X-ray computed tomography (CT) is a powerful tool for industrial inspection. However, the harsh conditions encountered in some production environments make accurate motion control difficult, leading to motion artifacts in CT applications. A technique is demonstrated that removes motion artifacts by using an iterative-solver CT reconstruction method that includes a bulk Radon transform shifting step to align radiographic data before reconstruction. The paper uses log scanning in a sawmill as an example application. We show how for a known nominal object density distribution (circular prismatic in the case of a log), the geometric center and radius of the log may be approximated from its radiographs and any motion compensated for. This may then be fed into a previously developed iterative reconstruction CT scheme based on a polar voxel geometry and useful for describing logs. The method is validated by taking the known density distribution of a physical phantom and producing synthetic radiographs in which the axis of object rotation does not coincide with the center of field of view for a hypothetical scanner geometry. Reconstructions could then be made on radiographs that had been corrected and compared to those that had not. This was done for progressively larger offsets between these two axes and the reduction in voxel density vector error studied. For CT applications in industrial settings in which precise motion control is impractical or too costly, radiographic data shifting and scaling based on predictive models for the Radon transform appears to be a simple but effective technique.
Motion Compensation for Industrial Computed Tomography
Vancouver, BC V6T 1Z2, Canada
University of British Columbia,
Vancouver, BC V6T 1Z4, Canada
Manuscript received November 11, 2017; final manuscript received February 26, 2018; published online April 13, 2018. Assoc. Editor: K. Elliott Cramer.
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Angus, E., An, Y., and Schajer, G. S. (April 13, 2018). "Motion Compensation for Industrial Computed Tomography." ASME. ASME J Nondestructive Evaluation. August 2018; 1(3): 031002. https://doi.org/10.1115/1.4039691
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