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Three dimensions, two microscopes, one code: Automatic differentiation for x-ray nanotomography beyond the depth of focus limit

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...

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Detalles Bibliográficos
Autores principales: Du, Ming, Nashed, Youssef S. G., Kandel, Saugat, Gürsoy, Doğa, Jacobsen, Chris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7101216/
https://www.ncbi.nlm.nih.gov/pubmed/32258397
http://dx.doi.org/10.1126/sciadv.aay3700
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author Du, Ming
Nashed, Youssef S. G.
Kandel, Saugat
Gürsoy, Doğa
Jacobsen, Chris
author_facet Du, Ming
Nashed, Youssef S. G.
Kandel, Saugat
Gürsoy, Doğa
Jacobsen, Chris
author_sort Du, Ming
collection PubMed
description 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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spelling pubmed-71012162020-04-03 Three dimensions, two microscopes, one code: Automatic differentiation for x-ray nanotomography beyond the depth of focus limit Du, Ming Nashed, Youssef S. G. Kandel, Saugat Gürsoy, Doğa Jacobsen, Chris Sci Adv Research Articles 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. American Association for the Advancement of Science 2020-03-27 /pmc/articles/PMC7101216/ /pubmed/32258397 http://dx.doi.org/10.1126/sciadv.aay3700 Text en Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). http://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (http://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
spellingShingle Research Articles
Du, Ming
Nashed, Youssef S. G.
Kandel, Saugat
Gürsoy, Doğa
Jacobsen, Chris
Three dimensions, two microscopes, one code: Automatic differentiation for x-ray nanotomography beyond the depth of focus limit
title Three dimensions, two microscopes, one code: Automatic differentiation for x-ray nanotomography beyond the depth of focus limit
title_full Three dimensions, two microscopes, one code: Automatic differentiation for x-ray nanotomography beyond the depth of focus limit
title_fullStr Three dimensions, two microscopes, one code: Automatic differentiation for x-ray nanotomography beyond the depth of focus limit
title_full_unstemmed Three dimensions, two microscopes, one code: Automatic differentiation for x-ray nanotomography beyond the depth of focus limit
title_short Three dimensions, two microscopes, one code: Automatic differentiation for x-ray nanotomography beyond the depth of focus limit
title_sort three dimensions, two microscopes, one code: automatic differentiation for x-ray nanotomography beyond the depth of focus limit
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7101216/
https://www.ncbi.nlm.nih.gov/pubmed/32258397
http://dx.doi.org/10.1126/sciadv.aay3700
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