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W. Jacquet, Nyssen, E., Sun, Y., De Munter, S., Sijbers, J., and Politis, C., “Alveolar Nerve Unfolding Technique for Synoptic Analysis: Visualization and Quantification of Inferior Alveolar Nerve Tracings in Three-dimensional Cone-Beam Computed Tomography.”, The Journal of craniofacial surgery, vol. 24, no. 4, pp. e374-7, 2013.
K. Vinay Jambhali, Koirala, B., Bnoulkacem, Z., and Scheunders, P., “Soil Moisture Content Estimation from Hyperspectral Remote Sensing Data”, in 2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2023, pp. 1-5.PDF icon soil_ketaki.pdf (580.15 KB)
E. Janssens, De Beenhouwer, J., Van Dael, M., Verboven, P., Nicolai, B., and Sijbers, J., “Neural Network Based X-Ray Tomography for Fast Inspection of Apples on a Conveyor Belt”, in IEEE International Conference on Image Processing, 2015, pp. 917-921.
E. Janssens, Pelt, D., De Beenhouwer, J., Van Dael, M., Verboven, P., Nicolai, B., and Sijbers, J., “Fast Neural Network Based X-Ray Tomography of Fruit on a Conveyor Belt”, Chemical Engineering Transactions, vol. 44, pp. 181-186, 2015.
E. Janssens, De Beenhouwer, J., Sanctorum, J., Senck, S., Heinzl, C., and Sijbers, J., “Dual axis Dark Field Contrast Tomography for visualisation of scattering directions in a CFRP sample”, 4th Conference on X-ray and Neutron Phase Imaging with Gratings. Zürich, Switzerland, pp. 79-80, 2017.PDF icon Download poster (729.94 KB)PDF icon Download abstract (116.29 KB)
E. Janssens, De Beenhouwer, J., Van Dael, M., De Schryver, T., Van Hoorebeke, L., Verboven, P., Nicolai, B., and Sijbers, J., “Neural network Hilbert transform based filtered backprojection for fast inline X-ray inspection”, Measurement Science and Technology, vol. 29, no. 3, 2018.PDF icon Download paper (3.4 MB)
E. Janssens, Senck, S., Heinzl, C., Kastner, J., De Beenhouwer, J., and Sijbers, J., “Fast Reconstruction of CFRP X-ray Images based on a Neural Network Filtered Backprojection Approach”, in 7th Conference on Industrial Computed Tomography, Leuven, Belgium, 2017.PDF icon Download paper (345.06 KB)
E. Janssens, “Advances in X-ray reconstruction algorithms for limited data problems in conventional and non-conventional projection geometries”, 2018.PDF icon Download thesis (17.29 MB)
E. Janssens, Alves Pereira, L. F., De Beenhouwer, J., Tsang, I. R., Van Dael, M., Verboven, P., Nicolai, B., and Sijbers, J., “Fast inline inspection by neural network based filtered backprojection: Application to apple inspection”, Case Studies in Nondestructive Testing and Evaluation, vol. 6, pp. 14–20, 2016.PDF icon Download paper (726.63 KB)
E. Janssens, Sijbers, J., Dierick, M., and De Beenhouwer, J., “Fast detection of cracks in ultrasonically welded parts by inline X-ray inspection”, in 9th Conference on Industrial Computed Tomography, Padova, Italy, 2019.
P. Jedrasik, Garcia, J., De Boeck, B., and Van Dyck, D., “Optimal filtering versus regularization methods in the Fourier precompensation based proximity neurocorrection in electron beam lithography”, Microelectronic Engineering, vol. 41, pp. 195-198, 1998.
H. K. Jenssen, Oberlander, B. C., De Beenhouwer, J., Sijbers, J., and Verwerft, M., “Neutron radiography and tomography applied to fuel degradation during ramp tests and loss of coolant accident tests in a research reactor”, Progress in Nuclear Energy, vol. 72, pp. 55-62, 2014.
B. Jeurissen, Descoteaux, M., Mori, S., and Leemans, A., “Diffusion MRI fiber tractography of the brain”, NMR in Biomedicine, 2019.
B. Jeurissen, “Improved analysis of brain connectivity using high angular resolution diffusion MRI”, University of Antwerp, 2012.PDF icon Download thesis book (35.42 MB)
B. Jeurissen and Szczepankiewicz, F., “Multi-tissue spherical deconvolution of tensor-valued diffusion MRI.”, Neuroimage, vol. 245, p. 118717, 2021.
B. Jeurissen, Leemans, A., Tournier, J. - D., and Sijbers, J., “Probabilistic Fiber Tracking using the Residual Bootstrap with Constrained Spherical Deconvolution MRI”, ISMRM, 17th Scientific Meeting and Exhibition. Honolulu, USA, 2009.
B. Jeurissen, Leemans, A., Tournier, J. - D., and Sijbers, J., “Estimation of Uncertainty in Constrained Spherical Deconvolution Fiber Orientations”, in 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Paris, France, 2008, pp. 907-910.
B. Jeurissen, “Processing multi-shell diffusion MRI data using MRtrix3”, Front. Neuroinform. Conference Abstract: Second Belgian Neuroinformatics Congress. 2015.
B. Jeurissen, Leemans, A., Jones, D. K., Tournier, J. - D., and Sijbers, J., “Estimating the number of fiber orientations in diffusion MRI voxels: a constrained spherical deconvolution study”, ISMRM, 18th Scientific Meeting and Exhibition. Stockholm, Sweden, 2010.
B. Jeurissen, Leemans, A., Tournier, J. - D., Jones, D. K., and Sijbers, J., “Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging.”, Human Brain Mapping, vol. 34, pp. 2747-66, 2013.
B. Jeurissen, Tournier, J. - D., and Sijbers, J., “Tissue-type segmentation using non-negative matrix factorization of multi-shell diffusion-weighted MRI images”, ISMRM 23th Annual Meeting, Toronto, Ontario, Canada, vol. 23. p. 349, 2015.PDF icon Download abstract (1.11 MB)
B. Jeurissen, Leemans, A., Tournier, J. - D., and Sijbers, J., “Bootstrap methods for estimating uncertainty in Constrained Spherical Deconvolution fiber orientations”, ISMRM, 16th Scientific Meeting and Exhibition. Toronto, Canada, p. 3324, 2008.
B. Jeurissen, Leemans, A., Tournier, J. - D., Jones, D. K., and Sijbers, J., “Assessing the implications of complex fiber configurations for DTI metrics in real data sets”, ISMRM, 20th Scientific Meeting and Exhibition. Melbourne, Australia, p. 3584, 2012.
B. Jeurissen, Naeyaert, M., Leemans, A., and Sijbers, J., “Registration based correction of DWI gradient orientations”, ISMRM, 19th Scientific Meeting and Exhibition. Montreal, Canada, 2011.
B. Jeurissen, Tournier, J. - D., Dhollander, T., Connelly, A., and Sijbers, J., “Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data”, NeuroImage, vol. 103, pp. 411–426, 2014.

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