Publications

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Thesis
Q. Collier, “Robust estimation of diffusion tensor and diffusion kurtosis imaging parameters”, University of Antwerp, Antwerp, 2018.
J. Cant, “Iterative reconstruction for mobile chest tomosynthesis”, 2017.PDF icon Download thesis (5.77 MB)
Patent
J. Cant and Sijbers, J., “Method and system for correcting geometric misalignment during image reconstruction in chest tomosynthesis”, U.S. Patent EP16173767.12016.
J. Cant and Sijbers, J., “Computerized Tomographic Image Exposure and Reconstruction Method”, U.S. Patent PCT/EP2015/0599562016.PDF icon Download patent (595.32 KB)
Journal Article
H. Zivari Adab, Chalavi, S., Beets, I., Gooijers, J., Leunissen, I., Cheval, B., Collier, Q., Sijbers, J., Jeurissen, B., Swinnen, S. P., and Boisgontier, M., “White matter microstructural organization of interhemispheric pathways predicts different stages of bimanual coordination learning in young and older adults”, European Journal of Neuroscience , vol. 47, no. 5, pp. 446–459, 2018.
H. Zivari Adab, Chalavi, S., Beets, I., Gooijers, J., Leunissen, I., Cheval, B., Collier, Q., Sijbers, J., Jeurissen, B., Swinnen, S. P., and Boisgontier, M., “White matter microstructural organization of interhemispheric pathways predicts different stages of bimanual coordination learning in young and older adults”, European Journal of Neuroscience , vol. 47, no. 5, pp. 446–459, 2018.
H. Zivari Adab, Chalavi, S., Beets, I., Gooijers, J., Leunissen, I., Cheval, B., Collier, Q., Sijbers, J., Jeurissen, B., Swinnen, S. P., and Boisgontier, M., “White matter microstructural organization of interhemispheric pathways predicts different stages of bimanual coordination learning in young and older adults”, European Journal of Neuroscience , vol. 47, no. 5, pp. 446–459, 2018.
D. Iuso, Chatterjee, S., Cornelissen, S., Verhees, D., De Beenhouwer, J., and Sijbers, J., “Voxel-wise classification for porosity investigation of additive manufactured parts with 3D unsupervised and (deeply) supervised neural models”, Applied Intelligence, In Press.
D. Iuso, Chatterjee, S., Cornelissen, S., Verhees, D., De Beenhouwer, J., and Sijbers, J., “Voxel-wise classification for porosity investigation of additive manufactured parts with 3D unsupervised and (deeply) supervised neural models”, Applied Intelligence, In Press.
K. Hufkens, Scheunders, P., and Ceulemans, R., “Validation of the sigmoid wave curve fitting algorithm on a forest-tundra ecotone in the Northwest Territories, Canada”, Ecological Informatics, vol. 4, pp. 1-7, 2009.
C. C. Hung, Scheunders, P., Pham, M., Su, M. C., and Coleman, T., “Using Intelligent Optimization Techniques in the K-means Algorithm for Multispectral Image Classification”, International Journal of Fuzzy Systems, vol. 6, pp. 107-117, 2004.
M. Siqueira Pinto, Winzeck, S., Kornaropoulos, E. N., Richter, S., Paolella, R., Correia, M. M., Glocker, B., Williams, G., Vik, A., Posti, J., Håberg, A. Kristine, Stenberg, J., Guns, P. - J., den Dekker, A. J., Menon, D. K., Sijbers, J., Van Dyck, P., and Newcombe, V. F. J., “Use of support vector machines approach via ComBat harmonized diffusion tensor imaging for the diagnosis and prognosis of mild traumatic brain injury: a CENTER-TBI study”, Journal of Neurotrauma, vol. 40, no. 13-14, pp. 1317-1338, 2023.
W. S. Oliveira, Teixeira, J. V., Tsang, I. R., Cavalcanti, G. D. C., and Sijbers, J., “Unsupervised Retinal Vessel Segmentation Using Combined Filters”, Plos One, vol. 11, pp. 1-21, 2016.
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)
P. Bladt, van Osch, M. J. P., Clement, P., Achten, E., Sijbers, J., and den Dekker, A. J., “Supporting measurements or more averages? How to quantify cerebral blood flow most reliably in 5 minutes by arterial spin labeling”, Magnetic Resonance in Medicine, vol. 84, no. 5, pp. 2523-2536, 2020.PDF icon Download paper (1.3 MB)
B. Koirala, Khodadadzadeh, M., Contreras, C., Zahiri, Z., Gloaguen, R., and Scheunders, P., “A supervised method for nonlinear hyperspectral unmixing”, Remote Sensing, vol. 11, no. 20 , 2019.PDF icon remotesensing-11-02458-v3.pdf (3.23 MB)
L. Serbruyns, Leunissen, I., Huysmans, T., Cuypers, K., Meesen, R. L., van Ruitenbeek, P., Sijbers, J., and Swinnen, S. P., “Subcortical volumetric changes across the adult lifespan: subregional thalamic atrophy accounts for age-related sensorimotor performance declines”, Cortex, vol. 65, pp. 128-138, 2015.PDF icon paper (1.35 MB)
S. Sekar, Jonckers, E., Verhoye, M., Willems, R., Veraart, J., Van Audekerke, J., Couto, J., Giugliano, M., Wuyts, K., Dedeurwaerdere, S., Sijbers, J., Mackie, C., Ver Donck, L., Steckler, T., and Van Der Linden, A., “Subchronic memantine induced concurrent functional disconnectivity and altered ultra-structural tissue integrity in the rodent brain: revealed by multimodal MRI.”, Psychopharmacology, vol. 227, no. 3, pp. 479-91, 2013.
N. J. Forde, Ronan, L., Suckling, J., Scanlon, C., Neary, S., Holleran, L., Leemans, A., Tait, R., Rua, C., Fletcher, P. C., Jeurissen, B., Dodds, C. M., Miller, S. R., Bullmore, E. T., McDonald, C., Nathan, P. J., and Cannon, D. M., “Structural neuroimaging correlates of allelic variation of the BDNF val66met polymorphism.”, NeuroImage, vol. 90, pp. 280-9, 2014.
N. J. Forde, O'Donoghue, S., Scanlon, C., Emsell, L., Chaddock, C., Leemans, A., Jeurissen, B., Barker, G. J., Cannon, D. M., Murray, R. M., and others, “Structural brain network analysis in families multiply affected with bipolar I disorder”, Psychiatry Research: Neuroimaging, vol. 234, pp. 44–51, 2015.
N. J. Forde, O'Donoghue, S., Scanlon, C., Emsell, L., Chaddock, C., Leemans, A., Jeurissen, B., Barker, G. J., Cannon, D. M., Murray, R. M., and others, “Structural brain network analysis in families multiply affected with bipolar I disorder”, Psychiatry Research: Neuroimaging, vol. 234, pp. 44–51, 2015.
Y. Huybrechts, De Ridder, R., De Samber, B., Boudin, E., Tonelli, F., Knapen, D., Schepers, D., De Beenhouwer, J., Sijbers, J., Forlino, A., Coucke, P., P. Witten, E., Kwon, R., Willaert, A., Hendrickx, G., and Van Hul, W., “The sqstm1tmΔUBA zebrafish model, a proof-of-concept in vivo model for Paget’s disease of bone?”, Bone Reports, vol. 16, no. 101483, pp. 75-76, 2022.
G. Liu, Nath, T., Guo, Z., Linneweber, G., Claeys, A., Li, J., Bengochea, M., De Backer, S., Weyn, B., Sneyders, M., Nicasy, H., Yu, P., Scheunders, P., and Hassan, B., “A simple computer vision pipeline reveals the effects of isolation on social interaction dynamics in Drosophila”, Plos Computational Biology, vol. 14, no. 8, p. e1006410, 2018.
G. Zhang, Scheunders, P., Cerra, D., and Muller, R., “Shadow-aware nonlinear spectral unmixing for hyperspectral imagery”, IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 5514-5533, 2022.PDF icon shadow-aware_nonlinear_spectral_unmixing_for_hyperspectral_imagery.pdf (9.51 MB)
N. Chabi, Iuso, D., Beuing, O., Preim, B., and Saalfeld, S., “Self-calibration of C-arm imaging system using interventional instruments during an intracranial biplane angiography.”, Int J Comput Assist Radiol Surg, 2022.

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