发表机构
Argonne National Laboratory; University of Wisconsin–Madison; Emory University（阿贡国家实验室; 威斯康星大学麦迪逊分校; 埃默里大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对扫描探针X射线层析成像中的扫描位置漂移误差，提出一种基于优化的校准方法，同时重建物体，并在合成与真实图像上验证其优于无校准的重建。
AI 中文摘要
扫描探针X射线层析成像对于样品纳米尺度结构的成像非常有用。然而，由于X射线光学器件亮度和相干性的提高，图像分辨率可达10纳米，但极易受实验误差影响。若未能解决这些误差，可能导致图像模糊，最坏情况下，会导致对成像物体结构的误判。在本工作中，我们提出了一种新颖的基于优化的方法，用于校准一种常见且具有挑战性的实验误差来源——扫描位置的漂移，同时重建物体。该方法利用不同测量中耦合且互补的信息，以强制测量与重建之间的一致性。我们在合成和真实层析图像上展示了所提出的方法，并展示了其与未进行显式误差校准的重建相比的优越性能。
英文摘要
Scanning-probe x-ray tomography is useful for imaging nanoscale structures of a sample. The image resolution, which can reach down to 10 nm by the increased brightness and coherence of the x-ray optics, however, is highly susceptible to experimental error. Failure to address these errors can lead to a smeared image and, in the worst case, to misinterpretation of the imaged object's structure. In this work, we present a novel optimization-based approach to calibrate a common yet challenging source of experimental error, the drifts of the scanning positions, while simultaneously reconstructing the object. This approach uses the coupled and complementary information from different measurements to enforce consistency between the measurements and the reconstruction. We illustrate the proposed approach on both synthetic and real tomography images and show its superior performance compared with reconstruction without explicit error calibration.
