Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Feature detection network-based correction method for accurate nano-tomography reconstruction

Not Accessible

Your library or personal account may give you access

Abstract

Driven by the development of advanced x-ray optics such as Fresnel zone plates, nano-resolution full-field transmission x-ray microscopy (Nano-CT) has become a powerful technique for the non-destructive volumetric inspection of objects and has long been developed at different synchrotron radiation facilities. However, Nano-CT data are often associated with random sample jitter because of the drift or radial/axial error motion of the rotation stage during measurement. Without a proper sample jitter correction process prior to reconstruction, the use of Nano-CT in providing accurate 3D structure information for samples is almost impossible. In this paper, to realize accurate 3D reconstruction for Nano-CT, a correction method based on a feature detection neural network, which can automatically extract target features from a projective image and precisely correct sample jitter errors, is proposed, thereby resulting in high-quality nanoscale 3D reconstruction. Compared with other feature detection methods, even if the target feature is overlapped by other high-density materials or impurities, the proposed Nano-CT correction method still acquires sub-pixel accuracy in geometrical correction and is more suitable for Nano-CT reconstruction because of its universal and faster correction speed. The simulated and experimental datasets demonstrated the reliability and validity of the proposed Nano-CT correction method.

© 2022 Optica Publishing Group

Full Article  |  PDF Article
More Like This
Automatic marker-based alignment method for a nano-resolution full-field transmission X-ray microscope

Chenpeng Zhou, Yan Wang, Shanfeng Wang, Jin Zhang, Tianyu Fu, Wanxia Huang, Kai Zhang, and Qingxi Yuan
Appl. Opt. 62(36) 9536-9543 (2023)

Drift correction in laboratory nanocomputed tomography using joint feature correlation

Mengnan Liu, Han Yu, Xiaoqi Xi, Siyu Tan, Linlin Zhu, Zhicun Zhang, Lei Li, Jian Chen, and Bin Yan
Appl. Opt. 62(11) 2784-2791 (2023)

Data availability

Data supporting the findings of the study are available in Ref. [38].

38. 4W1A-Lab, Python code for “Feature detection network-based correction method for accurate nano-tomography reconstruction,” Github, (2022), https://github.com/4W1A-Lab/correction

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (5)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Tables (2)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (2)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved