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

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 acquisit...

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Autores principales:	Xiaogang Yang, Vincent De Andrade, William Scullin, Eva L. Dyer, Narayanan Kasthuri, Francesco De Carlo, Doğa Gürsoy
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f2c3eb2baba2489aa424e39aaef3f1eb
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spelling	oai:doaj.org-article:f2c3eb2baba2489aa424e39aaef3f1eb2021-12-02T15:08:02ZLow-dose x-ray tomography through a deep convolutional neural network10.1038/s41598-018-19426-72045-2322https://doaj.org/article/f2c3eb2baba2489aa424e39aaef3f1eb2018-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-19426-7https://doaj.org/toc/2045-2322Abstract 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 specimensXiaogang YangVincent De AndradeWilliam ScullinEva L. DyerNarayanan KasthuriFrancesco De CarloDoğa GürsoyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-13 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xiaogang Yang Vincent De Andrade William Scullin Eva L. Dyer Narayanan Kasthuri Francesco De Carlo Doğa Gürsoy Low-dose x-ray tomography through a deep convolutional neural network
description	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
format	article
author	Xiaogang Yang Vincent De Andrade William Scullin Eva L. Dyer Narayanan Kasthuri Francesco De Carlo Doğa Gürsoy
author_facet	Xiaogang Yang Vincent De Andrade William Scullin Eva L. Dyer Narayanan Kasthuri Francesco De Carlo Doğa Gürsoy
author_sort	Xiaogang Yang
title	Low-dose x-ray tomography through a deep convolutional neural network
title_short	Low-dose x-ray tomography through a deep convolutional neural network
title_full	Low-dose x-ray tomography through a deep convolutional neural network
title_fullStr	Low-dose x-ray tomography through a deep convolutional neural network
title_full_unstemmed	Low-dose x-ray tomography through a deep convolutional neural network
title_sort	low-dose x-ray tomography through a deep convolutional neural network
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/f2c3eb2baba2489aa424e39aaef3f1eb
work_keys_str_mv	AT xiaogangyang lowdosexraytomographythroughadeepconvolutionalneuralnetwork AT vincentdeandrade lowdosexraytomographythroughadeepconvolutionalneuralnetwork AT williamscullin lowdosexraytomographythroughadeepconvolutionalneuralnetwork AT evaldyer lowdosexraytomographythroughadeepconvolutionalneuralnetwork AT narayanankasthuri lowdosexraytomographythroughadeepconvolutionalneuralnetwork AT francescodecarlo lowdosexraytomographythroughadeepconvolutionalneuralnetwork AT dogagursoy lowdosexraytomographythroughadeepconvolutionalneuralnetwork
_version_	1718388291558965248

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

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