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A. Adibi, Karami, A., Heylen, R., and Scheunders, P., Optical Solutions for Improving Spatial Resolution of Hyperspectral Sensors, in 8th workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing, Los Angeles, USA, 2016.
R. Adnan, Dhondt, E., Danneels, L., Hodges, P., Jeurissen, B., and Van Oosterwijck, J., The chronification of pain : are peripheral muscle dysfunctions linked to central alteration in the brain?, 16th PAIN Research Meeting (PRM) 2017. 2017.
R. Adnan, Dhondt, E., Danneels, L., Hodges, P., Jeurissen, B., and Van Oosterwijck, J., The chronification of pain: are peripheral muscle dysfunction linked to central alteration in the brain, 16th PAIN Research Meeting (PRM) 2017. 2017.
H. Aerts, Schirner, M., Jeurissen, B., Van Roost, D., Achten, E., Ritter, P., and Marinazzo, D., Modeling brain dynamics in brain tumor patients using The Virtual Brain, eNeuro, 2018.
M. A. Akhter, Mahmood, Z., and Scheunders, P., Hyperspectral image subpixel mapping using Getis index, in IEEE-WHISPERS 2013, Workshop on Hyperspectral Image and Signal Processing, Gainesville, Florida, June 25-28, 2013, 2013.
M. A. Akhter, Heylen, R., and Scheunders, P., Geometric matched filter for hyperspectral partial unmixing, in IEEE-Whispers 2014, Workshop on Hyperspectral Image and Signal Processing, Lausanne, Suisse, 2014.
M. A. Akhter, Heylen, R., and Scheunders, P., Hyperspectral unmixing with projection onto convex sets using distance geometry, in IEEE IGARSS 2015, International Geoscience and Remote Sensing Symposium, Milan, Italy, July 26-31, 2015, pp. 5059-5062.
M. A. Akhter, Heylen, R., and Scheunders, P., A geometric matched filter for hyperspectral target detection and partial unmixing, IEEE Geoscience and Remote Sensing letters, vol. 12, pp. 661-665, 2015.
L. F. Alves Pereira, De Beenhouwer, J., and Sijbers, J., Sparse-view Medical Tomosynthesis via Mixed Scale Dense Convolutional Framelet Networks, in IEEE International Symposium on Biomedical Imaging (ISBI), 2023, pp. 880-884.
L. F. Alves Pereira, Janssens, E., Cavalcanti, G. D. C., Tsang, I. R., Van Dael, M., Verboven, P., Nicolai, B., and Sijbers, J., Inline Discrete Tomography system: application to agricultural product inspection, Computers and Electronics in Agriculture, vol. 138, pp. 117–126, 2017.
L. F. Alves Pereira, Janssens, E., Van Dael, M., Verboven, P., Nicolai, B., Cavalcanti, G. D. C., Tsang, I. J., and Sijbers, J., Fast X-ray Computed Tomography via Image Completion, in 6th Conference on Industrial Computed Tomography(iCT), Wels, Austria, 2016, pp. 1-5.
L. F. Alves Pereira, Dabravolski, A., Tsang, I. R., Cavalcanti, G. D. C., and Sijbers, J., Conveyor belt X-ray CT using Domain Constrained Discrete Tomography, in Sibgrapi conference on Graphics, Patterns and Images, 2014, pp. 290 - 297.PDF icon Download paper (3.45 MB)
L. F. Alves Pereira, De Beenhouwer, J., and Sijbers, J., The Deep Steerable Convolutional Framelet Network for Suppressing Directional Artifacts in X-ray Tomosynthesis, in 31st European Signal Processing Conference, EUSIPCO, 2023.
L. F. Alves Pereira, Roelandts, T., and Sijbers, J., Inline 3D X-ray Inspection of Food using Discrete Tomography, in InsideFood Symposium, Leuven, Belgium, 2013.PDF icon Download paper (292.06 KB)
L. F. Alves Pereira, De Beenhouwer, J., Kastner, J., and Sijbers, J., Extreme Sparse X-ray Computed Laminography Via Convolutional Neural Networks, in ICTAI 2020, 2020.PDF icon Download paper (2.5 MB)
L. F. Alves Pereira, Van Nieuwenhove, V., De Beenhouwer, J., and Sijbers, J., A Deep Convolutional Framelet Network based on Tight Steerable Wavelet: application to sparse-view medical tomosynthesis, in International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2021.
V. Anania, Billiet, T., Jeurissen, B., Ribbens, A., den Dekker, A. J., and Sijbers, J., Improved voxel-wise quantification of diffusion and kurtosis metrics in the presence of noise and intensity outliers, 12th Annual Meeting ISMRM Benelux Chapter, Arnhem, The Netherlands. 2020.
V. Anania, Collier, Q., Veraart, J., Buikema, A. Eline, Vanhevel, F., Billiet, T., Jeurissen, B., den Dekker, A. J., and Sijbers, J., Improved diffusion parameter estimation by incorporating T2 relaxation properties into the DKI-FWE model, NeuroImage, vol. 256, p. 119219, 2022.
V. Anania, Jeurissen, B., Morez, J., Buikema, A. Eline, Billiet, T., Sijbers, J., and den Dekker, A. J., Optimal experimental design for the T2-weighted diffusion kurtosis imaging free water elimination model, ESMRMB 2021 Online 38th Annual Scientific Meeting 7–9 October 2021. Magn Reson Mater Phy, vol. 34. pp. S54-S55, 2021.
V. Anania, Billiet, T., Jeurissen, B., Sijbers, J., and den Dekker, A. J., Robust outlier detection for diffusion kurtosis MRI based on IRLLS, 36th Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine & Biology, vol. 32. 2019.
V. Anania, Jeurissen, B., Morez, J., Buikema, A. Eline, Billiet, T., Sijbers, J., and den Dekker, A. J., Optimal acquisition settings for simultaneous diffusion kurtosis, free water fraction and T2 estimation, Joint Annual Meeting ISMRM-ESMRMB. 2022.
V. Andrejchenko, Zahiri, Z., Heylen, R., and Scheunders, P., A spectral mixing model accounting for multiple reflections and shadow, in IGARSS 2019, International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 286-289.
V. Andrejchenko, Hyperspectral image mixture analysis using notions of sparsity, nonlinearity and decision fusion , 2020.
V. Andrejchenko, Liao, W., Philips, W., and Scheunders, P., Decision Fusion Framework for Hyperspectral Image Classification Based on Markov and Conditional Random Fields, Remote Sensing, vol. 11, 2019.PDF icon remotesensing-11-00624.pdf (1.5 MB)
V. Andrejchenko, Heylen, R., Scheunders, P., Philips, W., and Liao, W., Classification of hyperspectral images with very small training size using sparse unmixing, in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 2016.

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