@article {2323,
	title = {CAD-ASTRA: A versatile and efficient mesh projector for X-ray tomography with the ASTRA-toolbox},
	journal = {Optics Express},
	volume = {32},
	year = {2024},
	pages = {3425-3439},
	doi = {https://doi.org/10.1364/OE.498194},
	author = {Pavel Paramonov and Francken, Nicholas and Jens Renders and Domenico Iuso and Tim Elberfeld and Jan De Beenhouwer and Jan Sijbers}
}
@mastersthesis {2290,
	title = {Parametric Fiber Analysis for Glass Fiber-reinforced Composite Tomographic Images},
	year = {2023},
	type = {PhD thesis},
	author = {Tim Elberfeld}
}
@article {2276,
	title = {cuPARE: Parametric Reconstruction of Curved Fibres from Glass fibre-reinforced Composites},
	journal = {Nondestructive Testing and Evaluation},
	year = {2022},
	doi = {10.1080/10589759.2022.2155647},
	author = {Tim Elberfeld and Bernhard Fr{\"o}hler and Christoph Heinzl and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2240,
	title = {Efficient X-ray projection of triangular meshes based on ray tracing and rasterization},
	booktitle = {SPIE Optical Engineering: Developments in X-Ray Tomography XIV },
	volume = {12242},
	year = {2022},
	pages = {122420W },
	doi = {https://doi.org/10.1117/12.2633448},
	author = {Pavel Paramonov and Jens Renders and Tim Elberfeld and Jan De Beenhouwer and Jan Sijbers}
}
@article {2092,
	title = {FleXCT: a Flexible X-ray CT scanner with 10 degrees of freedom},
	journal = {Optics Express},
	volume = {29},
	year = {2021},
	pages = {3438-3457},
	doi = {https://doi.org/10.1364/OE.409982},
	author = {Bjorn De Samber and Jens Renders and Tim Elberfeld and Yves Maris and Jonathan Sanctorum and Nathana{\"e}l Six and Liang, Zhihua and Jan De Beenhouwer and Jan Sijbers}
}
@article {2057,
	title = {Analysis and Comparison of Algorithms for the Tomographic Reconstruction of Curved Fibres},
	journal = {Nondestructive Testing and Evaluation},
	volume = {35},
	year = {2020},
	pages = {328-341},
	doi = {https://doi.org/10.1080/10589759.2020.1774583},
	author = {Bernhard Fr{\"o}hler and Tim Elberfeld and Torsten M{\"o}ller and Hans-Christian Hege and Jan De Beenhouwer and Jan Sijbers and Johann Kastner and Christoph Heinzl}
}
@inproceedings {1946,
	title = {Fiber assignment by continuous tracking for parametric fiber reinforced polymer reconstruction},
	booktitle = {15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D)},
	volume = {11072},
	year = {2019},
	doi = {http://dx.doi.org/10.1117/12.2534836},
	author = {Tim Elberfeld and Jan De Beenhouwer and Jan Sijbers}
}
@conference {1952,
	title = {Mixed-Scale Dense Convolutional Neural Network based Improvement of Glass Fiber-reinforced Composite CT Images},
	year = {2019},
	month = {07/2019},
	abstract = {For the study of glass fiber-reinforced polymers (GFRP), {\textmu}CT is the method of choice. Obtaining
GFRP parameters from a {\textmu}CT scan is difficult, due to the presence of noise and artifacts. We propose a method
to improve GFRP image quality using a recently introduced deep neural network. We describe the network{\textquoteright}s
setup and the data generation and show how the trained network improves the reconstruction.},
	author = {Tim Elberfeld and Shabab Bazrafkan and Jan De Beenhouwer and Jan Sijbers}
}
@article {1923,
	title = {A Visual Tool for the Analysis of Algorithms for Tomographic Fiber Reconstruction in Materials Science},
	journal = {Computer Graphics Forum},
	volume = {38},
	year = {2019},
	pages = {273-283},
	doi = {10.1111/cgf.13688},
	author = {Bernhard Fr{\"o}hler and Tim Elberfeld and Torsten M{\"o}ller and Johannes Weissenb{\"o}ck and Jan De Beenhouwer and Jan Sijbers and Hans-Christian Hege and Johann Kastner and Christoph Heinzl}
}
@conference {1847,
	title = {Parametric Reconstruction of Advanced Glass Fiber-reinforced Polymer Composites from X-ray Images},
	year = {2018},
	address = {Wels, Austria},
	abstract = {A novel approach to the reconstruction of glass fiber-reinforced polymers (GFRP) from X-ray micro-computed tomography
(μCT) data is presented. The traditional fiber analysis workflow requires complete sample reconstruction, pre-processing and
segmentation, followed by the analysis of fiber distribution, orientation, and other features of interest. Each step in the chain
introduces errors that propagate through the pipeline and impair the accuracy of the estimation of those features. In the
approach presented in this paper, we combine iterative reconstruction techniques and a priori knowledge about the sample, to
reconstruct the volume and estimate the orientation of the fibers simultaneously. Fibers are modeled using rigid cylinders in
space whose orientation and position is then iteratively refined. The output of the algorithm is a non voxel-based dataset of the
fibers{\textquoteright} parametric representation, allowing to directly assess fiber features and distribution characteristics and to simulate the resulting material properties.},
	keywords = {GFRP, Materials Science, Modeling of Microstructures, Parametric Reconstruction, Tomography, {\textmu}CT},
	author = {Tim Elberfeld and Jan De Beenhouwer and Arnold Jan den Dekker and Christoph Heinzl and Jan Sijbers}
}
@article {1859,
	title = {Parametric Reconstruction of Glass Fiber-reinforced Polymer Composites from X-ray Projection Data - A Simulation Study},
	journal = {Journal of Nondestructive Evaluation},
	volume = {37},
	year = {2018},
	month = {Jul},
	pages = {1573-4862},
	abstract = {We present a new approach to estimate geometry parameters of glass fibers in glass fiber-reinforced polymers from simulated X-ray micro-computed tomography scans. Traditionally, these parameters are estimated using a multi-step procedure including image reconstruction, pre-processing, segmentation and analysis of features of interest. Each step in this chain introduces errors that propagate through the pipeline and impair the accuracy of the estimated parameters. In the approach presented in this paper, we reconstruct volumes from a low number of projection angles using an iterative reconstruction technique and then estimate position, direction and length of the contained fibers incorporating a priori knowledge about their shape, modeled as a geometric representation, which is then optimized. Using simulation experiments, we show that our method can estimate those representations even in presence of noisy data and only very few projection angles available.},
	keywords = {GFRP, Glass fiber reinforced polymer, Materials Science, Modeling of micro-structures, Parametric model, Tomography, {\textmu}CT},
	doi = {10.1007/s10921-018-0514-0},
	url = {https://doi.org/10.1007/s10921-018-0514-0},
	author = {Tim Elberfeld and Jan De Beenhouwer and Arnold Jan den Dekker and Christoph Heinzl and Jan Sijbers}
}
