿射和非刚性校正，无需先验结构模型，并作为开源、GPU加速的软件实现，将处理时间减少两到三个数量级，从而实现常规和自动化的定量多维显微镜漂移校正。
英文摘要
In scanning microscopy, drift causes the specimen to be sampled at positions displaced from the nominal probe positions. This displacement alters the spatial assignment of the recorded signals and biases quantitative measurements across two-dimensional imaging, channel-resolved spectroscopic mapping, and scan-position-resolved diffraction analysis. Here, we extend orthogonal-scan drift correction from 2D images to spectrum images and diffraction datasets. We demonstrate how to recover probe positions using either differently oriented multidimensional scans or structural reference images. The recovered positions are used either to resample the multidimensional data onto a regular grid or to assign each recorded signal to its corrected coordinate. Our method combines affine and non-rigid correction, requires no prior structural model, and is implemented as open-source, GPU-accelerated software that reduces processing times by two to three orders of magnitude, enabling routine and automated drift correction for quantitative multidimensional microscopy.
