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 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.
authors.affiliation: X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA.
authors.forename: Xiaogang
authors.initials: X
authors.lastname: Yang
authors.affiliation: X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA.
authors.forename: Francesco
authors.initials: F
authors.lastname: De Carlo
authors.affiliation: Materials Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA.
authors.forename: Charudatta
authors.initials: C
authors.lastname: Phatak
authors.affiliation: X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA.
authors.forename: Dogˇa
authors.initials: D
authors.lastname: Gürsoy
keywords: Python; convolutional neural network; open-source; rotation axis; tomography reconstruction