A convolutional neural network approach to calibrating the rotation axis for X‐ray computed tomography. (24th January 2017)

• Record Type: Journal Article
• Authors: Yang, Xiaogang; De Carlo, Francesco; Phatak, Charudatta; Gürsoy, Dogˇa
• Is Part Of: Journal of synchrotron radiation. Volume 24:Part 2(2017)
• Page Start: 469
• Page End: 475
• Publication Date: 2017-01-24
• Subjects: tomography reconstruction -- rotation axis -- convolutional neural network -- open‐source -- Python; Synchrotron radiation -- Periodicals; Free electron lasers -- Periodicals; 539.73505
• DOI: 10.1107/S1600577516020117
• Languages: English
• ISSNs: 0909-0495

Abstract

Abstract : Use of convolutional neural networks for automated calibration of rotation axes is described. 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 for reducing or removing other 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.