Publications

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Journal Article
N. Six, De Beenhouwer, J., and Sijbers, J., poly-DART: A discrete algebraic reconstruction technique for polychromatic X-ray CT, Optics Express, vol. 27, no. 23, pp. 33427-33435, 2019.PDF icon Download paper (997.08 KB)
W. J. Palenstijn, Batenburg, K. J., and Sijbers, J., Performance improvements for iterative electron tomography reconstruction using graphics processing units (GPUs), Journal of structural biology, vol. 176, no. 2, pp. 250-253, 2011.PDF icon Download paper (527.21 KB)
D. Giraldo, Khan, H., Pineda, G., Liang, Z., Lozano, A., Van Mijweersch, B., Woodruff, H. C., Lambin, P., Romero, E., Peeters, L. M., and Sijbers, J., Perceptual super-resolution in multiple sclerosis MRI, Frontiers in Neuroscience, In Press.
G. Ramos-Llordén, den Dekker, A. J., and Sijbers, J., Partial Discreteness: a Novel Prior for Magnetic Resonance Image Reconstruction, IEEE Transactions on Medical Imaging, vol. 36, no. 5, pp. 1041 - 1053, 2017.PDF icon Download paper (3.72 MB)
T. Elberfeld, De Beenhouwer, J., den Dekker, A. J., Heinzl, C., and Sijbers, J., Parametric Reconstruction of Glass Fiber-reinforced Polymer Composites from X-ray Projection Data - A Simulation Study, Journal of Nondestructive Evaluation, vol. 37, no. 62, pp. 1573-4862, 2018.
T. Huysmans, Sijbers, J., and Verdonk, B., Parameterization of tubular surfaces on the cylinder, Journal of the Winter School of Computer Graphics, vol. 13, pp. 97-104, 2005.PDF icon Download paper (798.29 KB)
J. Sijbers, den Dekker, A. J., Raman, E., and Van Dyck, D., Parameter estimation from magnitude MR images, International Journal of Imaging Systems and Technology, vol. 10, pp. 109-114, 1999.PDF icon Download paper (350.44 KB)
B. G. Booth, Hoefnagels, E., Huysmans, T., Sijbers, J., and Keijsers, N. L. W., PAPPI: Personalized analysis of plantar pressure images using statistical modelling and parametric mapping, PlosOne, vol. 15, no. 2, p. e0229685, 2020.
D. Iuso, Paramonov, P., De Beenhouwer, J., and Sijbers, J., PACS: Projection-driven with Adaptive CADs X-ray Scatter compensation for additive manufacturing inspection, Precision Engineering, vol. 90, pp. 108-121, 2024.PDF icon Download paper (4.28 MB)
B. G. Booth, Keijsers, N. L. W., and Sijbers, J., Outlier detection for foot complaint diagnosis: modeling confounding factors using metric learning, IEEE Intelligent Systems, vol. 36, no. 3, pp. 41-49, 2021.
H. E. Bortier, Bernat, A., Huysmans, T., Van Glabbeek, F., Sijbers, J., Pinho, R., Gielen, J., and Hubens, G., Osteologic exploration of the clavicle: a new approach, The FASEB Journal, vol. 23, 2009.
K. J. Batenburg and Sijbers, J., Optimal Threshold Selection for Tomogram Segmentation by Projection Distance Minimization, IEEE Transactions on Medical Imaging, vol. 28, pp. 676-686, 2009.PDF icon Download full paper (2.51 MB)
W. Van Aarle, Batenburg, K. J., and Sijbers, J., Optimal threshold selection for segmentation of dense homogeneous objects in tomographic reconstructions, IEEE Transactions on Medical Imaging, vol. 30, pp. 980-989, 2011.PDF icon Download paper (1.59 MB)
J. Gonnissen, De Backer, A., den Dekker, A. J., Martinez, G. T., Rosenauer, A., Sijbers, J., and Van Aert, S., Optimal experimental design for the detection of light atoms from high-resolution scanning transmission electron microscopy images, Applied Physics Letters, vol. 105, no. 063116, 2014.
D. H. J. Poot, den Dekker, A. J., Achten, E., Verhoye, M., and Sijbers, J., Optimal experimental design for Diffusion Kurtosis Imaging, IEEE Transactions on Medical Imaging, vol. 29, pp. 819-829, 2010.PDF icon Download paper (1.12 MB)
J. Morez, Szczepankiewicz, F., den Dekker, A. J., Vanhevel, F., Sijbers, J., and Jeurissen, B., Optimal experimental design and estimation for q-space trajectory imaging, Human Brain Mapping, vol. 44, no. 4, pp. 1793-1809, 2023.PDF icon Download paper (5.87 MB)
M. Van Dael, Rogge, S., Verboven, P., Saeys, W., Sijbers, J., and Nicolai, B., Online Tomato Inspection Using X-Ray Radiographies and 3- Dimensional Shape Models, Chemical Engineering Transactions, vol. 44, pp. 37-42, 2015.
G. Ramos-Llordén, Vegas-Sánchez-Ferrero, G., Björk, M., Vanhevel, F., Parizel, P. M., Estépar, R. San José, den Dekker, A. J., and Sijbers, J., NOVIFAST: A fast algorithm for accurate and precise VFA MRI T1 mapping, IEEE Transactions on Medical Imaging, vol. 37, no. 11, pp. 2414 - 2427, 2018.PDF icon Download paper (3.3 MB)
M. Naeyaert, Roose, D., Mai, Z., Keliris, A. J., Sijbers, J., Van Der Linden, A., and Verhoye, M., Normalized Averaged Range (nAR), a Robust Quantification Method for MPIO-content, Journal of Magnetic Resonance, vol. 300, pp. 18-27, 2019.
W. Van Hecke, Leemans, A., D'Agostino, E., De Backer, S., Vandervliet, E., Parizel, P. M., and Sijbers, J., Nonrigid Coregistration of Diffusion Tensor Images Using a Viscous Fluid Model and Mutual Information, IEEE Transactions on Medical Imaging, vol. 26, pp. 1598-1612, 2007.PDF icon Download paper (1.85 MB)
P. V. Sudeep, Palanisamy, P., Kesavadas, C., Sijbers, J., den Dekker, A. J., and Rajan, J., A nonlocal maximum likelihood estimation method for enhancing magnetic resonance phase maps, Signal Image and Video Processing, vol. 11, no. 5, pp. 913-920, 2017.
J. Rajan, Veraart, J., Van Audekerke, J., Verhoye, M., and Sijbers, J., Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images, Magnetic Resonance Imaging, vol. 30, no. 10, pp. 1512-1518, 2012.PDF icon Download full paper (1.11 MB)
J. Rajan, Veraart, J., Van Audekerke, J., Verhoye, M., and Sijbers, J., Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images., Magnetic resonance imaging, vol. 30, no. 10, pp. 1512-8, 2012.
T. Van De Looverbosch, Bhuiyan, H. Rahman, Verboven, P., Dierick, M., Van Loo, D., De Beenhouwer, J., Sijbers, J., and Nicolai, B., Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning, Food Control, vol. 113, no. 107170, pp. 1-13, 2020.
T. Van De Looverbosch, Raeymaekers, E., Verboven, P., Sijbers, J., and Nicolai, B., Non-destructive internal disorder detection of Conference pears by semantic segmentation of X-ray CT scans using deep learning, Expert Systems with Applications, vol. 176, no. 114925, pp. 1-12, 2021.

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