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P. Pullens, Bladt, P., Sijbers, J., Maas, A. I. R., and Parizel, P. M., A safe, cheap and easy-to-use isotropic diffusion phantom for clinical and multicenter studies, Medical Physics, vol. 44, no. 3, pp. 1063–1070, 2017.
A. Presenti, Sijbers, J., den Dekker, A. J., and De Beenhouwer, J., CAD-based defect inspection with optimal view angle selection based on polychromatic X-ray projection images, in 9th Conference on Industrial Computed Tomography, Padova, Italy, 2019, pp. 1-5.PDF icon ict2019_full_paper_55.pdf (216.2 KB)
A. Presenti, Sijbers, J., and De Beenhouwer, J., Dynamic angle selection for few-view X-ray inspection of CAD based objects, in Proc. SPIE, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2019, vol. 11072.
A. Presenti, Liang, Z., Alves Pereira, L. F., Sijbers, J., and De Beenhouwer, J., Automatic anomaly detection from X-ray images based on autoencoder, Nondestructive Testing and Evaluation, vol. 37, no. 5, 2022.
A. Presenti, Bazrafkan, S., Sijbers, J., and De Beenhouwer, J., Deep learning-based 2D-3D sample pose estimation for X-ray 3DCT, in 10th Conference on Industrial Computed Tomography (ICT 2020), 2020.
A. Presenti, Sijbers, J., and De Beenhouwer, J., Dynamic few-view X-ray imaging for inspection of CAD-based objects, Expert Systems with Applications, vol. 180, p. 115012, 2021.
A. Presenti, 3D X-ray radiography-based inspection of manufactured objects, 2022.
A. Presenti, Liang, Z., Alves Pereira, L. F., Sijbers, J., and De Beenhouwer, J., CNN-based Pose Estimation of Manufactured Objects During Inline X-ray Inspection, in 2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI), 2021.
A. Presenti, Liang, Z., Alves Pereira, L. F., Sijbers, J., and De Beenhouwer, J., Fast and accurate pose estimation of additive manufactured objects from few X-ray projections, Expert Systems With Applications, vol. 213, no. 118866, pp. 1-10, 2023.
A. Presenti, Liang, Z., Alves Pereira, L. F., Sijbers, J., and De Beenhouwer, J., CNN-based pose estimation from a single X-ray projection for 3D inspection of manufactured objects, in 11th Conference on Industrial Computed Tomography, 2022.
J. Praet, Manyakov, N., Muchene, L., Mai, Z., Terzopoulos, V., De Backer, S., Torremans, A., Guns, P. - J., Van De Casteele, T., Bottelbergs, A., Van Broeck, B., Sijbers, J., Smeets, D., Shkedy, Z., Bijnens, L., Pemberton, D., Schmidt, M., Van Der Linden, A., and Verhoye, M., Diffusion kurtosis imaging allows the early detection and longitudinal follow-up of amyloid β-induced pathology., Alzheimer's Research & Therapy , vol. 10, no. 1, pp. 1-16, 2018.
A. Postnov, De Schutter, T. T., Sijbers, J., Karperien, M., and De Clerck, N., Glucocorticoid-Induced Osteoporosis in Growing Mice Is Not Prevented by Simultaneous Intermittent PTH Treatment, Calcified Tissue International, vol. 85, pp. 530-537, 2009.PDF icon Download paper (344.96 KB)
D. H. J. Poot, Jeurissen, B., Bastiaensen, Y., Veraart, J., Van Hecke, W., Parizel, P. M., and Sijbers, J., Super-Resolution for Multislice Diffusion Tensor Imaging, Magnetic Resonance in Medicine, vol. 69, no. 1, pp. 103–113, 2013.PDF icon Download paper (1.04 MB)
D. H. J. Poot, den Dekker, A. J., and Sijbers, J., Pearson Set of Distributions as Improved Signal Model for Diffusion Kurtosis Imaging, ISMRM conference proceedings. ISMRM, p. 1383, 2009.PDF icon Download full paper (638.23 KB)
D. H. J. Poot, Sijbers, J., den Dekker, A. J., and Pintjens, W., Automatic estimation of the noise variance from the histogram of a magnetic resonance image, in IEEE/EBMS Benelux Symposium proceedings, 2006, pp. 135-138.
D. H. J. Poot, Sijbers, J., and den Dekker, A. J., Optimizing the Diffusion Kurtosis imaging acquisition, in European Society for Magnetic Resonance in Medicine and Biology, Valencia, Spain, 2008.
D. H. J. Poot, Sijbers, J., den Dekker, A. J., and Bos, R., Estimation of the noise variance from the background histogram mode of an MR image, in Proceedings of the 25th Benelux Meeting on Systems and Control, Heeze, The Netherlands, 2006.
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)
D. H. J. Poot, Van Meir, V., and Sijbers, J., General and Efficient Super-Resolution method for Multi-Slice MRI, in Medical Image Computing and Computer Assisted Intervention, 2010, vol. 13, no. 1, pp. 615-622.PDF icon Download paper (229.84 KB)
D. H. J. Poot, Sijbers, J., and den Dekker, A. J., An exploration of spatial similarities in temporal noise spectra in fMRI measurements, in Proceedings of SPIE Medical Imaging 2008, San Diego, CA, USA, 2008, vol. 6914, p. 69142.
D. H. J. Poot, den Dekker, A. J., Verhoye, M., Blockx, I., Van Audekerke, J., Van Der Linden, A., and Sijbers, J., Optimizing the Diffusion Weighting Gradients for Diffusion-Kurtosis Imaging, ISMRM2009 proceedings, vol. 2009. p. 1394, 2009.PDF icon Download full paper (624.88 KB)
D. H. J. Poot, Pintjens, W., Verhoye, M., Van Der Linden, A., and Sijbers, J., Improved B0 field map estimation for high field EPI, Magnetic Resonance Imaging, vol. 28, pp. 441-450, 2010.PDF icon Download paper (1.29 MB)
D. H. J. Poot, Advances in the reconstruction and statistical processing of Magnetic Resonance images, 2009.PDF icon Download thesis (6.18 MB)
D. H. J. Poot, Sijbers, J., den Dekker, A. J., and Bos, R., Estimation of the noise variance from the background histogram mode of an MR image, in Proceedings of SPS-DARTS 2006 (The second annual IEEE BENELUX/DSP Valley Signal Processing Symposium), Antwerp, Belgium, 2006, pp. 159-162.
L. Plantagie, Palenstijn, W. J., Sijbers, J., and Batenburg, K. J., Spatial Variations in Reconstruction Methods for CT, in The Second International Conference on Image Formation in X-Ray Computed Tomography (CT Meeting), 2012, pp. 170-173.

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