Publications

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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, 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., 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, 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)
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)
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. 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., “CNN-based pose estimation from a single X-ray projection for 3D inspection of manufactured objects”, in 11th Conference on Industrial Computed Tomography, 2022.
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 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, 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 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., “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.
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.
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J. Rajan, Van Audekerke, J., Van Der Linden, A., Verhoye, M., and Sijbers, J., “An adaptive non local maximum likelihood estimation method for denoising magnetic resonance images”, in IEEE International Symposium on Biomedical Imaging (ISBI), 2012.PDF icon Download full paper (307.46 KB)
J. Rajan, Jeurissen, B., Sijbers, J., and Kannan, K., “Denoising Magnetic Resonance Images using Fourth Order Complex Diffusion”, in 13th International Machine Vision and Image Processing Conference, Dublin, Ireland, 2009, pp. 123-127.
J. Rajan, den Dekker, A. J., and Sijbers, J., “A new non local maximum likelihood estimation method for Rician noise reduction in Magnetic Resonance images using the Kolmogorov-Smirnov test”, Signal Processing, vol. 103, pp. 16-23, 2014.
J. Rajan, Verhoye, M., and Sijbers, J., “A maximum likelihood estimation method for denoising magnitude MRI using restricted local neighborhood”, in SPIE Medical Imaging, 2011, vol. 7962.
J. Rajan, Van Audekerke, J., Veraart, J., Verhoye, M., and Sijbers, J., “An extended NLML method for denoising non-central chi distributed data - application to parallel MRI”, Fourth Annual Meeting of the Benelux ISMRM chapter. p. 41, 2011.
J. Rajan, Poot, D. H. J., Juntu, J., and Sijbers, J., “Segmentation Based Noise Variance Estimation from Background MRI Data”, in ICIAR , Porto, Portugal, 2010, vol. 6111, pp. 62-70.
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 and Sijbers, J., “Denoising SENSE reconstructed MR images”, 5th Annual Symposium of the Benelux Chapter of the IEEE Engineering in Medicine and Biology Society. 2011.
J. Rajan, den Dekker, A. J., Juntu, J., and Sijbers, J., “A New Nonlocal Maximum Likelihood Estimation Method for Denoising Magnetic Resonance Images”, in 5th International Conference, PReMI 2013, Kolkata, India, December 10-14, 2013. Proceedings, 2013, Lecture Notes in Computer Science., vol. 8251.

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