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2022
Y. - T. Ling, Cools, S., Bogdanowicz, J., Fleischmann, C., De Beenhouwer, J., Sijbers, J., and Vandervorst, W., A Bottom-Up Volume Reconstruction Method for Atom Probe Tomography, Microscopy and Microanalysis, vol. 28, no. 4, pp. 1-14, 2022.
B. Rasti, Koirala, B., Scheunders, P., and Chanussot, J., Deep Blind Unmixing using Minimum Simplex Convolutional Network, in IEEE International Geoscience and Remote Sensing Symposium, 2022, pp. 28-31.PDF icon misicnet_igarss2022.pdf (1.29 MB)
J. Christopher, Lumbeeck, L. - P., Paramonov, P., De Beenhouwer, J., and Sijbers, J., Discrete Terahertz tomography: a simulation study, e-Journal of Nondestructive Testing, vol. 27, no. 3, 2022.PDF icon Download paper (345.5 KB)
G. Barisano, Sepehrband, F., Collins, H. R., Jillings, S., Jeurissen, B., Taylor, A. J., Schoenmaekers, C., De Laet, C., Rukavishnikov, I., Nosikova, I., Rumshiskaya, A., Annen, J., Sijbers, J., Laureys, S. S. L., Van Ombergen, A., Petrovichev, V., Sinitsyn, V., Pechenkova, E. V., Grishin, A., Eulenburg, Pzu, Law, M., Sunaert, S., Parizel, P. M., Tomilovskaya, E., Roberts, D., and Wuyts, F. L., The effect of prolonged Spaceflight on Cerebrospinal Fluid and Perivascular Spaces of Astronauts and Cosmonauts, PNAS, vol. 119, no. 17, 2022.
G. Pascoletti, Huysmans, T., Conti, P., and Zanetti, E. M., Evaluation of a Morphable Anthropomorphic Articulated Total Body Model, in Design Tools and Methods in Industrial Engineering II - Proceedings of the 2nd International Conference on Design Tools and Methods in Industrial Engineering, ADM 2021, 2022, pp. 761–772.
G. Pascoletti, Huysmans, T., Conti, P., and Zanetti, E. M., Evaluation of a Morphable Anthropomorphic Articulated Total Body Model, in Design Tools and Methods in Industrial Engineering II - Proceedings of the 2nd International Conference on Design Tools and Methods in Industrial Engineering, ADM 2021, 2022, pp. 761–772.
G. Pascoletti, Huysmans, T., Conti, P., and Zanetti, E. M., Evaluation of a Morphable Anthropomorphic Articulated Total Body Model, in Design Tools and Methods in Industrial Engineering II - Proceedings of the 2nd International Conference on Design Tools and Methods in Industrial Engineering, ADM 2021, 2022, pp. 761–772.
D. Iuso, Chatterjee, S., Heylen, R., Cornelissen, S., De Beenhouwer, J., and Sijbers, J., Evaluation of deeply supervised neural networks for 3D pore segmentation in additive manufacturing, in SPIE Optical Engineering: Developments in X-Ray Tomography XIV , 2022, vol. 12242, p. 122421K.PDF icon Download paper (protected) (1.79 MB)
D. Iuso, Chatterjee, S., Heylen, R., Cornelissen, S., De Beenhouwer, J., and Sijbers, J., Evaluation of deeply supervised neural networks for 3D pore segmentation in additive manufacturing, in SPIE Optical Engineering: Developments in X-Ray Tomography XIV , 2022, vol. 12242, p. 122421K.PDF icon Download paper (protected) (1.79 MB)
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.
B. Rasti, Koirala, B., Scheunders, P., and Chanussot, J., MiSiCNet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, no. 5522815, 2022.PDF icon misicnet_ieee_tgrs_author_version.pdf (5.57 MB)
B. Rast, Koirala, B., Scheunders, P., and Chanussot, J., MiSiCNet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022.PDF icon ieee_journal_misicnet.pdf (11.02 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.
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)
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.
2021
M. Naeyaert, Aelterman, J., Golkov, V., Cremers, D., Pizurica, A., Sijbers, J., and Verhoye, M., Accelerating in vivo fast spin echo high angular resolution diffusion imaging with an isotropic resolution in mice through compressed sensing, Magnetic Resonance in Medicine, vol. 85, no. 3, pp. 1397-1413, 2021.
M. Roshani, Phan, G., Roshani, G. Hossein, Hanus, R., Corniani, E., and Nazemi, E., Combination of X-ray tube and GMDH neural network as a nondestructive and potential technique for measuring characteristics of gas-oil–water three phase flows, Measurement, vol. 168, 2021.
M. Roshani, Phan, G. T. T., Roshani, G. Hossein, Hanus, R., Duong, T., Corniani, E., Nazemi, E., and Kalmouni, E. M., Evaluation of flow pattern recognition and void fraction measurement in two phase flow independent of oil pipeline’s scale layer thickness, Alexandria Engineering Journal, vol. 60, 2021.
M. Naeyaert, Golkov, V., Cremers, D., Sijbers, J., and Verhoye, M., Faster and better HARDI using FSE and holistic reconstruction, International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting. 2021.
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.
M. Siqueira Pinto, Winzeck, S., Correia, M. M., Kornaropoulos, E. N., Menon, D. K., Glocker, B., den Dekker, A. J., Sijbers, J., Guns, P. - J., Van Dyck, P., and Newcombe, V. F. J., Outcome prediction in Mild Traumatic Brain Injury patients using conventional and diffusion MRI via Support Vector Machine: A CENTER-TBI study, ISMRM & SMRT Annual Meeting. 2021.
M. Siqueira Pinto, Winzeck, S., Richter, S., Correia, M. M., Kornaropoulos, E. N., Menon, D. K., Glocker, B., Guns, P. - J., den Dekker, A. J., Sijbers, J., Newcombe, V. F. J., and Van Dyck, P., Outcome prediction of mild traumatic brain injury using support vector machine based on longitudinal MRdiffusion imaging from CENTER-TBI, Magn Reson Mater Phy (ESMRMB), vol. 34. p. S54, 2021.
E. Ribeiro Sabidussi, Caan, M., Bazrafkan, S., den Dekker, A. J., Sijbers, J., Niessen, W. J., and Poot, D. H. J., Recurrent Inference Machines as Inverse Problem Solvers for MR Relaxometry, in MIDL 2021 - Medical Imaging with Deep Learning, 2021.
E. Ribeiro Sabidussi, Klein, S., Caan, M., Bazrafkan, S., den Dekker, A. J., Sijbers, J., Niessen, W. J., and Poot, D. H. J., Recurrent Inference Machines as inverse problem solvers for MR relaxometry, Medical Image Analysis, vol. 74, pp. 1-11, 2021.PDF icon Download paper (2.26 MB)

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