Shi, Jiayang;
Brown, Louisa;
Zekavat, Amir R;
Pelt, Daniël M;
Hagen, Charlotte K;
(2025)
Self-supervised resolution enhancement for anisotropic volumes in edge illumination X-ray phase contrast micro-computed tomography.
Tomography of Materials and Structures
, 7
, Article 100046. 10.1016/j.tmater.2024.100046.
Preview |
Text
Hagen_1-s2.0-S2949673X24000238-main.pdf Download (7MB) | Preview |
Abstract
X-ray phase contrast micro-computed tomography (micro-CT) can achieve higher contrast than conventional absorption-based X-ray micro-CT by utilizing refraction in addition to attenuation. In this work, we focus on a specific X-ray phase contrast technique, edge illumination (EI) micro-CT. EI uses a sample mask with transmitting apertures that split the X-ray beam into narrow beamlets, enabling detection of refraction-included intensity variations. Between the typical mask designs (circular and slit-shaped apertures), slit-shaped apertures offer practical advantages over circular ones, as they only require sample stepping in one direction, thereby reducing scanning time. However, this leads to anisotropic resolution, as the slit-shaped apertures enhances resolution only along the direction orthogonal to the slits. To address this limitation, we propose a self-supervised method that trains on high-resolution in-plane images to enhance resolution for out-of-plane images, effectively mitigating anisotropy. Our results on both simulated and real EI micro-CT datasets demonstrate the effectiveness of the proposed method.
Type: | Article |
---|---|
Title: | Self-supervised resolution enhancement for anisotropic volumes in edge illumination X-ray phase contrast micro-computed tomography |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1016/j.tmater.2024.100046 |
Publisher version: | https://doi.org/10.1016/j.tmater.2024.100046 |
Language: | English |
Additional information: | © 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Self-supervised learning, Resolution enhancement, Super-resolution, Micro-computed tomography, X-ray phase-contrast imaging |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
URI: | https://discovery.ucl.ac.uk/id/eprint/10202846 |
Archive Staff Only
![]() |
View Item |