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Title: A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography

Abstract

This paper presents an algorithm to calibrate the center-of-rotation for X-ray tomography by using a machine learning approach, the Convolutional Neural Network (CNN). The algorithm shows excellent accuracy from the evaluation of synthetic data with various noise ratios. It is further validated with experimental data of four different shale samples measured at the Advanced Photon Source and at the Swiss Light Source. The results are as good as those determined by visual inspection and show better robustness than conventional methods. CNN has also great potential forreducing or removingother artifacts caused by instrument instability, detector non-linearity,etc. An open-source toolbox, which integrates the CNN methods described in this paper, is freely available through GitHub at tomography/xlearn and can be easily integrated into existing computational pipelines available at various synchrotron facilities. Source code, documentation and information on how to contribute are also provided.

Authors:
; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1354916
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Synchrotron Radiation (Online); Journal Volume: 24; Journal Issue: 2
Country of Publication:
United States
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY

Citation Formats

Yang, Xiaogang, De Carlo, Francesco, Phatak, Charudatta, and Gürsoy, Dogˇa. A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography. United States: N. p., 2017. Web. doi:10.1107/S1600577516020117.
Yang, Xiaogang, De Carlo, Francesco, Phatak, Charudatta, & Gürsoy, Dogˇa. A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography. United States. doi:10.1107/S1600577516020117.
Yang, Xiaogang, De Carlo, Francesco, Phatak, Charudatta, and Gürsoy, Dogˇa. Tue . "A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography". United States. doi:10.1107/S1600577516020117.
@article{osti_1354916,
title = {A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography},
author = {Yang, Xiaogang and De Carlo, Francesco and Phatak, Charudatta and Gürsoy, Dogˇa},
abstractNote = {This paper presents an algorithm to calibrate the center-of-rotation for X-ray tomography by using a machine learning approach, the Convolutional Neural Network (CNN). The algorithm shows excellent accuracy from the evaluation of synthetic data with various noise ratios. It is further validated with experimental data of four different shale samples measured at the Advanced Photon Source and at the Swiss Light Source. The results are as good as those determined by visual inspection and show better robustness than conventional methods. CNN has also great potential forreducing or removingother artifacts caused by instrument instability, detector non-linearity,etc. An open-source toolbox, which integrates the CNN methods described in this paper, is freely available through GitHub at tomography/xlearn and can be easily integrated into existing computational pipelines available at various synchrotron facilities. Source code, documentation and information on how to contribute are also provided.},
doi = {10.1107/S1600577516020117},
journal = {Journal of Synchrotron Radiation (Online)},
number = 2,
volume = 24,
place = {United States},
year = {Tue Jan 24 00:00:00 EST 2017},
month = {Tue Jan 24 00:00:00 EST 2017}
}