Nicholls Daniel, Kobylynska Maryna, Broad Zoë, Wells Jack, Robinson Alex, McGrouther Damien, Moshtaghpour Amirafshar, Kirkland Angus I, Fleck Roland A, Browning Nigel D
Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, L69 3BX, UK.
SenseAI Innovations Ltd., Liverpool, L69 3BX, UK.
Microsc Microanal. 2024 Mar 7;30(1):96-102. doi: 10.1093/micmic/ozae005.
Traditional image acquisition for cryo focused ion-beam scanning electron microscopy (FIB-SEM) tomography often sees thousands of images being captured over a period of many hours, with immense data sets being produced. When imaging beam sensitive materials, these images are often compromised by additional constraints related to beam damage and the devitrification of the material during imaging, which renders data acquisition both costly and unreliable. Subsampling and inpainting are proposed as solutions for both of these aspects, allowing fast and low-dose imaging to take place in the Focused ion-beam scanning electron microscopy FIB-SEM without an appreciable loss in image quality. In this work, experimental data are presented which validate subsampling and inpainting as a useful tool for convenient and reliable data acquisition in a FIB-SEM, with new methods of handling three-dimensional data being employed in the context of dictionary learning and inpainting algorithms using a newly developed microscope control software and data recovery algorithm.
传统的用于冷冻聚焦离子束扫描电子显微镜(FIB-SEM)断层扫描的图像采集通常需要在数小时内拍摄数千张图像,从而产生海量数据集。在对束敏感材料进行成像时,这些图像往往会受到与成像过程中束损伤和材料失透相关的额外限制的影响,这使得数据采集既昂贵又不可靠。针对这两个方面,提出了下采样和图像修复方法,使得在聚焦离子束扫描电子显微镜FIB-SEM中能够进行快速低剂量成像,而不会导致图像质量出现明显损失。在这项工作中,展示了实验数据,这些数据验证了下采样和图像修复作为在FIB-SEM中方便可靠地进行数据采集的有用工具,同时在字典学习和使用新开发的显微镜控制软件及数据恢复算法的图像修复算法的背景下,采用了处理三维数据的新方法。