ArticleOptics express2022
Joint iterative reconstruction and 3D rigid alignment for X-ray tomography.
Article in Optics express, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
9 citing papers in PubMed, 10 citations in OpenAlex.
- Data-driven deformation correction in X-ray spectro-tomography with implicit neural networks.Patterns (New York, N.Y.) · 2026Article
- Octo-diamond crystal of nanoscale tetrahedra with interchanging chiral motifs.Nature materials · 2025Article
- TomoPyUI: a user-friendly tool for rapid tomography alignment and reconstruction.Journal of synchrotron radiation · 2024Article
- Enabling 3D CT-scanning of cultural heritage objects using only in-house 2D X-ray equipment in museums.Nature communications · 2024Article
- Towards full-stack deep learning-empowered data processing pipeline for synchrotron tomography experiments.Innovation (Cambridge (Mass.)) · 2024Review
- Solving complex nanostructures with ptychographic atomic electron tomography.Nature communications · 2023Article
- Article
- 3D Reconstruction of Wrist Bones from C-Arm Fluoroscopy Using Planar Markers.Diagnostics (Basel, Switzerland) · 2023Article
- Joint iterative reconstruction and 3D rigid alignment for X-ray tomography.Optics express · 2022Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
X-ray tomography is widely used for three-dimensional structure determination in many areas of science, from the millimeter to the nanometer scale. The resolution and quality of the 3D reconstruction is limited by the availability of alignment parameters that correct for the mechanical shifts of the sample or sample stage for the images that constitute a scan. In this paper we describe an algorithm for marker-free, fully automated and accurately aligned and reconstructed X-ray tomography data. Our approach solves the tomographic reconstruction jointly with projection data alignment based on a rigid-body deformation model. We demonstrate the robustness of our method on both synthetic phantom and experimental data and show that our method is highly efficient in recovering relatively large alignment errors without prior knowledge of a low resolution approximation of the 3D structure or a reasonable estimate of alignment parameters.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.