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Piecewise linear fitting in dynamic micro-CT

(2018) MATERIALS CHARACTERIZATION. 139. p.259-268
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Abstract
Piecewise linear fitting, the technique proposed in this paper, performs data reduction on a large dynamic CT dataset and it already takes a step in the direction of the data analysis and characterization that needs to be performed afterwards. In addition, it drastically improves the signal-to-noise ratio. This is demonstrated on two complementary samples: a Bentheimer sandstone and a pharmaceutical tablet. This technique is developed for dynamic high-resolution CT scanning or 4D-mu CT, a tool to study dynamic processes in situ on the micro-scale. We propose to start from the low quality reconstruction and perform a piecewise linear fit in the time direction for each voxel. This effectively uses the nearby temporal information, regardless of the nature of the dynamic process, without introducing spatial correlation.
Keywords
X-ray micro-computed tomography, Analysis, Image processing, Reconstruction, Signal-to-noise ratio, X-RAY TOMOGRAPHY, COMPUTED-TOMOGRAPHY, RECONSTRUCTION ALGORITHM, IMAGE-RECONSTRUCTION, PORE, DIFFUSION, ROCK, PET

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MLA
Heyndrickx, Marjolein, et al. “Piecewise Linear Fitting in Dynamic Micro-CT.” MATERIALS CHARACTERIZATION, vol. 139, 2018, pp. 259–68, doi:10.1016/j.matchar.2018.03.007.
APA
Heyndrickx, M., Boone, M., De Schryver, T., Bultreys, T., Goethals, W., Verstraete, G., … Van Hoorebeke, L. (2018). Piecewise linear fitting in dynamic micro-CT. MATERIALS CHARACTERIZATION, 139, 259–268. https://doi.org/10.1016/j.matchar.2018.03.007
Chicago author-date
Heyndrickx, Marjolein, Matthieu Boone, Thomas De Schryver, Tom Bultreys, Wannes Goethals, Glenn Verstraete, Valérie Vanhoorne, and Luc Van Hoorebeke. 2018. “Piecewise Linear Fitting in Dynamic Micro-CT.” MATERIALS CHARACTERIZATION 139: 259–68. https://doi.org/10.1016/j.matchar.2018.03.007.
Chicago author-date (all authors)
Heyndrickx, Marjolein, Matthieu Boone, Thomas De Schryver, Tom Bultreys, Wannes Goethals, Glenn Verstraete, Valérie Vanhoorne, and Luc Van Hoorebeke. 2018. “Piecewise Linear Fitting in Dynamic Micro-CT.” MATERIALS CHARACTERIZATION 139: 259–268. doi:10.1016/j.matchar.2018.03.007.
Vancouver
1.
Heyndrickx M, Boone M, De Schryver T, Bultreys T, Goethals W, Verstraete G, et al. Piecewise linear fitting in dynamic micro-CT. MATERIALS CHARACTERIZATION. 2018;139:259–68.
IEEE
[1]
M. Heyndrickx et al., “Piecewise linear fitting in dynamic micro-CT,” MATERIALS CHARACTERIZATION, vol. 139, pp. 259–268, 2018.
@article{8556144,
  abstract     = {{Piecewise linear fitting, the technique proposed in this paper, performs data reduction on a large dynamic CT dataset and it already takes a step in the direction of the data analysis and characterization that needs to be performed afterwards. In addition, it drastically improves the signal-to-noise ratio. This is demonstrated on two complementary samples: a Bentheimer sandstone and a pharmaceutical tablet. 
This technique is developed for dynamic high-resolution CT scanning or 4D-mu CT, a tool to study dynamic processes in situ on the micro-scale. We propose to start from the low quality reconstruction and perform a piecewise linear fit in the time direction for each voxel. This effectively uses the nearby temporal information, regardless of the nature of the dynamic process, without introducing spatial correlation.}},
  author       = {{Heyndrickx, Marjolein and Boone, Matthieu and De Schryver, Thomas and Bultreys, Tom and Goethals, Wannes and Verstraete, Glenn and Vanhoorne, Valérie and Van Hoorebeke, Luc}},
  issn         = {{1044-5803}},
  journal      = {{MATERIALS CHARACTERIZATION}},
  keywords     = {{X-ray micro-computed tomography,Analysis,Image processing,Reconstruction,Signal-to-noise ratio,X-RAY TOMOGRAPHY,COMPUTED-TOMOGRAPHY,RECONSTRUCTION ALGORITHM,IMAGE-RECONSTRUCTION,PORE,DIFFUSION,ROCK,PET}},
  language     = {{eng}},
  pages        = {{259--268}},
  title        = {{Piecewise linear fitting in dynamic micro-CT}},
  url          = {{http://doi.org/10.1016/j.matchar.2018.03.007}},
  volume       = {{139}},
  year         = {{2018}},
}

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