ArticleiScience2023
A general image misalignment correction method for tomography experiments.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Data-driven deformation correction in X-ray spectro-tomography with implicit neural networks.Patterns (New York, N.Y.) · 2026Article
- StreamSAXS: a Python-based workflow platform for processing streaming SAXS/WAXS data.Journal of synchrotron radiation · 2024Article
- A step towards 6D WAXD tensor tomography.IUCrJ · 2024Article
- Towards full-stack deep learning-empowered data processing pipeline for synchrotron tomography experiments.Innovation (Cambridge (Mass.)) · 2024Review
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Authors and funding
9 authors.
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Abstract
Tomography experiments generate three-dimensional (3D) reconstructed slices from a series of two-dimensional (2D) projection images. However, the mechanical system generates joint offsets that result in unaligned 2D projections. This misalignment affects the reconstructed images and reduces their actual spatial resolution. In this study, we present a novel method called outer contour-based misalignment correction (OCMC) for correcting image misalignments in tomography. We use the sample's outer contour structure as auxiliary information to estimate the extent of misalignment in each image. This method is generic and can be used with various tomography imaging techniques. We validated our method with five datasets collected from different samples and across various tomography techniques. The OCMC method demonstrated significant advantages in terms alignment accuracy and time efficiency. As an end-to-end correction method, OCMC can be easily integrated into an online tomography data processing pipeline and facilitate feedback control in future synchrotron tomography experiments.
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