Publications

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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 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, 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, 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.
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, “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.
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.
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, 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.
J. Rajan, Poot, D. H. J., Juntu, J., and Sijbers, J., “Noise measurement from magnitude MRI using local estimates of variance and skewness.”, Physics in medicine and biology, vol. 55, no. 16, pp. N441-9, 2010.PDF icon Download paper (219.85 KB)
J. Rajan, “Estimation and removal of noise from single and multiple coil Magnetic Resonance images”, 2012.PDF icon Download thesis (3.23 MB)
J. Rajan, Van Audekerke, J., Verhoye, M., Van Der Linden, A., and Sijbers, J., “Denoising magnitude MRI using an adaptive NLML method”, ESMRMB Congress 28th Annual Scientific Meeting. Leipzig, Germay, p. 383, 2011.

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