Trushal Sardhara , Alexander Shkurmanov , Yong Li , Shan Shi , Christian J. Cyron , Roland C. Aydin , Martin Ritter
{"title":"Role of slice thickness quantification in the 3D reconstruction of FIB tomography data of nanoporous materials","authors":"Trushal Sardhara , Alexander Shkurmanov , Yong Li , Shan Shi , Christian J. Cyron , Roland C. Aydin , Martin Ritter","doi":"10.1016/j.ultramic.2023.113878","DOIUrl":null,"url":null,"abstract":"<div><p>In focused ion beam (FIB) tomography, a combination of FIB with a scanning electron microscope (SEM) is used for collecting a series of planar images of the microstructure of nanoporous materials. These planar images serve as the basis for reconstructing the three-dimensional microstructure through segmentation algorithms. However, the assumption of a constant distance between consecutively imaged sections is generally invalid due to random variations in the FIB milling process. This variation complicates the accurate reconstruction of the three-dimensional microstructure. Using synthetic FIB tomography data, we present an algorithm that repositions slices according to their actual thickness and interpolates the results using machine learning-based methods. We applied our algorithm to real datasets, comparing two standard approaches of microstructure reconstruction: <em>on-the-fly</em> via image processing and <em>ruler-based</em> via sample structuring. Our findings indicate that the <em>ruler-based</em> method, combined with our novel slice repositioning and interpolation algorithm, exhibits superior performance in reconstructing the microstructure.</p></div>","PeriodicalId":23439,"journal":{"name":"Ultramicroscopy","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S030439912300195X/pdfft?md5=62650ccf6925bd8a723f9835a48daf18&pid=1-s2.0-S030439912300195X-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ultramicroscopy","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S030439912300195X","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MICROSCOPY","Score":null,"Total":0}
引用次数: 0
Abstract
In focused ion beam (FIB) tomography, a combination of FIB with a scanning electron microscope (SEM) is used for collecting a series of planar images of the microstructure of nanoporous materials. These planar images serve as the basis for reconstructing the three-dimensional microstructure through segmentation algorithms. However, the assumption of a constant distance between consecutively imaged sections is generally invalid due to random variations in the FIB milling process. This variation complicates the accurate reconstruction of the three-dimensional microstructure. Using synthetic FIB tomography data, we present an algorithm that repositions slices according to their actual thickness and interpolates the results using machine learning-based methods. We applied our algorithm to real datasets, comparing two standard approaches of microstructure reconstruction: on-the-fly via image processing and ruler-based via sample structuring. Our findings indicate that the ruler-based method, combined with our novel slice repositioning and interpolation algorithm, exhibits superior performance in reconstructing the microstructure.
期刊介绍:
Ultramicroscopy is an established journal that provides a forum for the publication of original research papers, invited reviews and rapid communications. The scope of Ultramicroscopy is to describe advances in instrumentation, methods and theory related to all modes of microscopical imaging, diffraction and spectroscopy in the life and physical sciences.