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B. Auer, Zeraatkar, N., De Beenhouwer, J., Kalluri, K., Kuo, P. H., Furenlid, L. R., and King, M. A., Investigation of a Monte Carlo simulation and an analytic-based approach for modeling the system response for clinical I-123 brain SPECT imaging, in 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, vol. 11072, pp. 187 – 190.
B. Auer, Kalluri, K., De Beenhouwer, J., Zeraatkar, N., Momsen, N., Kuo, P. H., Furenlid, L. R., and King, M. A., Keel-Edge Height Selection for Improved Multi-Pinhole 123I Brain SPECT Imaging, Journal of Nuclear Medicine, vol. 61, p. 573, 2020.PDF icon Download paper (127.73 KB)
B. Auer, Könik, A., Fromme, T. J., Kalluri, K., De Beenhouwer, J., Zubal, G. I., Furenlid, L. R., and King, M. A., Preliminary evaluation of surface mesh modeling of system geometry, anatomy phantom, and source activity for GATE simulations, in IEEE Nuclear Science Symposium and Medical Imaging Conference, Sydney, Australia, 2018.
B. Auer, De Beenhouwer, J., Zeraatkar, N., Kuo, P. H., Furenlid, L. R., and King, M. A., Preliminary investigation of attenuation and scatter correction strategies for a next-generation SPECT system dedicated to quantitative clinical brain imaging, in IEEE Nuclear Science Symposium and Medical Imaging Conference, Manchester, UK, 2019.
B. Auer, Kalluri, K., De Beenhouwer, J., Doty, K., Zeraatkar, N., Kuo, P. H., Furenlid, L. R., and King, M. A., Reconstruction using Depth of Interaction Information of Curved and Flat Detector Designs for Quantitative Multi-Pinhole Brain SPECT, Journal of Nuclear Medicine, vol. 61, p. 103, 2020.PDF icon snm2020_recon_curved.pdf (103.23 KB)
B. Auer, De Beenhouwer, J., Fromme, T. J., Kalluri, K., Goding, J. C., Zubal, G. I., Furenlid, L. R., and King, M. A., Preliminary investigation of design parameters of an innovative multi- pinhole system dedicated to brain SPECT imaging, in IEEE Nuclear Science Symposium and Medical Imaging Conference, Sydney, Australia, 2018.
B. Auer, Kalluri, K., De Beenhouwer, J., Zeraatkar, N., Könik, A., Kuo, P. H., Furenlid, L. R., and King, M. A., Investigation of keel versus knife edge pinhole profiles for a next-generation SPECT system dedicated to clinical brain imaging, 2nd International Conference on Monte Carlo Techniques for Medical Applications. 2019.
B. Auer, Kalluri, K., Abayazeed, A. H., De Beenhouwer, J., Zeraatkar, N., Lindsay, C., Momsen, N., R. Richards, G., May, M., Kupinski, M. A., Kuo, P. H., Furenlid, L. R., and King, M. A., Aperture size selection for improved brain tumor detection and quantification in multi-pinhole 123I-CLINDE SPECT imaging, in IEEE Nuclear Science Symposium and Medical Imaging Conference, Boston, USA (2020), 2020.
M. Shahrimie Asaari, Mishra, P., Mertens, S., Dhondt, S., Wuyts, N., and Scheunders, P., Modeling effects of illumination and plant geometry on leaf reflectance spectra in close-range hyperspectral imaging, in 8th WHISPERS - Evolution in Remote Sensing, Los Angeles, USA, 2016.
M. Shahrimie Asaari, Mertens, S., Dhondt, S., Inze, D., Wuyts, N., and Scheunders, P., Analysis of hyperspectral images for detection of drought stress and recovery in maize plants in a high-throughput phenotyping platform, Computers and Electronics in Agriculture, vol. 162, pp. 749-758, 2019.
M. Shahrimie Asaari, Mishra, P., Mertens, S., Dhondt, S., Inze, D., Wuyts, N., and Scheunders, P., Close-range hyperspectral image analysis for the early detection of plant stress responses in individual plants in a high-throughput phenotyping platform, ISPRS Journal of Photogrammetry and Remote Sensing , vol. 138, pp. 121-138, 2018.
M. Shahrimie Asaari, Mertens, S., Dhondt, S., Wuyts, N., and Scheunders, P., Detection of plant responses to drought using close-range hyperspectral imaging in a high-throughput phenotyping platform, in IEEE Whispers 2018, Workshop on Hyperspectral Image and Signal Processing, Amsterdam, 23-26 September , 2018.
G. Araizi-Kanoutas, Geessinck, J., Gauquelin, N., Smit, S., Verbeeck, X. H., Mishra, S. K., Bencok, P., Schlueter, C., Lee, T. - L., Krishnan, D., Fatermans, J., Verbeeck, J., Rijnders, G., Koster, G., and Golden, M. S., Co valence transformation in isopolar LaCoO3/LaTiO3 perovskite heterostructures via interfacial engineering, Phys. Rev. Materials, vol. 4, 2020.
B. T. Antonsen, Jiang, Y., Veraart, J., Qu, H., Nguyen, H. P., Sijbers, J., Von Hörsten, S., Johnson, A. G., and Leergaard, T. B., Altered diffusion tensor imaging measurements in aged transgenic Huntington disease rats., Brain structure & function, vol. 218, no. 3, pp. 767-78, 2013.PDF icon Download paper (645.02 KB)
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, 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.
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.
V. Andrejchenko, Heylen, R., Liao, W., Philips, W., and Scheunders, P., MRF-based decision fusion for hyperspectral image classification, in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 2018.
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, 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.
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.
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, 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)

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