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

Autor:	Du M; Department of Materials Science, Northwestern University, Evanston, IL 60208, USA., Nashed YSG; Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL 60439, USA., Kandel S; Applied Physics Program, Northwestern University, Evanston, IL 60208, USA., Gürsoy D; Advanced Photon Source, Argonne National Laboratory, Lemont, IL 60439, USA.; Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208 USA., Jacobsen C; Advanced Photon Source, Argonne National Laboratory, Lemont, IL 60439, USA.; Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208, USA.; Chemistry of Life Processes Institute, Northwestern University, Evanston, IL 60208, USA.
Jazyk:	angličtina
Zdroj:	Science advances [Sci Adv] 2020 Mar 27; Vol. 6 (13), pp. eaay3700. Date of Electronic Publication: 2020 Mar 27 (Print Publication: 2020).
DOI:	10.1126/sciadv.aay3700
Abstrakt:	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. (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).)
Databáze:	MEDLINE
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