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Neural network Hilbert transform based filtered backprojection for fast inline x-ray inspection

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Abstract
X-ray imaging is an important tool for quality control since it allows to inspect the interior of products in a non-destructive way. Conventional x-ray imaging, however, is slow and expensive. Inline x-ray inspection, on the other hand, can pave the way towards fast and individual quality control, provided that a sufficiently high throughput can be achieved at a minimal cost. To meet these criteria, an inline inspection acquisition geometry is proposed where the object moves and rotates on a conveyor belt while it passes a fixed source and detector. Moreover, for this acquisition geometry, a new neural-network-based reconstruction algorithm is introduced: the neural network Hilbert transform based filtered backprojection. The proposed algorithm is evaluated both on simulated and real inline x-ray data and has shown to generate high quality reconstructions of 400 x 400 reconstruction pixels within 200 ms, thereby meeting the high throughput criteria.
Keywords
inline inspection, x-ray tomography, filtered backprojection, ELECTRON TOMOGRAPHY, ASTRA TOOLBOX, RECONSTRUCTION, ALGORITHM

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Chicago
Janssens, Eline, Jan De Beenhouwer, Mattias Van Dael, Thomas De Schryver, Luc Van Hoorebeke, Pieter Verboven, Bart Nicolai, and Jan Sijbers. 2018. “Neural Network Hilbert Transform Based Filtered Backprojection for Fast Inline X-ray Inspection.” Measurement Science and Technology 29 (3).
APA
Janssens, Eline, De Beenhouwer, J., Van Dael, M., De Schryver, T., Van Hoorebeke, L., Verboven, P., Nicolai, B., et al. (2018). Neural network Hilbert transform based filtered backprojection for fast inline x-ray inspection. MEASUREMENT SCIENCE AND TECHNOLOGY, 29(3).
Vancouver
1.
Janssens E, De Beenhouwer J, Van Dael M, De Schryver T, Van Hoorebeke L, Verboven P, et al. Neural network Hilbert transform based filtered backprojection for fast inline x-ray inspection. MEASUREMENT SCIENCE AND TECHNOLOGY. 2018;29(3).
MLA
Janssens, Eline, Jan De Beenhouwer, Mattias Van Dael, et al. “Neural Network Hilbert Transform Based Filtered Backprojection for Fast Inline X-ray Inspection.” MEASUREMENT SCIENCE AND TECHNOLOGY 29.3 (2018): n. pag. Print.
@article{8551475,
  abstract     = {X-ray imaging is an important tool for quality control since it allows to inspect the interior of products in a non-destructive way. Conventional x-ray imaging, however, is slow and expensive. Inline x-ray inspection, on the other hand, can pave the way towards fast and individual quality control, provided that a sufficiently high throughput can be achieved at a minimal cost. To meet these criteria, an inline inspection acquisition geometry is proposed where the object moves and rotates on a conveyor belt while it passes a fixed source and detector. Moreover, for this acquisition geometry, a new neural-network-based reconstruction algorithm is introduced: the neural network Hilbert transform based filtered backprojection. The proposed algorithm is evaluated both on simulated and real inline x-ray data and has shown to generate high quality reconstructions of 400 x 400 reconstruction pixels within 200 ms, thereby meeting the high throughput criteria.},
  articleno    = {034012},
  author       = {Janssens, Eline and De Beenhouwer, Jan and Van Dael, Mattias and De Schryver, Thomas and Van Hoorebeke, Luc and Verboven, Pieter and Nicolai, Bart and Sijbers, Jan},
  issn         = {0957-0233},
  journal      = {MEASUREMENT SCIENCE AND TECHNOLOGY},
  keyword      = {inline inspection,x-ray tomography,filtered backprojection,ELECTRON TOMOGRAPHY,ASTRA TOOLBOX,RECONSTRUCTION,ALGORITHM},
  language     = {eng},
  number       = {3},
  pages        = {12},
  title        = {Neural network Hilbert transform based filtered backprojection for fast inline x-ray inspection},
  url          = {http://dx.doi.org/10.1088/1361-6501/aa9de3},
  volume       = {29},
  year         = {2018},
}

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