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Author Notes:

Correspondence and requests for materials should be addressed to X.Y. (email: xiaogang.yang@outlook.com)

X.Y. developed the algorithm, implemented the simulations, processed the experimental data and analyzed the results.

V.D.A. conducted all the nano-CT measurements. F.D.C. and D.G. assisted the data analysis.

W.S. scaled up the CNN approach for running on GPU cluster.

N.K. provided the mouse brain sample.

E.L.D. implemented the segmentation and 3D rendering of the brain data.

All the authors reviewed the manuscript.

The authors declare no competing interests.

Subjects:

Research Funding:

This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357.

This research also used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357.

Low-dose x-ray tomography through a deep convolutional neural network

Tools:

Journal Title:

Scientific Reports

Volume:

Volume 8, Number 1

Publisher:

, Pages 2575-2575

Type of Work:

Article | Final Publisher PDF

Abstract:

Synchrotron-based X-ray tomography offers the potential for rapid large-scale reconstructions of the interiors of materials and biological tissue at fine resolution. However, for radiation sensitive samples, there remain fundamental trade-offs between damaging samples during longer acquisition times and reducing signals with shorter acquisition times. We present a deep convolutional neural network (CNN) method that increases the acquired X-ray tomographic signal by at least a factor of 10 during low-dose fast acquisition by improving the quality of recorded projections. Short-exposure-time projections enhanced with CNNs show signal-to-noise ratios similar to long-exposure-time projections. They also show lower noise and more structural information than low-dose short-exposure acquisitions post-processed by other techniques. We evaluated this approach using simulated samples and further validated it with experimental data from radiation sensitive mouse brains acquired in a tomographic setting with transmission X-ray microscopy. We demonstrate that automated algorithms can reliably trace brain structures in low-dose datasets enhanced with CNN. This method can be applied to other tomographic or scanning based X-ray imaging techniques and has great potential for studying faster dynamics in specimens.

Copyright information:

© 2018 The Author(s).

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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