@article {2423, title = {Framework for optimal design of a multi-source rectangular X-ray cargo scanning system}, journal = {Radiation Physics and Chemistry}, volume = {239}, year = {2026}, pages = {113332}, doi = {https://doi.org/10.1016/j.radphyschem.2025.113332 }, author = {Caroline Bossuyt and Arnold Jan den Dekker and Domenico Iuso and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {2393, title = {Single X-ray Projection Material Decomposition using a Mesh Projector}, booktitle = {14th Conference on Industrial Computed Tomography (iCT), 4 - 7 February 2025, Antwerp, Belgium (iCT 2025)}, year = {2025}, author = {Fleur Linsen and Domenico Iuso and Jan Sijbers} } @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 Nicholas Francken and Jens Renders and Domenico Iuso and Tim Elberfeld and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {2374, title = {Deep image prior for sparse-view reconstruction in static, rectangular multi-source x-ray CT systems for cargo scanning}, booktitle = {SPIE Developments in X-Ray Tomography XV}, volume = {13152}, year = {2024}, doi = {https://doi.org/10.1117/12.3027668}, author = {Caroline Bossuyt and Arnold Jan den Dekker and Domenico Iuso and T. Le Hoang and Julie Escoda and Marius Costin and Jan Sijbers and Jan De Beenhouwer} } @mastersthesis {2413, title = {Ensuring Flawlessness in Additive Manufacturing: Advances in X-ray Inspection Techniques for Efficient Defect Detection}, volume = {PhD in Sciences}, year = {2024}, type = {PhD thesis}, author = {Domenico Iuso} } @inproceedings {2350, title = {High-resolution tiled X-ray cone-beam CT using the ASTRA toolbox}, booktitle = {13th Conference on Industrial Computed Tomography (iCT) 2024}, year = {2024}, month = {03/2024}, address = {School of Engineering, Wels Campus, Austria}, abstract = {Dealing with large and/or elongated objects presents a challenge in micro computed tomography ({\textmu}CT). In conventional CT, the necessity for the X-ray beam to fully cover the scanned object which has to remain well within the detector boundaries is essential to avoid region-of-interest artifacts in the reconstructed images. Consequently, the magnification of the object and thereby the spatial resolution are limited by the physical dimensions and degrees of freedom of the imaging system. A possible way to increase the magnification and thus the resolution of a {\textmu}CT image is to perform a tiled scan of the object. In this paper, we propose an iterative reconstruction approach that yields a high-resolution image from such a tiled CT scan. The approach utilizes the ASTRA toolbox, leveraging its key features: the vector-based definition of projection geometry and the linear tomography operators. Our simulations demonstrate that the proposed approach achieves higher resolution through increased magnification.}, keywords = {cone beam, iterative reconstruction, micro computed tomography, X-ray tomography}, isbn = {1435-4934}, doi = {10.58286/29258 }, author = {Pavel Paramonov and Joaquim G. Sanctorum and Domenico Iuso and Jan Sijbers and Jan De Beenhouwer} } @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 {2358, title = {PACS: Projection-driven with Adaptive CADs X-ray Scatter compensation for additive manufacturing inspection}, journal = {Precision Engineering}, volume = {90}, year = {2024}, pages = {108-121}, doi = {https://doi.org/10.1016/j.precisioneng.2024.08.006}, author = {Domenico Iuso and Pavel Paramonov and Jan De Beenhouwer and Jan Sijbers} } @article {2336, title = {Practical multi-mesh registration for few-view poly-chromatic X-ray inspection}, journal = {Journal of Non-destructive Testing}, volume = {43}, year = {2024}, chapter = {63}, doi = {10.1007/s10921-024-01071-y}, author = {Domenico Iuso and Pavel Paramonov and Jan De Beenhouwer and Jan Sijbers} } @article {2349, title = {Voxel-wise segmentation for porosity investigation of additive manufactured parts with 3D unsupervised and (deeply) supervised neural models}, journal = {Applied Intelligence}, volume = {54}, year = {2024}, pages = { 13160{\textendash}13177}, doi = {https://doi.org/10.1007/s10489-024-05647-z}, author = {Domenico Iuso and Soumick Chatterjee and Sven Cornelissen and Dries Verhees and Jan De Beenhouwer and Jan Sijbers} } @article {2285, title = {Towards material and process agnostic features for the classification of pore types in metal additive manufacturing}, journal = {Materials \& Design}, volume = {227}, year = {2023}, pages = {111757}, doi = {10.1016/j.matdes.2023.111757}, author = {M. Vandecasteele and R. Heylen and Domenico Iuso and A. Thanki and W. Philips and A. Witvrouw and D. Verhees and B. G. Booth} } @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} } @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} } @article {2248, title = {Self-calibration of C-arm imaging system using interventional instruments during an intracranial biplane angiography.}, journal = {Int J Comput Assist Radiol Surg}, year = {2022}, month = {2022 Mar 12}, abstract = {To create an accurate 3D reconstruction of the vascular trees, it is necessary to know the exact geometrical parameters of the angiographic imaging system. Many previous studies used vascular structures to estimate the system{\textquoteright}s exact geometry. However, utilizing interventional devices and their relative features may be less challenging, as they are unique in different views. We present a semi-automatic self-calibration approach considering the markers attached to the interventional instruments to estimate the accurate geometry of a biplane X-ray angiography system for neuroradiologic use.

METHODS: A novel approach is proposed to detect and segment the markers using machine learning classification, a combination of support vector machine and boosted tree. Then, these markers are considered as reference points to optimize the acquisition geometry iteratively.

RESULTS: The method is evaluated on four clinical datasets and three pairs of phantom angiograms. The mean and standard deviation of backprojection error for the catheter or guidewire before and after self-calibration are [Formula: see text]~mm and [Formula: see text]~mm, respectively. The mean and standard deviation of the 3D root-mean-square error (RMSE) for some markers in the phantom reduced from [Formula: see text] to [Formula: see text]~mm.

CONCLUSION: A semi-automatic approach to estimate the accurate geometry of the C-arm system was presented. Results show the reduction in the 2D backprojection error as well as the 3D RMSE after using our proposed self-calibration technique. This approach is essential for 3D reconstruction of the vascular trees or post-processing techniques of angiography systems that rely on accurate geometry parameters.}, issn = {1861-6429}, doi = {10.1007/s11548-022-02580-9}, author = {Chabi, Negar and Domenico Iuso and Beuing, Oliver and Preim, Bernhard and Saalfeld, Sylvia} } @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} } @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} }