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Research Papers

Motion Compensation for Industrial Computed Tomography

[+] Author and Article Information
Edward Angus

FP Innovations, Inc.,
Vancouver, BC V6T 1Z2, Canada
e-mail: tedangus@gmail.com

Yuntao An

FP Innovations, Inc.,
Vancouver, BC V6T 1Z2, Canada

Gary S. Schajer

Department of Mechanical Engineering,
University of British Columbia,
Vancouver, BC V6T 1Z4, Canada

1Corresponding author.

Manuscript received November 11, 2017; final manuscript received February 26, 2018; published online April 13, 2018. Assoc. Editor: K. Elliott Cramer.

ASME J Nondestructive Evaluation 1(3), 031002 (Apr 13, 2018) (9 pages) Paper No: NDE-17-1107; doi: 10.1115/1.4039691 History: Received November 11, 2017; Revised February 26, 2018

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.

Copyright © 2018 by ASME
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Figures

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Fig. 6

Semi-elliptical Radon transform centroids

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Fig. 5

Radon transform of a uniform cylinder

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Fig. 4

Log motion causing Radon: (a) scaling and (b) shifting

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Fig. 3

Cylindrical rebinning to equal angular pixel rays

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Fig. 2

Polar voxel pattern

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Fig. 1

Source detector geometry

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Fig. 7

Normalization and scaling of Radon transform: (a) measured data, (b) rebinned data, and (c) scaled data

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Fig. 8

Physical phantom: (a,b) hole feature pattern (in), (c) the central 12 “featured” slices, and (d) mounted in the scanner

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Fig. 11

Synthetic radiograph frame with active sampling shown

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Fig. 12

Log precession of 50% FOVradius during object space rotation, log translation to right

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Fig. 13

Degradation of reconstructions with offset: (a) centered log, (b) 1% FOVradius, (c) 5% FOVradius, and (d) 10%FOVradius

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Fig. 14

Error of solution versus FOV offset of the phantom

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Fig. 15

Normalized reconstruction—offset error of 50% field of view

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Fig. 16

Reconstruction of a hemlock log after correction of a 5% offset: (a) Isometric view after correction and (b) before correction

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Fig. 9

Phantom radiograph with active pixel sampling shown in white

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Fig. 10

Phantom complete reconstruction: (a) longitudinal slices detailing feature rows, (b) end-on view, and (c) isometric and end-on view of MDF segmentation

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