ArticleScience advances2020
Three dimensions, two microscopes, one code: Automatic differentiation for x-ray nanotomography beyond the depth of focus limit.
Article in Science advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- Multi-beam multi-slice X-ray ptychography.Scientific reports · 2025Article
- Wavelength-multiplexed multi-mode EUV reflection ptychography based on automatic differentiation.Light, science & applications · 2024Article
- Automatic Differentiation for Inverse Problems in X-ray Imaging and Microscopy.Life (Basel, Switzerland) · 2023Article
- Artifact identification in X-ray diffraction data using machine learning methods.Journal of synchrotron radiation · 2023Article
- Efficient ptychographic phase retrieval via a matrix-free Levenberg-Marquardt algorithm.Optics express · 2021Article
- Using a modified double deep image prior for crosstalk mitigation in multislice ptychography.Journal of synchrotron radiation · 2021Article
- Upscaling X-ray nanoimaging to macroscopic specimens.Journal of applied crystallography · 2021Review
- Adorym: a multi-platform generic X-ray image reconstruction framework based on automatic differentiation.Optics express · 2021Article
- Comparison of distributed memory algorithms for X-ray wave propagation in inhomogeneous media.Optics express · 2020Article
- Near, far, wherever you are: simulations on the dose efficiency of holographic and ptychographic coherent imaging.Journal of applied crystallography · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Conventional tomographic reconstruction algorithms assume that one has obtained pure projection images, involving no within-specimen diffraction effects nor multiple scattering. Advances in x-ray nanotomography are leading toward the violation of these assumptions, by combining the high penetration power of x-rays, which enables thick specimens to be imaged, with improved spatial resolution that decreases the depth of focus of the imaging system. We describe a reconstruction method where multiple scattering and diffraction effects in thick samples are modeled by multislice propagation and the 3D object function is retrieved through iterative optimization. We show that the same proposed method works for both full-field microscopy and for coherent scanning techniques like ptychography. Our implementation uses the optimization toolbox and the automatic differentiation capability of the open-source deep learning package TensorFlow, demonstrating a straightforward way to solve optimization problems in computational imaging with flexibility and portability.
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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.