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B. De Samber, Renders, J., Elberfeld, T., Maris, Y., Sanctorum, J., Six, N., Liang, Z., De Beenhouwer, J., and Sijbers, J., “FleXCT: a Flexible X-ray CT scanner with 10 degrees of freedom”, Optics Express, vol. 29, no. 3, pp. 3438-3457, 2021.PDF icon Download paper (14.25 MB)
A. De Luca, Ianus, A., Leemans, A., Palombo, M., Shemesh, N., Zhang, H., Alexander, D. C., Nilsson, M., Froeling, M., Biessels, G. - J., Zucchelli, M., Frigo, M., Albay, E., Sedlar, S., Alimi, A., Deslauriers-Gauthier, S., Deriche, R., Fick, R., Afzali, M., Pieciak, T., Bogusz, F., Aja-Fernandez, S., Ozarslan, E., Jones, D. K., Chen, H., Jin, M., Zhang, Z., Wang, F., Nath, V., Parvathaneni, P., Morez, J., Sijbers, J., Jeurissen, B., Shreyas,, Fadnavis,, Endres, S., Rokem, A., Garyfallidis, E., Sanchez, I., Prchkovska, V., Rodrigues, P., Landman, B. A., and Schilling, K. G., “On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge”, NeuroImage, vol. 240, no. 118367, 2021.
T. De Kerf, “Hyperspectral imaging for automated inspection of offshore wind infrastructure”, 2023.
T. De Kerf, Gestels, A., Janssens, K., Steenackers, G., Scheunders, P., and Vanlanduit, S., “Quantitative detection of corrosion minerals in carbon steel using shortwave infrared hyperspectral imaging”, RSC Advances, vol. 12, pp. 32775-32783, 2022.
T. De Kerf, Pipintakos, G., Zahiri, Z., Vanlanduit, S., and Scheunders, P., “Identification of corrosion minerals usning shortwave infrared hyperspectral imaging”, Sensors, vol. 22, no. 1, p. 407, 2022.PDF icon sensors-22-00407-v2.pdf (4.41 MB)
G. De Groof, Verhoye, M., Leemans, A., and Van Der Linden, A., “Using diffusion tensor imaging (DTI) to assess the neuronal plasticity in the brain of a songbird”, in 4th Annual Symposium – Young Belgian Magnetic Resonance Scientists, Brussels, Belgium, 2005.
G. De Groof, Verhoye, M., Van Meir, V., Tindemans, I., Leemans, A., and Van Der Linden, A., “In Vivo Visualization of the Neuroanatomy and Brain Connectivity of Starling Brain Through Diffusion Tensor Imaging”, in 6th Bi-Annual Meeting – Belgian Society for Neuroscience, Brussels, Belgium, 2005.
G. De Groof, Verhoye, M., Leemans, A., and Van Der Linden, A., “Seasonal changes in neuronal connectivity in the songbird brain discerned by repeated in vivo DTI”, in 13th Scientific Meeting - International Society for Magnetic Resonance in Medicine, Miami, USA, 2005, p. 715.
G. De Groof, Verhoye, M., Leemans, A., and Van Der Linden, A., “DTI parameters: Fractional Anisotropy, Radial and Axial Diffusivity reveal seasonal neuroplasticity in the adult songbird brain”, in 4th annual meeting of the Society of Molecular Imaging, Keulen, Germany, 2005.
G. De Groof, Verhoye, M., Leemans, A., Sijbers, J., and Van Der Linden, A., “Paired voxel-wise statistical mapping of in vivo Diffusion Tensor Imaging (DTI) data to assess the seasonal neuronal plasticity in the brain of a songbird”, in 1st Annual Meeting - European Society of Molecular Imaging, Paris, France, 2006.
G. De Groof, Verhoye, M., Van Meir, V., Tindemans, I., Leemans, A., and Van Der Linden, A., “In vivo diffusion tensor imaging (DTI) of brain subdivisions and vocal pathways in songbirds”, NeuroImage, vol. 29, pp. 754-763, 2006.
G. De Groof, Verhoye, M., Leemans, A., and Van Der Linden, A., “Using diffusion tensor imaging (DTI) to assess the neuronal plasticity and neuroconnectivity in the brain of a songbird”, in Molecular & Cellular Basis of Neuroconnectivity, Leuven, Belgium, 2006.
G. De Groof, Verhoye, M., Leemans, A., and Van Der Linden, A., “Using diffusion tensor imaging (DTI) to assess the neuronal plasticity in the brain of a songbird”, in XXeme Congres du Groupement d'Etudes de Resonance Magnetique, Blankenberge, Belgium, 2006.
F. De Carlo, Gursoy, D., Ching, D., Batenburg, K. J., Ludwig, W., Mancini, L., Welford, F. M., Mokso, R., Pelt, D., Sijbers, J., and Rivers, M., “TomoBank: A Tomographic Data Repository for Computational X-ray Science”, Measurement Science and Technology, vol. 29, no. 3, pp. 1-10, 2018.PDF icon Download paper (5.71 MB)
T. De Bondt, Jacquemyn, Y., Van Hecke, W., Sijbers, J., Sunaert, S., and Parizel, P. M., “Regional gray matter volume differences and sex-hormone correlations as a function of menstrual cycle phase and hormonal contraceptives use.”, Brain research, vol. 1530, pp. 22-31, 2013.
T. De Bondt, Van Hecke, W., Veraart, J., Leemans, A., Sijbers, J., Sunaert, S., Jacquemyn, Y., and Parizel, P. M., “Does the use of hormonal contraceptives cause microstructural changes in cerebral white matter? Preliminary results of a DTI and tractography study.”, European radiology, vol. 23, no. 1, pp. 57-64, 2013.PDF icon Download paper (411.18 KB)
B. De Boeck, “Infotree - A study of its foundations”, University of Antwerp, Antwerp, 2001.
B. De Boeck, Scheunders, P., and Van Dyck, D., “From inductive inference to the fundamental equations of measuerement”, in Proc. First International Conference on Complex Systems, 2000, pp. 115-122.
J. De Beenhouwer, Palenstijn, W. J., Bleichrodt, F., Batenburg, K. J., and Sijbers, J., “A framework for markerless alignment with full 3D flexibility”, European Microscopy Conference. 2012.PDF icon Download abstract (117.9 KB)
J. De Beenhouwer and Sijbers, J., “A PHASE‑CONTRAST X‑RAY IMAGING SYSTEM FOR OBTAINING A DARK‑FIELD IMAGE AND A METHOD THEREFOR”, U.S. Patent EP 4 351 425 B12025.PDF icon Download patent (547.58 KB)
S. De Backer, Cornelissen, F., Lemeire, J., Nuydens, R., Meert, T., Schelkens, P., and Scheunders, P., “Mosiacing of Fibered Fluorescence Microscopy Video”, Lecture notes in Computer Science, vol. 5259, pp. 915-923, 2008.
S. De Backer, Kempeneers, P., Debruyn, W., and Scheunders, P., “A Band Selection Technique for Spectral Classification”, IEEE Geoscience and Remote Sensing Letters, vol. 2, pp. 319-323, 2005.
S. De Backer and Scheunders, P., “A competitive elliptical clustering algorithm”, Pattern Recognition Letters, vol. 20, pp. 1141-1147, 1999.
S. De Backer, Kempeneers, P., Debruyn, W., and Scheunders, P., “Classification of Dune Vegetation from Remotely Sensed Hyperspectral Images”, in Image Analysis and Recognition Proc. of International Conference on Image Analysis and Recognition, Porto, Portugal, 2004, pp. 497-503.
A. De Backer, Fatermans, J., den Dekker, A. J., and Van Aert, S., “Chapter Five - Optimal experiment design for nanoparticle atom counting from ADF STEM images”, in Advances in Imaging and Electron Physics, vol. 217, Science Direct Elsevier, 2021.

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