@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 Iuso, Domenico and Tim Elberfeld and Jan De Beenhouwer and Jan Sijbers} } @article {2329, title = {Edge illumination x-ray phase contrast simulations using the CAD-ASTRA toolbox}, journal = {Optics Express}, volume = {32}, year = {2024}, pages = {10005-10021}, doi = {10.1364/OE.516138}, url = {https://opg.optica.org/oe/abstract.cfm?doi=10.1364/OE.516138}, author = {Francken, Nicholas and Jonathan Sanctorum and Pavel Paramonov and Sijbers, Jan and Jan De Beenhouwer} } @mastersthesis {2326, title = {Improved X-ray CT reconstruction techniques with non-linear imaging models}, year = {2024}, type = {PhD thesis}, author = {Nathana{\"e}l Six} } @article {2321, title = {Joint multi-contrast CT for edge illumination X-ray phase contrast imaging using split Barzilai-Borwein steps}, journal = {Optics Express}, volume = {32}, year = {2024}, pages = {1135-1150}, doi = {https://doi.org/10.1364/OE.502542}, author = {Nathana{\"e}l Six and Jens Renders and Jan De Beenhouwer and Jan Sijbers} } @unpublished {2328, title = {MIRT: a simultaneous reconstruction and affine motion compensation technique for four dimensional computed tomography (4DCT)}, year = {2024}, publisher = {arXiv.org e-Print archive}, doi = {http://dx.doi.org/10.48550/arXiv.2402.04480}, author = {Anh-Tuan Nguyen and Jens Renders and Domenico Iuso and Yves Maris and Jeroen Soete and Martine Wevers and Jan Sijbers and Jan De Beenhouwer} } @article {2295, title = {Adapting an XCT-scanner to enable edge illumination X-ray phase contrast imaging}, journal = {e-Journal of Nondestructive Testing}, volume = {28}, year = {2023}, issn = {1435-4934}, doi = {doi.org/10.58286/27755}, author = {Ben Huyge and Pieter-Jan Vanthienen and Nathana{\"e}l Six and Jan Sijbers and Jan De Beenhouwer} } @inproceedings {2287, title = {A condensed history approach to x-ray dark field effects in edge illumination phase contrast simulations}, booktitle = {45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, year = {2023}, author = {Francken, Nicholas and Jonathan Sanctorum and Jens Renders and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer} } @inproceedings {2292, title = {The Deep Steerable Convolutional Framelet Network for Suppressing Directional Artifacts in X-ray Tomosynthesis}, booktitle = {31st European Signal Processing Conference, EUSIPCO}, year = {2023}, author = {Luis Filipe Alves Pereira and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {2283, title = {DELTA-MRI: Direct deformation Estimation from LongiTudinally Acquired k-space data}, booktitle = {IEEE International Symposium on Biomedical Imaging}, year = {2023}, doi = {10.1109/ISBI53787.2023.10230697}, author = {Jens Renders and Banafshe Shafieizargar and Marleen Verhoye and Jan De Beenhouwer and Arnold Jan den Dekker and Jan Sijbers} } @article {2305, title = {Efficient iterative reconstruction with beam shape compensation for THz computed tomography}, journal = {Applied Optics}, volume = {62}, year = {2023}, month = {April 14, 2023}, pages = {F31-F40}, chapter = {F31}, abstract = {Terahertz (THz) computed tomography is an emerging nondestructive and non-ionizing imaging method. Most THz reconstruction methods rely on the Radon transform, originating from x-ray imaging, in which x rays propagate in straight lines. However, a THz beam has a finite width, and ignoring its shape results in blurred reconstructed images. Moreover, accounting for the THz beam model in a straightforward way in an iterative reconstruction method results in extreme demands in memory and in slow convergence. In this paper, we propose an efficient iterative reconstruction that incorporates the THz beam shape, while avoiding the above disadvantages. Both simulation and real experiments show that our approach results in improved resolution recovery in the reconstructed image. Furthermore, we propose a suitable preconditioner to improve the convergence speed of our reconstruction.}, keywords = {Beam shaping, computed tomography, Image quality, image reconstruction, THz imaging}, doi = {https://doi.org/10.1364/AO.482511}, url = {https://opg.optica.org/viewmedia.cfm?r=1\&rwjcode=ao\&uri=ao-62-17-F31\&seq=0}, author = {Lars-Paul Lumbeeck and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer} } @inproceedings {2299, title = {Enhancing industrial inspection with efficient edge illumination x-ray phase contrast simulations}, booktitle = {IEEE EUROCON 2023 -20th International Conference on Smart Technologies, Torino, Italy}, year = {2023}, author = {Francken, Nicholas and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer} } @article {2264, title = {Fast and accurate pose estimation of additive manufactured objects from few X-ray projections}, journal = {Expert Systems With Applications}, volume = {213}, year = {2023}, pages = {1-10}, doi = {https://doi.org/10.1016/j.eswa.2022.118866}, author = {Alice Presenti and Liang, Zhihua and Luis Filipe Alves Pereira and Jan Sijbers and Jan De Beenhouwer} } @article {2298, title = {Fiber Orientation Estimation from X-ray Dark Field Images of Fiber Reinforced Polymers using Constrained Spherical Deconvolution}, journal = {Polymers}, volume = {15}, year = {2023}, pages = {2887}, doi = {https://doi.org/10.3390/polym15132887}, author = {Ben Huyge and Jonathan Sanctorum and Ben Jeurissen and Jan De Beenhouwer and Jan Sijbers} } @article {2301, title = {Grating designs for cone beam edge illumination X-ray phase contrast imaging: a simulation study}, journal = {Optics Express}, volume = {31}, year = {2023}, pages = {28051-28064}, abstract = {Edge illumination is an emerging X-ray phase contrast imaging technique providing attenuation, phase and dark field contrast. Despite the successful transition from synchrotron to lab sources, the cone beam geometry of lab systems limits the effectiveness of using conventional planar gratings. The non-parallel incidence of X-rays introduces shadowing effects, worsening with increasing cone angle. To overcome this limitation, several alternative grating designs can be considered. In this paper, the effectiveness of three alternative designs is compared to conventional gratings using numerical simulations. Improvements in flux and contrast are discussed, taking into account practical considerations concerning the implementation of the designs.}, doi = {10.1364/OE.495789}, url = {https://opg.optica.org/oe/fulltext.cfm?uri=oe-31-17-28051\&id=536127}, author = {Pieter-Jan Vanthienen and Jonathan Sanctorum and Ben Huyge and Nathana{\"e}l Six and Jan Sijbers and Jan De Beenhouwer} } @article {2304, title = {ImWIP: open-source image warping toolbox with adjoints and derivatives}, journal = {SoftwareX}, volume = {24}, year = {2023}, pages = {101524}, doi = {https://doi.org/10.1016/j.softx.2023.101524}, author = {Jens Renders and Ben Jeurissen and Anh-Tuan Nguyen 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 {2306, title = {A reconstruction method for atom probe tomography}, number = {18017575}, year = {2023}, edition = {US20230307207A1}, chapter = {US Patent App. 18/017,575, 2023}, author = {Jan Sijbers and Jan De Beenhouwer and Yu-Ting Ling and Wilfried Vandervorst} } @inproceedings {2279, title = {Region-based motion-compensated iterative reconstruction technique for dynamic computed tomography}, booktitle = {IEEE International Symposium on Biomedical Imaging (ISBI)}, year = {2023}, address = {Cartagena de Indias, Colombia}, doi = {https://doi.org/10.1109/ISBI53787.2023.10230608}, author = {Anh-Tuan Nguyen and Jens Renders and Jan Sijbers and Jan De Beenhouwer} } @inproceedings {2281, title = {Sparse-view Medical Tomosynthesis via Mixed Scale Dense Convolutional Framelet Networks}, booktitle = {IEEE International Symposium on Biomedical Imaging (ISBI)}, year = {2023}, pages = {880-884}, doi = { https://doi.org/10.23919/EUSIPCO58844.2023.10289781}, author = {Luis Filipe Alves Pereira and Jan De Beenhouwer and Jan Sijbers} } @article {2255, title = {Tabu-DART: A dynamic update strategy for efficient discrete algebraic reconstruction}, journal = {The Visual Computer}, volume = {39}, year = {2023}, pages = {4671{\textendash}4683}, doi = {10.1007/s00371-022-02616-w}, author = {Daniel Frenkel and Nathana{\"e}l Six and Jan De Beenhouwer and Jan Sijbers} } @article {2294, title = {Toward denoising of 3D CT scans with few data}, journal = {e-Journal of Nondestructive Testing}, volume = {28}, year = {2023}, issn = {1435-4934}, doi = {doi.org/10.58286/27741}, author = {Liang, Zhihua and Anneke Van Heteren and Sijbers, Jan and Jan De Beenhouwer} } @article {2204, title = {3D total variation denoising in X-CT imaging applied to pore extraction in additively manufactured parts}, journal = {Measurement Science and Technology}, volume = {33}, year = {2022}, pages = {1-12}, doi = {10.1088/1361-6501/ac459a}, author = {Rob Heylen and Aditi Thanki and Dries Verhees and Domenico Iuso and Jan De Beenhouwer and Jan Sijbers and Ann Witvrouw and Han Haitjema and Abdellatif Bey-Temsamani} } @mastersthesis {2280, title = {3D X-ray radiography-based inspection of manufactured objects}, volume = {PhD}, year = {2022}, type = {PhD thesis}, author = {Alice Presenti} } @inproceedings {2239, title = {An accelerated motion-compensated iterative reconstruction technique for dynamic computed tomography}, booktitle = {Proc. SPIE 12242, Developments in X-Ray Tomography XIV, 122421F}, year = {2022}, address = {San Diego, CA, United States}, doi = {https://doi.org/10.1117/12.2631570}, author = {Anh-Tuan Nguyen and Jens Renders and Jeroen Soete and Martine Wevers and Jan Sijbers and Jan De Beenhouwer} } @conference {2268, title = {Adaptive triangular mesh for phase contrast imaging}, year = {2022}, url = {https://ictms2022.sciencesconf.org/385325/document}, author = {Jannes Merckx and Bart van Lith and Jan Sijbers and Jan De Beenhouwer} } @mastersthesis {2278, title = {Advances in biplanar X-ray imaging: calibration and 2D/3D registration}, volume = {PhD}, year = {2022}, type = {PhD thesis}, author = {Van Nguyen} } @inproceedings {2262, title = {Alternative grating designs for cone-beam edge illumination X-ray phase contrast imaging}, booktitle = { Proc. SPIE 12242, Developments in X-Ray Tomography XIV}, volume = {12242}, year = {2022}, month = {10/2022}, pages = {122420Z}, publisher = {SPIE}, organization = {SPIE}, address = {San Diego, USA}, doi = {10.1117/12.2632301}, author = {Pieter-Jan Vanthienen and Jonathan Sanctorum and Ben Huyge and Nathana{\"e}l Six and Jan Sijbers and Jan De Beenhouwer} } @inproceedings {2192, title = {Analytic derivatives of scaling motion-compensated projection operators for dynamic computed tomography}, booktitle = {e-Journal of Nondestructive Testing}, volume = { 27 (3)}, year = {2022}, pages = {1-5}, doi = {https://doi.org/10.58286/26588}, url = {https://www.ndt.net/search/docs.php3?id=26588}, author = {Anh-Tuan Nguyen and Jens Renders and Jeroen Soete and Martine Wevers and Jan Sijbers and Jan De Beenhouwer} } @inproceedings {2237, title = {Augmenting a conventional X-ray scanner with edge illumination based phase contrast imaging: how to design the gratings?}, booktitle = {Proc. SPIE 12242, Developments in X-Ray Tomography XIV}, year = {2022}, month = {10/2022}, pages = {1224218}, publisher = {SPIE}, organization = {SPIE}, address = {San Diego, USA}, doi = {10.1117/12.2633455}, author = {Jonathan Sanctorum and Nathana{\"e}l Six and Jan Sijbers and Jan De Beenhouwer} } @article {2236, title = {Automatic anomaly detection from X-ray images based on autoencoder}, journal = {Nondestructive Testing and Evaluation}, volume = {37}, year = {2022}, doi = {10.1080/10589759.2022.2074415}, author = {Alice Presenti and Zhihua Liang and Luis Filipe Alves Pereira and Jan Sijbers and Jan De Beenhouwer} } @article {2257, title = {Automatic landmark detection and mapping for 2D/3D registration with BoneNet}, journal = {Frontiers Veterinary Science}, year = {2022}, doi = {https://doi.org/10.3389/fvets.2022.923449}, author = {Van Nguyen and Luis Filipe Alves Pereira and Liang, Zhihua and Falk Mielke and Jeroen Van Houtte and Jan Sijbers and Jan De Beenhouwer} } @article {2212, title = {A Bottom-Up Volume Reconstruction Method for Atom Probe Tomography}, journal = {Microscopy and Microanalysis}, volume = {28}, year = {2022}, pages = {1-14}, doi = {10.1017/S1431927621012836}, author = {Yu-Ting Ling and Siegfried Cools and Janusz Bogdanowicz and Claudia Fleischmann and Jan De Beenhouwer and Jan Sijbers and Wilfried Vandervorst} } @inproceedings {2194, title = {CNN-based pose estimation from a single X-ray projection for 3D inspection of manufactured objects}, booktitle = {11th Conference on Industrial Computed Tomography}, year = {2022}, author = {Alice Presenti and Zhihua Liang and Luis Filipe Alves Pereira and Jan Sijbers and Jan De Beenhouwer} } @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} } @article {2193, title = {Discrete Terahertz tomography: a simulation study}, journal = {e-Journal of Nondestructive Testing}, volume = {27}, year = {2022}, issn = {1435-4934}, doi = {doi.org/10.58286/26608}, author = {Jana Christopher and Lars-Paul Lumbeeck and Pavel Paramonov and Jan De Beenhouwer and Jan Sijbers} } @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} } @inproceedings {2243, title = {Evaluation of deeply supervised neural networks for 3D pore segmentation in additive manufacturing}, booktitle = {SPIE Optical Engineering: Developments in X-Ray Tomography XIV }, volume = {12242}, year = {2022}, pages = {122421K}, doi = {https://doi.org/10.1117/12.2633318}, author = {Domenico Iuso and Soumick Chatterjee and Rob Heylen and Sven Cornelissen and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {2272, title = {Fiber orientation estimation by constrained spherical deconvolution of the anisotropic edge illumination x-ray dark field signal}, booktitle = {SPIE: Developments in X-Ray Tomography XIV}, volume = {12242}, year = {2022}, pages = {122420V }, doi = {ttps://doi.org/10.1117/12.2633482}, author = {Ben Huyge and Ben Jeurissen and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {2242, title = {Joint reconstruction of attenuation, refraction and dark field X-ray phase contrasts using split Barzilai-Borwein steps}, booktitle = {SPIE Optical Engineering: Developments in X-Ray Tomography XIV }, volume = {12242}, year = {2022}, pages = {122420O}, doi = {https://doi.org/10.1117/12.2633587}, author = {Nathana{\"e}l Six and Jens Renders and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {2241, title = {Optimization of a multi-source rectangular X-ray CT geometry for inline inspection}, booktitle = {SPIE Optical Engineering: Developments in X-Ray Tomography XIV }, volume = {12242}, year = {2022}, pages = {1224219 }, doi = {https://doi.org/10.1117/12.2633523}, author = {Caroline Bossuyt and Jan De Beenhouwer and Jan Sijbers} } @article {2246, title = {Probability of Detection applied to X-ray inspection using numerical simulations}, journal = {Nondestructive Testing and Evaluation}, volume = {37}, year = {2022}, pages = {536-551}, doi = {10.1080/10589759.2022.2071892}, author = {Miroslav Yosifov and M. Reiter and S. Heupl and C. Gusenbauer and Bernhard Fr{\"o}hler and R. Fernandez- Gutierrez and Jan De Beenhouwer and Jan Sijbers and Johann Kastner and Christoph Heinzl} } @conference {2217, title = {Sparse view rectangular X-ray CT for cargo inspection using the ASTRA toolbox}, year = {2022}, author = {Caroline Bossuyt and Jan De Beenhouwer and Jan Sijbers} } @article {2249, title = {The sqstm1tmΔUBA zebrafish model, a proof-of-concept in vivo model for Paget{\textquoteright}s disease of bone?}, journal = {Bone Reports}, volume = {16}, year = {2022}, pages = {75-76}, doi = {10.1016/j.bonr.2022.101483}, author = {Yentl Huybrechts and Rapha{\"e}l De Ridder and Bjorn De Samber and Eveline Boudin and Francesca Tonelli and Dries Knapen and Dorien Schepers and Jan De Beenhouwer and Jan Sijbers and Antonella Forlino and Paul Coucke and P. Eckhard Witten and Ronald Kwon and Andy Willaert and Gretl Hendrickx and Wim Van Hul} } @article {2263, title = {Virtual grating approach for Monte Carlo simulations of edge illumination-based x-ray phase contrast imaging}, journal = {Optics Express}, volume = {31}, year = {2022}, pages = {38695-38708}, doi = {https://doi.org/10.1364/OE.472145}, author = {Jonathan Sanctorum and Jan Sijbers and Jan De Beenhouwer} } @inproceedings {2186, title = {3D THz Tomography Incorporating the Beam Shape}, booktitle = {2021 OSA Imaging and Applied Optics Congress}, year = {2021}, doi = {10.1364/AIS.2021.JTu5A.36}, author = {Lars-Paul Lumbeeck and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer} } @article {2144, title = {Adjoint image warping using multivariate splines with application to 4D-CT}, journal = {Medical Physics}, volume = {48}, year = {2021}, pages = {6362-6374}, doi = {10.1002/mp.14765}, author = {Jens Renders and Jan Sijbers and Jan De Beenhouwer} } @inproceedings {2098, title = {Analysis of flat fields in edge illumination phase contrast imaging}, booktitle = {2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)}, year = {2021}, pages = {1310-1313}, publisher = {IEEE}, organization = {IEEE}, address = {Nice, France}, doi = {10.1109/ISBI48211.2021.9433849}, url = {https://ieeexplore.ieee.org/document/9433849}, author = {Ben Huyge and Jonathan Sanctorum and Nathana{\"e}l Six and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {2172, title = {CAD-based scatter compensation for polychromatic reconstruction of additive manufactured parts}, booktitle = {IEEE ICIP}, year = {2021}, pages = {2948-2952}, doi = {10.1109/ICIP42928.2021.9506536}, author = {Domenico Iuso and Ehsan Nazemi and Nathana{\"e}l Six and Bjorn De Samber and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {2201, title = {CNN-based Pose Estimation of Manufactured Objects During Inline X-ray Inspection}, booktitle = {2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI)}, year = {2021}, abstract = {X-ray Computed Tomography (CT) is a nondestructive technique widely used for inspection of manufactured objects generated from a reference computer-aided-design (CAD) representation. In a conventional CT inspection framework, a volumetric reconstruction is computed from a large number of X-ray projections of the object. Afterwards, a surface is extracted, aligned and compared to the CAD model. For an accurate comparison, a high-resolution reconstruction is needed, requiring hundreds of projections, making this procedure not suitable for real-time inspection. In contrast to CT-based inspection, radiograph-based inspection only requires a few radiographs that then can be compared with simulated projections from the reference CAD model. For an effective comparison, however, an accurate 3D pose estimation of the object and consequent alignment between the measured object and the reference model are crucial. In this paper, we present an inline projection-based 3D pose estimation framework using convolutional neural networks (CNNs). Through realistic simulation experiments, we show that, with only two projections, estimation of the pose of the object is possible at the resolution of the acquisition system.}, author = {Alice Presenti and Zhihua Liang and Luis Filipe Alves Pereira and Jan Sijbers and Jan De Beenhouwer} } @inproceedings {2099, title = {Dark field sensitivity in single mask edge illumination lung imaging}, booktitle = {2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)}, year = {2021}, pages = {775-778}, publisher = {IEEE}, organization = {IEEE}, address = {Nice, France}, doi = {10.1109/ISBI48211.2021.9434024}, url = {https://ieeexplore.ieee.org/document/9434024}, author = {Jonathan Sanctorum and Jan Sijbers and Jan De Beenhouwer} } @inproceedings {2158, title = {A Deep Convolutional Framelet Network based on Tight Steerable Wavelet: application to sparse-view medical tomosynthesis}, booktitle = { International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine}, year = {2021}, author = {Luis Filipe Alves Pereira and Vincent Van Nieuwenhove and Jan De Beenhouwer and Jan Sijbers} } @article {2171, title = {Dynamic few-view X-ray imaging for inspection of CAD-based objects}, journal = {Expert Systems with Applications}, volume = {180}, year = {2021}, pages = {115012}, keywords = {CAD, Dynamic acquisition, Few-view inspection, Pose estimation, Visibility angles}, issn = {0957-4174}, doi = {https://doi.org/10.1016/j.eswa.2021.115012}, url = {https://www.sciencedirect.com/science/article/pii/S095741742100453X}, author = {Alice Presenti and Jan Sijbers and Jan De Beenhouwer} } @article {2188, title = {Extended imaging volume in cone-beam x-ray tomography using the weighted simultaneous iterative reconstruction technique}, journal = {Physics in Medicine and Biology}, volume = {66}, year = {2021}, chapter = {165008}, doi = {10.1088/1361-6560/ac16bc}, author = {Joaquim Sanctorum and Sam Van Wassenbergh and Van Nguyen and Jan De Beenhouwer and Jan Sijbers and Joris J. J. Dirckx} } @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 {2203, title = {Gauss-Newton-Krylov for Reconstruction of Polychromatic X-ray CT Images}, journal = {IEEE Transactions on Computational Imaging}, volume = {7}, year = {2021}, pages = {1304-1313}, doi = {10.1109/TCI.2021.3133226}, author = {Nathana{\"e}l Six and Jens Renders and Jan Sijbers and Jan De Beenhouwer} } @article {2148, title = {Geometry Calibration of a Modular Stereo Cone-Beam X-ray CT System}, journal = {Journal of Imaging}, volume = {7}, year = {2021}, pages = {1-12}, doi = {https://doi.org/10.3390/jimaging7030054}, author = {Van Nguyen and Joaquim Sanctorum and Sam Van Wassenbergh and Joris J. J. Dirckx and Jan Sijbers and Jan De Beenhouwer} } @article {2096, title = {Joint Deblurring and Denoising of THz Time-Domain Images}, journal = {IEEE Access}, volume = {9}, year = {2021}, pages = {162-176}, doi = {10.1109/ACCESS.2020.3045605}, author = {Marina Ljubenovi{\'c} and Lina Zhuang and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {2198, title = {Mesh-based reconstruction of dynamic foam images using X-ray CT}, booktitle = {International Conference on 3D Vision (3DV2021)}, year = {2021}, pages = {1312-1320}, doi = {10.1109/3DV53792.2021.00138}, author = {Jens Renders and Jan De Beenhouwer and Jan Sijbers} } @article {2163, title = {Monte-Carlo-Based Estimation of the X-ray Energy Spectrum for CT Artifact Reduction}, journal = {Applied Sciences}, volume = {11}, year = {2021}, chapter = {3145}, doi = {https://doi.org/10.3390/app11073145}, author = {Ehsan Nazemi and Nathana{\"e}l Six and Domenico Iuso and Bjorn De Samber and Jan Sijbers and Jan De Beenhouwer} } @conference {2178, title = {Motion compensating X-ray micro-CT of diamonds in a processing stage}, year = {2021}, month = {September}, author = {Jens Renders and Anh-Tuan Nguyen and Jan De Beenhouwer and Jan Sijbers} } @article {2071, title = {Projection-angle-dependent distortion correction in high-speed image-intensifier-based x-ray computed tomography}, journal = {Measurement Science and Technology}, volume = {32}, year = {2021}, pages = {1-11}, doi = {10.1088/1361-6501/abb33e}, author = {Joaquim Sanctorum and Sam Van Wassenbergh and Van Nguyen and Jan De Beenhouwer and Jan Sijbers and Joris J. J. Dirckx} } @inproceedings {2187, title = {A study of terahertz beam simulation with ray tracing for computed tomography}, booktitle = {2021 OSA Imaging and Applied Optics Congress}, year = {2021}, doi = {10.1364/AIS.2021.JTu5A.37}, author = {Pavel Paramonov and Lars-Paul Lumbeeck and Jan Sijbers and Jan De Beenhouwer} } @inproceedings {2157, title = {Tabu-DART: an dynamic update strategy for the Discrete Algebraic Reconstruction Technique based on Tabu-search}, booktitle = { International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine}, year = {2021}, author = {Daniel Frenkel and Jan De Beenhouwer and Jan Sijbers} } @article {2087, title = {To Recurse or not to Recurse A Low Dose CT Study}, journal = {Progress in Artificial Intelligence}, volume = {10}, year = {2021}, pages = {65{\textendash}81}, doi = {https://doi.org/10.1007/s13748-020-00224-0}, author = {Shabab Bazrafkan and Vincent Van Nieuwenhove and Joris Soons and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {2053, title = {Accurate terahertz simulation with ray tracing incorporating beam shape and refraction}, booktitle = {IEEE ICIP }, year = {2020}, pages = {3035-3039}, doi = {10.1109/ICIP40778.2020.9190937}, author = {Pavel Paramonov and Lars-Paul Lumbeeck and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {1979, title = {An adaptive probability map for the Discrete Algebraic Reconstruction Technique}, booktitle = {10th Conference on Industrial Computed Tomography (ICT 2020)}, year = {2020}, pages = {1-6}, author = {Daniel Frenkel and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {2028, title = {Adjoint pairs of image warping operators for motion modeling in 4D-CT}, booktitle = {6th International Conference on Image Formation in X-Ray Computed Tomography}, year = {2020}, author = {Jens Renders and Jan Sijbers and Jan De Beenhouwer} } @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 {2084, title = {Aperture size selection for improved brain tumor detection and quantification in multi-pinhole 123I-CLINDE SPECT imaging}, booktitle = {IEEE Nuclear Science Symposium and Medical Imaging Conference, Boston, USA (2020)}, year = {2020}, author = {Benjamin Auer and Kesava Kalluri and Aly H. Abayazeed and Jan De Beenhouwer and Navid Zeraatkar and Clifford Lindsay and Neil Momsen and R. Garrett Richards and Micaehla May and Matthew A. Kupinski and Philip H. Kuo and Lars R. Furenlid and Michael A. King} } @inproceedings {2027, title = {BeadNet: a network for automated spherical marker detection in radiographs for geometry calibration}, booktitle = {6th International Conference on Image Formation in X-Ray Computed Tomography}, year = {2020}, pages = {518-521}, author = {Van Nguyen and Jan De Beenhouwer and Shabab Bazrafkan and A-T. Hoang and Sam Van Wassenbergh and Jan Sijbers} } @inproceedings {2034, title = {CNN-based Deblurring of Terahertz Images}, booktitle = {Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP)}, volume = {4}, year = {2020}, pages = {323-330}, doi = {10.5220/0008973103230330}, author = {Marina Ljubenovi{\'c} and Shabab Bazrafkan and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {1977, title = {Deep learning-based 2D-3D sample pose estimation for X-ray 3DCT}, booktitle = {10th Conference on Industrial Computed Tomography (ICT 2020)}, year = {2020}, author = {Alice Presenti and Shabab Bazrafkan and Jan Sijbers and Jan De Beenhouwer} } @inproceedings {2072, title = {Extreme Sparse X-ray Computed Laminography Via Convolutional Neural Networks}, booktitle = {ICTAI 2020}, year = {2020}, author = {Luis Filipe Alves Pereira and Jan De Beenhouwer and Johann Kastner and Jan Sijbers} } @article {2083, title = {Keel-Edge Height Selection for Improved Multi-Pinhole 123I Brain SPECT Imaging}, journal = {Journal of Nuclear Medicine}, volume = {61}, year = {2020}, pages = {573}, abstract = {573Objectives: Given its excellent resolution versus sensitivity trade-off, multi-pinhole SPECT has become a powerful tool for clinical imaging of small human structures such as the brain [1]. Our research team is designing and constructing a next-generation multi-pinhole system, AdaptiSPECT-C, for quantitative brain imaging. In this context, keel-edge pinhole has proven to increase significantly attenuation of gamma rays through the edges of the pinhole aperture compared to the most commonly clinically used knife-edge profile [2-4]. In this work, we investigate the potential improvement in imaging performance of multiple keel-edge pinhole profiles as a function of keel height for AdaptiSPECT-C compared to a knife-edge collimation for 123I-IMP brain perfusion. Methods: The prototype AdaptiSPECT-C system used herein is composed of 23 hexagonal detector modules hemi-spherically arranged along 3 rings. For modeling in GATE simulation (GS) [5], each of these modules is composed of 1.5 mm radius pinhole and a 1 cm thick NaI(Tl) crystal with a 5 cm thick back-scattering compartment, which was considered to simulate 123I down-scatter interactions. Multiple keel-edge heights, corresponding to 0.0 (knife edge), 0.375, 0.75, 1.0, 1.125, 1.5, 1.875, and 2.25 mm were studied. We evaluated the volumetric sensitivity and relative amount of collimator penetration for a 15\% energy window centered at 159 keV in simulated projections of a 21 cm diameter spherical source (e.g. corresponding to the system{\textquoteright}s volume of interest) centered at the focal point of the pinholes. For reconstruction, an approach developed in our group was employed for modeling using GS the system response and especially collimator penetration into the system matrix [6,7] for the knife and the keel-edge designs. An XCAT [8] brain phantom with source distribution for the perfusion imaging agent 123I-IMP was simulated using the pinhole designs. Projection were acquired considering two scenarios: noise free (S1), and equal imaging time comparison for a realistic clinical scan time (e.g. 30 min [9,10]) (S2). Reconstructions were performed with a customized 3D-MLEM software into images of 1203 voxels of (2 mm)3. The reconstructed images were then compared to the ground truth image in terms of the normalized root mean squared error (NRMSE) and activity recovery (\%AR) for selected three-dimensional brain regions. Results: A keel-edge height of 0.375-0.75 mm represents the best choice leading to a significant reduction of the amount of penetration (up to 50\%) at the expense of sensitivity (-20\%) compared to a knife-edge profile. Visually, for all scenarios, the use of such a keel-edge profile leads to better separation of the brain structures, especially the caudate and the putamen. When sensitivity is not taken into account (e.g. noise free scenario), increasing the keel height improves NRMSE results. For an equal imaging time comparison, lowest NRMSE values are achieved for a 0.375-0.75 mm keel-height. A further keel-height increase degrades the NRMSE results due to significant loss of counts compared to knife-edge design. A 0.75 mm keel height leads on average to the best \%ARs (e.g. closest value to 100\%), especially for the striatum and putamen. For cortex and cerebellum regions, \%ARs are comparable with those obtained for a knife-edge design. Conclusion: In this work, we demonstrated that the use of a 0.75 mm height keel-edge profile for AdaptiSPECT-C incorporating 1.5 mm radius pinholes leads to superior imaging performance compared to knife-edge collimation for clinical 123I brain perfusion imaging. A range of aperture radii from 0.5 to 3.5 mm for each design have been investigated and will be shown at the time of the conference. We are currently working on performing a numerical-observer task-performance study of defect-detection in perfusion. Research Support: National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Institutes of Health, Grant No R01 EB022521. Volumetric Sensitivities, Amount of Penetration, and Lowest NRMSE for the designs investigated}, url = {http://jnm.snmjournals.org/content/61/supplement_1/573.abstract}, author = {Benjamin Auer and Kesava Kalluri and Jan De Beenhouwer and Navid Zeraatkar and Neil Momsen and Philip H. Kuo and Lars R. Furenlid and Michael A. King} } @article {2055, title = {A low-cost geometry calibration procedure for a modular cone-beam X-ray CT system}, journal = {Nondestructive Testing and Evaluation }, volume = {35}, year = {2020}, pages = { 252-265}, doi = {https://doi.org/10.1080/10589759.2020.1774580}, author = {Van Nguyen and Jan De Beenhouwer and Joaquim Sanctorum and Sam Van Wassenbergh and Shabab Bazrafkan and Joris J. J. Dirckx and Jan Sijbers} } @article {2010, title = {A Machine Learning Approach to Growth Direction Finding for Automated Planting of Bulbous Plants}, journal = {Scientific Reports}, volume = {10}, year = {2020}, doi = {https://doi.org/10.1038/s41598-019-57405-8}, author = {Brian G. Booth and Jan Sijbers and Jan De Beenhouwer} } @inproceedings {2133, title = {Newton-Krylov Methods For Polychromatic X-Ray CT}, booktitle = {2020 IEEE International Conference on Image Processing (ICIP)}, year = {2020}, pages = {3045-3049}, publisher = {IEEE}, organization = {IEEE}, address = {Abu Dhabi}, keywords = {Attenuation, computed tomography, image reconstruction, Jacobian matrices, Linear programming, Mathematical model, Structural beams}, isbn = {978-1-7281-6396-3}, doi = {10.1109/ICIP40778.2020.9190717}, url = {https://ieeexplore.ieee.org/abstract/document/9190717}, author = {Nathana{\"e}l Six and Jens Renders and Jan Sijbers and Jan De Beenhouwer} } @article {2018, title = {Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning}, journal = {Food Control}, volume = {113}, year = {2020}, pages = {1-13}, doi = {https://doi.org/10.1016/j.foodcont.2020.107170}, author = {Tim Van De Looverbosch and Hafizur Rahman Bhuiyan and Pieter Verboven and Manuel Dierick and Van Loo, D. and Jan De Beenhouwer and Jan Sijbers and Bart Nicolai} } @inproceedings {2054, title = {The Radon Transform for Terahertz Computed Tomography Incorporating the Beam Shape}, booktitle = {IEEE ICIP}, year = {2020}, pages = {3040-3044}, doi = {10.1109/ICIP40778.2020.9191175}, author = {Lars-Paul Lumbeeck and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer} } @article {2082, title = {Reconstruction using Depth of Interaction Information of Curved and Flat Detector Designs for Quantitative Multi-Pinhole Brain SPECT}, journal = {Journal of Nuclear Medicine}, volume = {61}, year = {2020}, pages = {103}, abstract = {103Objectives: Brain SPECT has many clinical applications, especially for cerebral blood flow and dopamine transporter imaging [1,2]. In this context, a dedicated brain imaging, multi-pinhole system, AdaptiSPECT-C, is being developed by our group. Recent studies in cardiac and small animal imaging have demonstrated that the use of curved detector could improve image quality, by reducing parallax errors due to the depth of interaction (DOI) effect [3,4]. In this simulation study, we proposed to investigate using reconstruction with DOI modeling the potential advantage in imaging performance of curved over flat detectors for 123I-IMP perfusion imaging using the AdaptiSPECT-C system. Methods: The AdaptiSPECT-C design used in this work consists of 26 detector modules, 158 by 158 mm2 in size, arranged around the patient{\textquoteright}s head in three rings. The simulated detector modules were composed of a 8 mm thick NaI(Tl) crystal coupled to a 5 cm thick back-scattering compartment, representing components behind the crystal, to model 123I down-scatter interactions. Each detector module is associated with a 1.36 mm radius direct knife-edge pinhole aperture collimator. Two different system designs were considered, one based on curved detectors, and the other on flat detectors. The curved detectors were designed so that the radius of curvature corresponds to the detector to system center distance (e.g. 30.5 cm). This distance was the same for the flat detectors. GATE simulation [5] was employed to compute the system matrix [6,7] for both detector designs by forming the system response for the activity within each three-dimensional image-voxel of a 24 cm diameter sphere, thus including DOI variations and corrections in reconstruction [6,7]. An XCAT brain phantom [8] emulating 123I-IMP perfusion source distribution was simulated using the two designs. Data were acquired following three scenarios: noise free case (S1), equal number of counts comparison (S2) (e.g. 5.5M detected counts [9]), and equal imaging time comparison for the typical scan time (e.g. 30 min [10]) (S3). For S3, the total number of counts for the curved and flat detector designs, were respectively 9.24M and 9.17M. Projections were reconstructed with 3D-MLEM into images of 1203 voxels of (2 mm)3 and reconstruction compared to the ground truth image. The normalized root mean squared error (NRMSE) as well as percentage of activity recovery (\%AR) for several brain regions were used to evaluate the image quality. Results: Only a small gain in volumetric sensitivity ( 0.8\%) was obtained with the curved detector design. Qualitatively, the reconstructions for the curved and flat detector designs appear similar for all the 3 noise scenarios. Differences are mostly for the peripheral regions of the head where the differences in the obliquity of the gamma-rays passing through the apertures would be the greatest. Due to lower activity, those regions are also more impacted by noise. Quantitatively, a slight NRMSE improvement using curved detectors was seen. The curved detector design leads on average to the best ARs, especially for the striatum and putamen. Regions at the edges of the brain (e.g. cortex and cerebellum), more impacted by DOI effect, are similarly recovered by the two designs. Conclusion: We demonstrated that using curved instead of flat detector for AdaptiSPECT-C with solely centered pinholes leads to small improvement in sensitivity and image quality based on visual inspection, NRMSE, and activity recovery analysis. Flat detector associated with a sophisticated DOI correction was found to lead to similar results than those obtained with the curved detector. Further investigation will be performed using additional pinholes irradiating the 4 quadrants of the detectors which will increase the obliquity of the rays striking the detectors and may thus result in larger difference. Research Support: Grant No R01 EB022521 (NIBIB).}, url = {http://jnm.snmjournals.org/content/61/supplement_1/103.abstract}, author = {Benjamin Auer and Kesava Kalluri and Jan De Beenhouwer and Kimberly Doty and Navid Zeraatkar and Philip H. Kuo and Lars R. Furenlid and Michael A. King} } @inproceedings {2029, title = {Ring Artifact Reduction in Sinogram Space Using Deep Learning}, booktitle = {6th International Conference on Image Formation in X-Ray Computed Tomography}, year = {2020}, author = {Maxime Nauwynck and Shabab Bazrafkan and Anneke Van Heteren and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {2068, title = {Ringing Artefact Removal From Sparse View Tomosynthesis using Deep Neural Networks}, booktitle = {The 6th International Conference on Image Formation in X-Ray Computed Tomography}, year = {2020}, pages = {380-383}, author = {Shabab Bazrafkan and Vincent Van Nieuwenhove and Joris Soons and Jan De Beenhouwer and Jan Sijbers} } @article {2077, title = {X-ray phase contrast simulation for grating-based interferometry using GATE}, journal = {Optics Express}, volume = {28}, year = {2020}, pages = {33390-33412}, doi = {https://doi.org/10.1364/OE.392337}, author = {Jonathan Sanctorum and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {2090, title = {CAD-based defect inspection with optimal view angle selection based on polychromatic X-ray projection images}, booktitle = {9th Conference on Industrial Computed Tomography}, year = {2019}, pages = {1-5}, address = {Padova, Italy}, author = {Alice Presenti and Jan Sijbers and Arnold Jan den Dekker and Jan De Beenhouwer} } @conference {1965, title = {Deep learning based missing wedge artefact removal for electron tomography}, year = {2019}, pages = {660-661}, author = {Juho Rimpelainen and Shabab Bazrafkan and Jan Sijbers and Jan De Beenhouwer} } @inproceedings {1945, title = {Dynamic angle selection for few-view X-ray inspection of CAD based objects}, booktitle = {Proc. SPIE, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D)}, volume = {11072}, year = {2019}, doi = {https://doi.org/10.1117/12.2534894}, author = {Alice Presenti and Jan Sijbers and Jan De Beenhouwer} } @inproceedings {1883, title = {Fast detection of cracks in ultrasonically welded parts by inline X-ray inspection}, booktitle = {9th Conference on Industrial Computed Tomography}, year = {2019}, address = {Padova, Italy}, url = {https://www.ndt.net/article/ctc2019/papers/iCT2019_Full_paper_48.pdf}, author = {Eline Janssens and Jan Sijbers and Manuel Dierick and Jan De Beenhouwer} } @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} } @inproceedings {1918, title = {An Interactive Visual Comparison Tool for 3D Volume Datasets represented by Nonlinearly Scaled 1D Line Plots through Space-filling Curves}, booktitle = {9th Conference on Industrial Computed Tomography}, year = {2019}, address = {Padova, Italy}, author = {Johannes Weissenb{\"o}ck and Bernhard Fr{\"o}hler and Eduard Gr{\"o}ller and Jonathan Sanctorum and Jan De Beenhouwer and Jan Sijbers and Santhosh Ayalur Karunakaran and Helmuth Hoeller and Johann Kastner and Christoph Heinzl} } @inproceedings {1989, title = {Investigation of a Monte Carlo simulation and an analytic-based approach for modeling the system response for clinical I-123 brain SPECT imaging}, booktitle = {15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine}, volume = {11072}, year = {2019}, pages = {187 {\textendash} 190}, publisher = {International Society for Optics and Photonics}, organization = {International Society for Optics and Photonics}, keywords = {image reconstruction, Monte-Carlo simulation, SPECT I-123 brain imaging, System response modeling, Variance reduction technique (forced detection)}, doi = {10.1117/12.2534881}, url = {https://doi.org/10.1117/12.2534881}, author = {Benjamin Auer and Navid Zeraatkar and Jan De Beenhouwer and Kesava Kalluri and Philip H. Kuo and Lars R. Furenlid and Michael A. King} } @conference {1990, title = {Investigation of keel versus knife edge pinhole profiles for a next-generation SPECT system dedicated to clinical brain imaging}, year = {2019}, month = {2019}, author = {Benjamin Auer and Kesava Kalluri and Jan De Beenhouwer and Navid Zeraatkar and Arda K{\"o}nik and Philip H. Kuo and Lars R. Furenlid and Michael A. King} } @inproceedings {1885, title = {A low-cost and easy-to-use phantom for cone-beam geometry calibration of a tomographic X-ray system}, booktitle = {9th Conference on Industrial Computed Tomography}, year = {2019}, address = {Padova, Italy}, url = {https://www.ndt.net/article/ctc2019/papers/iCT2019_Full_paper_54.pdf}, author = {Van Nguyen and Jan De Beenhouwer and Joaquim Sanctorum and Sam Van Wassenbergh and Peter Aerts and Joris J. J. Dirckx 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 {1973, title = {poly-DART: A discrete algebraic reconstruction technique for polychromatic X-ray CT}, journal = {Optics Express}, volume = {27}, year = {2019}, pages = {33427-33435}, doi = {https://doi.org/10.1364/OE.27.033670}, author = {Nathana{\"e}l Six and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {1988, title = {Preliminary investigation of attenuation and scatter correction strategies for a next-generation SPECT system dedicated to quantitative clinical brain imaging}, booktitle = {IEEE Nuclear Science Symposium and Medical Imaging Conference}, year = {2019}, month = {11/2019}, address = {Manchester, UK}, author = {Benjamin Auer and Jan De Beenhouwer and Navid Zeraatkar and Philip H. Kuo and Lars R. Furenlid and Michael A. King} } @article {2085, title = {Primary, scatter, and penetration characterizations of parallel-hole and pinhole collimators for I-123 SPECT}, journal = {Physics in Medicine \& Biology}, volume = {64}, year = {2019}, pages = {245001}, abstract = {Multi-pinhole (MPH) collimators are known to provide better trade-off between sensitivity and resolution for preclinical, as well as for smaller regions in clinical SPECT imaging compared to conventional collimators. In addition to this geometric advantage, MPH plates typically offer better stopping power for penetration than the conventional collimators, which is especially relevant for I-123 imaging. The I-123 emits a series of high-energy (>300 keV, 2.5\% abundance) gamma photons in addition to the primary emission (159 keV, 83\% abundance). Despite their low abundance, high-energy photons penetrate through a low-energy parallel-hole (LEHR) collimator much more readily than the 159 keV photons, resulting in large downscatter in the photopeak window. In this work, we investigate the primary, scatter, and penetration characteristics of a single pinhole collimator that is commonly used for I-123 thyroid imaging and our two MPH collimators designed for I-123 DaTscan imaging for Parkinson{\textquoteright}s Disease, in comparison to three different parallel-hole collimators through a series of experiments and Monte Carlo simulations. The simulations of a point source and a digital human phantom with DaTscan activity distribution showed that our MPH collimators provide superior count performance in terms of high primary counts, low penetration, and low scatter counts compared to the parallel-hole and single pinhole collimators. For example, total scatter, multiple scatter, and collimator penetration events for the LEHR were 2.5, 7.6 and 14 times more than that of MPH within the 15\% photopeak window. The total scatter fraction for LEHR was 56\% where the largest contribution came from the high-energy scatter from the back compartments (31\%). For the same energy window, the total scatter for MPH was 21\% with only 1\% scatter from the back compartments. We therefore anticipate that using MPH collimators, higher quality reconstructions can be obtained in a substantially shorter acquisition time for I-123 DaTscan and thyroid imaging.}, doi = {10.1088/1361-6560/ab58fe}, url = {https://doi.org/10.1088\%2F1361-6560\%2Fab58fe}, author = {Arda K{\"o}nik and Benjamin Auer and Jan De Beenhouwer and Kesava Kalluri and Navid Zeraatkar and Lars R. Furenlid and Michael A. King} } @inproceedings {1884, title = {Simulated grating-based x-ray phase contrast images of CFRP-like objects}, booktitle = {9th Conference on Industrial Computed Tomography}, year = {2019}, pages = {1-8}, address = {Padova, Italy}, url = {https://www.ndt.net/article/ctc2019/papers/iCT2019_Full_paper_45.pdf}, author = {Jonathan Sanctorum and Jan De Beenhouwer and Johannes Weissenb{\"o}ck and Christoph Heinzl and Johann Kastner and Jan Sijbers} } @inproceedings {1917, title = {Tools for the Analysis of Datasets from X-Ray Computed Tomography based on Talbot-Lau Grating Interferometry}, booktitle = {9th Conference on Industrial Computed Tomography}, year = {2019}, address = {Padova, Italy}, url = {https://www.ndt.net/article/ctc2019/papers/iCT2019_Full_paper_52.pdf}, author = {Bernhard Fr{\"o}hler and Lucas da Cunha Melo and Johannes Weissenb{\"o}ck and Johann Kastner and Torsten M{\"o}ller and Hans-Christian Hege and Eduard Gr{\"o}ller and Jonathan Sanctorum and Jan De Beenhouwer and Jan Sijbers and Christoph Heinzl} } @conference {1980, title = {A Trajectory Based Bottom-Up Volume Reconstruction Method for Atom Probe Tomography}, year = {2019}, author = {Yu-Ting Ling and Siegfried Cools and Jan De Beenhouwer and Jan Sijbers and Wilfried Vandervorst} } @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} } @mastersthesis {1867, title = {Advances in X-ray reconstruction algorithms for limited data problems in conventional and non-conventional projection geometries}, volume = {PhD in Sciences / Physics}, year = {2018}, type = {PhD thesis}, author = {Eline Janssens} } @inproceedings {1831, title = {Comparative Visualization of Orientation Tensors in Fiber-Reinforced Polymers}, booktitle = {8th Conference on Industrial Computed Tomography, Wels, Austria }, year = {2018}, author = {Johannes Weissenb{\"o}ck and M Arikan and D Salaberger and Johann Kastner and Jan De Beenhouwer and Jan Sijbers and S Rauchenzauner and T Raab-Wernig and E Gr{\"o}ller and Christoph Heinzl} } @inproceedings {1830, title = {An efficient CAD projector for X-ray projection based 3D inspection with the ASTRA Toolbox}, booktitle = {8th Conference on Industrial Computed Tomography, Wels, Austria}, year = {2018}, author = {{\'A}rp{\'a}d Marinovszki and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {1869, title = {Joint reconstruction and flat-field estimation using support estimation}, booktitle = {IEEE Nuclear Science Symposium and Medical Imaging Conference}, year = {2018}, address = {Sydney, Australia}, doi = {10.1109/NSSMIC.2018.8824406}, author = {Nathana{\"e}l Six and Jan De Beenhouwer and Vincent Van Nieuwenhove and Wim Vanroose and Jan Sijbers} } @article {1816, title = {Neural network Hilbert transform based filtered backprojection for fast inline X-ray inspection}, journal = {Measurement Science and Technology}, volume = {29}, year = {2018}, doi = {10.1088/1361-6501/aa9de3}, author = {Eline Janssens and Jan De Beenhouwer and Mattias Van Dael and Thomas De Schryver and Luc Van Hoorebeke and Pieter Verboven and Bart Nicolai and Jan Sijbers} } @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} } @inproceedings {1833, title = {pDART: Discrete algebraic reconstruction using a polychromatic forward model}, booktitle = {The Fifth International Conference on Image Formation in X-Ray Computed Tomography}, year = {2018}, address = {Salt Lake City, Utah, USA}, author = {Nathana{\"e}l Six and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {1880, title = {Preliminary evaluation of surface mesh modeling of system geometry, anatomy phantom, and source activity for GATE simulations}, booktitle = {IEEE Nuclear Science Symposium and Medical Imaging Conference}, year = {2018}, month = {11/2018}, address = {Sydney, Australia}, author = {Benjamin Auer and Arda K{\"o}nik and Timothy J. Fromme and Kesava Kalluri and Jan De Beenhouwer and George I. Zubal and Lars R. Furenlid and Michael A. King} } @inproceedings {1879, title = {Preliminary investigation of design parameters of an innovative multi- pinhole system dedicated to brain SPECT imaging}, booktitle = {IEEE Nuclear Science Symposium and Medical Imaging Conference}, year = {2018}, month = {11/2018}, address = {Sydney, Australia}, author = {Benjamin Auer and Jan De Beenhouwer and Timothy J. Fromme and Kesava Kalluri and Justin C. Goding and George I. Zubal and Lars R. Furenlid and Michael A. King} } @article {1877, title = {Simulations of a Multipinhole SPECT Collimator for Clinical Dopamine Transporter (DAT) Imaging}, journal = {IEEE Transactions on Radiation and Plasma Medical Sciences}, volume = {2}, year = {2018}, month = {Sept}, pages = {444-451}, keywords = {Analytical models, Apertures, Brain, Collimators, Logic gates, multipinhole (MPH), Sensitivity, simulation, Single photon emission computed tomography, SPECT}, issn = {2469-7311}, doi = {10.1109/TRPMS.2018.2831208}, author = {Arda K{\"o}nik and Jan De Beenhouwer and J. M. Mukherjee and Kesava Kalluri and S Banerjee and N Zeraatkar and Timothy J. Fromme and Michael A. King} } @inproceedings {1858, title = {X-ray Phase-contrast Simulations of Fibrous Phantoms using GATE}, booktitle = {2018 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)}, year = {2018}, publisher = {IEEE}, organization = {IEEE}, address = {Sydney, Australia}, doi = {10.1109/NSSMIC.2018.8824641}, author = {Jonathan Sanctorum and Jan De Beenhouwer and Jan Sijbers} } @article {1727, title = {Data-Driven Affine Deformation Estimation and Correction in Cone Beam Computed Tomography}, journal = {IEEE Transactions on Image Processing}, volume = {26}, year = {2017}, pages = {1441-1451}, doi = {10.1109/TIP.2017.2651370}, author = {Vincent Van Nieuwenhove and Jan De Beenhouwer and Thomas De Schryver and Luc Van Hoorebeke and Jan Sijbers} } @conference {1785, title = {Dual axis Dark Field Contrast Tomography for visualisation of scattering directions in a CFRP sample}, year = {2017}, pages = {79-80}, address = {Z{\"u}rich, Switzerland}, abstract = {In Carbon Fiber Reinforced Polymer (CFRP), the carbon fibers embedded into the polymer matrix cause scattering when inspected with X-rays. This scattering is captured within the dark field image when the sample is scanned with a grating based interferometer. The main disadvantage is, however, that only directional scattering information can be achieved due to the orientation of the gratings. By scanning the sample twice, with a 90 degrees rotation of the sample in between, information from two different scattering directions can be combined into one 3D reconstruction volume. In this abstract, such an approach is shown to improve the representation of scattering inside the sample.}, keywords = {Dark field tomography, Dual axis, Phase contrast, Tomography, X-rays}, url = {https://indico.psi.ch/getFile.py/access?resId=1\&materialId=2\&confId=5055}, author = {Eline Janssens and Jan De Beenhouwer and Jonathan Sanctorum and Sascha Senck and Christoph Heinzl and Jan Sijbers} } @inproceedings {1753, title = {Fast Reconstruction of CFRP X-ray Images based on a Neural Network Filtered Backprojection Approach}, booktitle = {7th Conference on Industrial Computed Tomography}, year = {2017}, address = {Leuven, Belgium}, url = {http://www.ndt.net/events/iCT2017/app/content/Extended_Abstract/63_Janssens_Rev4.pdf}, author = {Eline Janssens and Sascha Senck and Christoph Heinzl and Johann Kastner and Jan De Beenhouwer and Jan Sijbers} } @article {2047, title = {Mobile imaging of an object using penetrating radiation}, number = {WO/2017/178334}, year = {2017}, chapter = {PCT/EP2017/058270}, author = {Jan Sijbers and Jan De Beenhouwer} } @mastersthesis {1821, title = {Model-based reconstruction algorithms for dynamic X-ray CT}, volume = {PhD in Sciences/Physics}, year = {2017}, type = {PhD thesis}, author = {Vincent Van Nieuwenhove} } @article {1773, title = {MoVIT: A tomographic reconstruction framework for 4D-CT}, journal = {Optics Express}, volume = {25}, year = {2017}, pages = {19236-19250}, abstract = {4D computed tomography (4D-CT) aims to visualise the temporal dynamics of a 3D sample with a sufficiently high temporal and spatial resolution. Successive time frames are typically obtained by sequential scanning, followed by independent reconstruction of each 3D dataset. Such an approach requires a large number of projections for each scan to obtain images with sufficient quality (in terms of artefacts and SNR). Hence, there is a clear trade-off between the rotation speed of the gantry (i.e. time resolution) and the quality of the reconstructed images. In this paper, the MotionVector-based Iterative Technique (MoVIT) is introduced which reconstructs a particular time frame by including the projections of neighbouring time frames as well. It is shown that such a strategy improves the trade-off between the rotation speed and the SNR. The framework is tested on both numerical simulations and on 4D X-ray CT datasets of polyurethane foam under compression. Results show that reconstructions obtained with MoVIT have a significantly higher SNR compared to the SNR of conventional 4D reconstructions.}, doi = {https://doi.org/10.1364/OE.25.019236}, author = {Vincent Van Nieuwenhove and Jan De Beenhouwer and Jelle Vlassenbroeck and Mark Brennan and Jan Sijbers} } @inproceedings {1743, title = {Registration Based SIRT: A reconstruction algorithm for 4D CT}, booktitle = {7th Conference on Industrial Computed Tomography}, year = {2017}, address = {Leuven, Belgium}, abstract = {The goal of 4D computed tomography (4D CT) is to study the temporal deformation of a 3D sample with a sufficiently high temporal and spatial resolution. Conventionally, the sample is sequentially scanned, resulting in datasets of successive time frames. Each of these datasets is then independently reconstructed. This framework results in a trade-off between the temporal resolution and the signal-to-noise ratio (SNR) of the reconstructed images. The proposed registration based simultaneous iterative reconstruction technique (RBSIRT) allows shortening the acquisition time per time frame, leading to improved temporal resolution at comparable SNR. To this end, the algorithm estimates the deformation field between different time frames, which allows incorporating projections of other time frames into the reconstruction of a particular time frame. The technique was validated on numeric simulations and on a real dynamic experiment of a polyurethane foam sample. The reconstructions obtained with RBSIRT have a significantly higher SNR compared to the SNR of conventional 4D reconstructions.}, url = {http://www.ndt.net/events/iCT2017/app/content/Paper/42_VanNieuwenhove.pdf}, author = {Vincent Van Nieuwenhove and Jan De Beenhouwer and Jelle Vlassenbroeck and Maarten Moesen and Mark Brennan and Jan Sijbers} } @conference {1784, title = {A workflow to reconstruct grating-based X-ray phase contrast CT images: application to CFRP samples}, year = {2017}, pages = {139-140}, address = {Z{\"u}rich, Switzerland}, abstract = {Carbon fiber reinforced polymer (CFRP) is an extremely strong and lightweight plastic of which the strength depends on the distribution of its fibers. Fiber bundles can be visualized by means of phase contrast X-ray computed tomography (PCCT) based on grating-based interferometry (GBI). However, many steps are involved in the reconstruction of GBI-PCCT images. In this abstract, a workflow for the reconstruction of 3D CFRP phase contrast images based on GBI projection data is presented.}, keywords = {Carbon fiber reinforced polymer, Image processing, Phase contrast, Tomography, X-rays}, url = {https://indico.psi.ch/getFile.py/access?resId=1\&materialId=2\&confId=5055}, author = {Jonathan Sanctorum and Eline Janssens and Arnold Jan den Dekker and Sascha Senck and Christoph Heinzl and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {1652, title = {Discrete tomographic reconstruction from deliberately motion blurred X-ray projections}, booktitle = {6th Conference on Industrial Computed Tomography}, year = {2016}, pages = {1-6}, address = {Wels, Austria}, author = {Wim Van Aarle and Jeroen Cant and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {1619, title = {Dynamic flat field correction in X-ray computed tomography}, booktitle = {Optimess conference}, year = {2016}, edition = {Antwerp}, author = {Vincent Van Nieuwenhove and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {1627, title = {A dynamic region estimation method for cerebral perfusion CT}, booktitle = {6th International Conference on Optical Measurement Techniques for Structures and Systems (OPTIMESS)}, year = {2016}, pages = {331-342}, abstract = {In cerebral perfusion computed tomography (PCT), multiple scans of the brain are acquired after an intravenous contrast bolus injection. Therefore, radiation dose is a major issue. Recently, methods have been proposed that can reconstruct high quality dynamic (i.e., 4D) images, while keeping the radiation dose limited. These methods typically require an accurate estimate of the dynamic region inside the brain volume, i.e., the region containing tissue/vessels. Conventionally, the dynamic region is indicated manually. In this work, a method for low-dose cerebral PCT is presented in which the dynamic region is estimated in an automatic way. Simulation results on two PCT phantoms show that the dynamic region can be accurately estimated, even in a very low-dose regime, which is an important step towards more powerful reconstruction methods for low-dose cerebral PCT. }, author = {Van Eyndhoven, Geert and Jan De Beenhouwer and Jan Sijbers} } @article {1701, title = {Fast and Flexible X-ray Tomography Using the ASTRA Toolbox}, journal = {Optics Express}, volume = {24}, number = {22}, year = {2016}, pages = {25129-25147}, doi = {10.1364/OE.24.025129}, author = {Wim Van Aarle and Willem Jan Palenstijn and Jeroen Cant and Eline Janssens and Folkert Bleichrodt and Andrei Dabravolski and Jan De Beenhouwer and Kees Joost Batenburg and Jan Sijbers} } @article {1665, title = {Fast inline inspection by neural network based filtered backprojection: Application to apple inspection}, journal = {Case Studies in Nondestructive Testing and Evaluation}, volume = {6}, year = {2016}, pages = {14{\textendash}20}, doi = {10.1016/j.csndt.2016.03.003}, author = {Eline Janssens and Luis Filipe Alves Pereira and Jan De Beenhouwer and Ing Ren Tsang and Mattias Van Dael and Pieter Verboven and Bart Nicolai and Jan Sijbers} } @conference {1741, title = {Investigating lattice strain in Au nanodecahedrons}, year = {2016}, doi = {10.1002/9783527808465.EMC2016.5519}, author = {Bart Goris and Jan De Beenhouwer and Annick De Backer and Daniele Zanaga and Kees Joost Batenburg and Ana S{\'a}nchez-Iglesias and Luis M Liz-Marzán and Sandra Van Aert and Jan Sijbers and Van Tendeloo, Gustaaf and Sara Bals} } @inproceedings {1667, title = {Investigation on Effect of scintillator thickness on Afterglow in Indirect X-ray Detectors}, booktitle = {6th Conference on Industrial Computed Tomography, Wels, Austria (iCT 2016)}, year = {2016}, abstract = {Solid-state scintillation detectors are widely used in modern multi-slice CT systems as well as synchrotron micro-tomography beamlines. Amongst other parameters, the performance of these detectors depends on the thickness of the scintillator. Thicker scintillators result in higher emission intensities, yet the resolution deteriorates as the thickness increases. To achieve a higher scan speed, thicker scintillators are more common. The thickness of scintillators however may influence the afterglow. In this paper, we investigate the effect of scintillator thickness on the afterglow, using scintillating screens of two different materials (LAG:Ce and Gadox) and different thicknesses. Experimental results show that, apart from the scintillator material and excitation condition, the thickness of scintillator has a decisive role on the scintillator decay and particularly on the afterglow. }, keywords = {afterglow, flat-panel, image lag, scintillator thickness, synchrotron, x-ray detector}, url = {http://www.ndt.net/article/ctc2016/papers/ICT2016_paper_id74.pdf}, author = {Karim Zarei Zefreh and Jan De Beenhouwer and Federica Marone Welford and Jan Sijbers} } @article {1703, title = {Local Attenuation Curve Optimization (LACO) framework for high quality perfusion maps in low-dose cerebral perfusion CT}, journal = {Medical Physics}, volume = {43}, year = {2016}, pages = {6429-6438}, doi = {10.1118/1.4967263}, author = {Vincent Van Nieuwenhove and Van Eyndhoven, Geert and Kees Joost Batenburg and Nico Buls and Jaf Vandemeulebroucke and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {1573, title = {Projection-based polygon estimation in X-ray computed tomography}, booktitle = {6th International Conference on Optical Measurement Techniques for Structures and Systems (OPTIMESS)}, year = {2016}, pages = {41-50}, author = {Andrei Dabravolski and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {1592, title = {Affine deformation correction in cone beam Computed Tomography}, booktitle = {Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine}, year = {2015}, pages = {182-185}, address = {Newport, Rhode Island, USA}, abstract = {In Computed Tomography (CT), motion and deformation during the acquisition produce streaks and blurring, known as motion artefacts. In contrast to other deformation correction techniques, this work introduces an efficient algorithm to correct for global affine deformations directly on the cone beam projections. During an experiment, the exact deformation parameters are unknown. These parameters are estimated in the projection domain by minimizing a plane based raw data redundancy criterion. Simulations and experimental data show a reduction of motion artefacts in the reconstructed images.}, url = {http://fully3d.org/proceedings.html}, author = {Vincent Van Nieuwenhove and Jan De Beenhouwer and Thomas De Schryver and Luc Van Hoorebeke and Jan Sijbers} } @article {1580, title = {The ASTRA Toolbox: a platform for advanced algorithm development in electron tomography}, journal = {Ultramicroscopy}, volume = {157}, year = {2015}, pages = {35{\textendash}47}, doi = {10.1016/j.ultramic.2015.05.002}, author = {Wim Van Aarle and Willem Jan Palenstijn and Jan De Beenhouwer and Thomas Altantzis and Sara Bals and Kees Joost Batenburg and Jan Sijbers} } @article {1615, title = {Dynamic intensity normalization using eigen flat fields in X-ray imaging}, journal = {Optics Express}, volume = {23}, year = {2015}, pages = {27975-27989}, abstract = {In X-ray imaging, it is common practice to normalize the acquired projection data with averaged flat fields taken prior to the scan. Unfortunately, due to source instabilities, vibrating beamline components such as the monochromator, time varying detector properties, or other confounding factors, flat fields are often far from stationary, resulting in significant systematic errors in intensity normalization. In this work, a simple and efficient method is proposed to account for dynamically varying flat fields. Through principal component analysis of a set of flat fields, eigen flat fields are computed. A linear combination of the most important eigen flat fields is then used to individually normalize each X-ray projection. Experiments show that the proposed dynamic flat field correction leads to a substantial reduction of systematic errors in projection intensity normalization compared to conventional flat field correction. }, doi = {10.1364/OE.23.027975}, author = {Vincent Van Nieuwenhove and Jan De Beenhouwer and Francesco De Carlo and Lucia Mancini and Federica Marone and Jan Sijbers} } @conference {1618, title = {A fast 4D CT reconstruction algorithm for affine deforming objects}, year = {2015}, edition = {Helsinki}, author = {Vincent Van Nieuwenhove and Jan De Beenhouwer and Jan Sijbers} } @article {1591, title = {Fast Neural Network Based X-Ray Tomography of Fruit on a Conveyor Belt}, journal = {Chemical Engineering Transactions}, volume = {44}, year = {2015}, pages = {181-186}, address = {Milano, Italy}, doi = {10.3303/CET1544031}, author = {Eline Janssens and Daan Pelt and Jan De Beenhouwer and Mattias Van Dael and Pieter Verboven and Bart Nicolai and Jan Sijbers} } @article {1611, title = {Measuring Lattice Strain in Three Dimensions through Electron Microscopy}, journal = {Nano Letters}, volume = {15}, year = {2015}, pages = {6996{\textendash}7001}, doi = {10.1021/acs.nanolett.5b03008}, author = {Bart Goris and Jan De Beenhouwer and Annick De Backer and Daniele Zanaga and Kees Joost Batenburg and Ana S{\'a}nchez-Iglesias and Luis M Liz-Marzán and Sandra Van Aert and Sara Bals and Jan Sijbers and Van Tendeloo, Gustaaf} } @inproceedings {1576, title = {Neural Network Based X-Ray Tomography for Fast Inspection of Apples on a Conveyor Belt}, booktitle = {IEEE International Conference on Image Processing}, year = {2015}, month = {Sept 21-27}, pages = {917-921}, doi = {10.1109/ICIP.2015.7350933}, author = {Eline Janssens and Jan De Beenhouwer and Mattias Van Dael and Pieter Verboven and Bart Nicolai and Jan Sijbers} } @article {1471, title = {Aligning Projection Images from Binary Volumes}, journal = {Fundamenta Informaticae}, volume = {135}, year = {2014}, pages = {1-22}, doi = {10.3233/FI-2014-1090}, author = {Folkert Bleichrodt and Jan De Beenhouwer and Jan Sijbers and Kees Joost Batenburg} } @article {1504, title = {Combined Estimation of Affine Movement and Reconstruction in Tomography}, year = {2014}, publisher = {3D Materials Science Conference}, author = {Vincent Van Nieuwenhove and Van Eyndhoven, Geert and Jan De Beenhouwer and Jan Sijbers} } @conference {1503, title = {Compensation of affine deformations in fan and cone beam projections}, year = {2014}, pages = {187-189}, author = {Vincent Van Nieuwenhove and Van Eyndhoven, Geert and Jan De Beenhouwer and Jan Sijbers} } @article {1472, title = {Neutron radiography and tomography applied to fuel degradation during ramp tests and loss of coolant accident tests in a research reactor}, journal = {Progress in Nuclear Energy}, volume = {72}, year = {2014}, pages = {55-62}, doi = {http://dx.doi.org/10.1016/j.pnucene.2013.11.001}, author = {Hakon Kristian Jenssen and B.C. Oberlander and Jan De Beenhouwer and Jan Sijbers and M. Verwerft} } @inproceedings {1650, title = {An Alignment Method for Fan Beam Tomography}, booktitle = {International Conference on Tomography of Materials and Structures (ICTMS)}, year = {2013}, pages = {103-107}, author = {Folkert Bleichrodt and Jan De Beenhouwer and Jan Sijbers and Kees Joost Batenburg} } @conference {1714, title = {A framework for markerless alignment with full 3D flexibility}, year = {2012}, author = {Jan De Beenhouwer and Willem Jan Palenstijn and Folkert Bleichrodt and Kees Joost Batenburg and Jan Sijbers} }