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C. Smekens, Vanhevel, F., Jeurissen, B., Van Dyck, P., Sijbers, J., and Janssens, T., Short T2* quantification of knee structures based on accelerated UTE Spiral VIBE MRI with SPIRiT reconstruction, 12th Annual Meeting of the ISMRM Benelux Chapter, Arnhem, The Netherlands. 2020.
N. Six, Renders, J., Sijbers, J., and De Beenhouwer, J., Gauss-Newton-Krylov for Reconstruction of Polychromatic X-ray CT Images, IEEE Transactions on Computational Imaging, vol. 7, pp. 1304-1313, 2021.
N. Six, De Beenhouwer, J., Van Nieuwenhove, V., Vanroose, W., and Sijbers, J., Joint reconstruction and flat-field estimation using support estimation, in IEEE Nuclear Science Symposium and Medical Imaging Conference, Sydney, Australia, 2018.PDF icon Download paper (1.53 MB)
N. Six, De Beenhouwer, J., and Sijbers, J., poly-DART: A discrete algebraic reconstruction technique for polychromatic X-ray CT, Optics Express, vol. 27, no. 23, pp. 33427-33435, 2019.PDF icon Download paper (997.08 KB)
N. Six, Renders, J., De Beenhouwer, J., and Sijbers, J., Joint reconstruction of attenuation, refraction and dark field X-ray phase contrasts using split Barzilai-Borwein steps, in SPIE Optical Engineering: Developments in X-Ray Tomography XIV , 2022, vol. 12242, p. 122420O.
N. Six, Renders, J., De Beenhouwer, J., and Sijbers, J., Joint multi-contrast CT for edge illumination X-ray phase contrast imaging using split Barzilai-Borwein steps, Optics Express, vol. 32, no. 2, pp. 1135-1150, 2024.PDF icon Download paper (13.42 MB)
N. Six, Renders, J., Sijbers, J., and De Beenhouwer, J., Newton-Krylov Methods For Polychromatic X-Ray CT, in 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, 2020, pp. 3045-3049.
N. Six, De Beenhouwer, J., and Sijbers, J., pDART: Discrete algebraic reconstruction using a polychromatic forward model, in The Fifth International Conference on Image Formation in X-Ray Computed Tomography, Salt Lake City, Utah, USA, 2018.PDF icon Download paper (1.61 MB)
N. Six, Improved X-ray CT reconstruction techniques with non-linear imaging models, 2024.
M. Siqueira Pinto, Paolella, R., Billiet, T., Van Dyck, P., Guns, P. - J., Jeurissen, B., Ribbens, A., den Dekker, A. J., and Sijbers, J., Harmonisation of Brain Diffusion MRI: Concepts and Methods, Frontiers in Neuroscience , vol. 14, pp. 1-17, 2020.PDF icon Download paper (2.61 MB)
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.
M. Siqueira Pinto, Paolella, R., Billiet, T., Van Dyck, P., Guns, P. - J., Jeurissen, B., Ribbens, A., den Dekker, A. J., and Sijbers, J., Voxelwise harmonisation of FA on a cohort of 605 healthy subjects using ComBat: an exploratory study, ESMRMB 2019, 36th Annual Scientific Meeting, Rotterdam, NL. 2019.
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.
J. Sijbers, den Dekker, A. J., and Bos, R., A likelihood ratio test for functional MRI data analysis to account for colored noise, Lecture Notes in Computer Science, vol. 3708, pp. 538-546, 2005.PDF icon Download full paper (483.15 KB)
J. Sijbers, den Dekker, A. J., Raman, E., and Van Dyck, D., Parameter estimation from magnitude MR images, International Journal of Imaging Systems and Technology, vol. 10, pp. 109-114, 1999.PDF icon Download paper (350.44 KB)
J. Sijbers, Scheunders, P., Van Dyck, D., and Raman, E., Proceedings of the Royal Microscopical Society, in Optimization of the SNR in NMR images using image sequences, London, UK, 1994, vol. 29, p. 232.
J. Sijbers, Vanrumste, B., Van Hoey, G., Boon, P., Verhoye, M., Van Der Linden, A., and Van Dyck, D., Automatic localization of EEG electrode markers within 3D MR data, Magnetic Resonance Imaging, vol. 18, pp. 485-488, 2000.PDF icon Download paper (178.74 KB)
J. Sijbers, den Dekker, A. J., Poot, D. H. J., Bos, R., Verhoye, M., Van Camp, N., and Van Der Linden, A., Robust estimation of the noise variance from background MR data, in Proceedings of SPIE Medical Imaging: Image Processing, San Diego, CA, USA, 2006, vol. 6144, pp. 2018-2028.
J. Sijbers, den Dekker, A. J., Verhoye, M., and Van Dyck, D., Optimal estimation of T2 maps from magnitude MR data, in Proceedings of SPIE Medical Imaging, San Diego, CA, USA, 1998, vol. 3338, pp. 384-390.PDF icon Download paper (1.04 MB)
J. Sijbers, Vanrumste, B., Van Hoey, G., Boon, P., Verhoye, M., Van Der Linden, A., and Van Dyck, D., Automatic detection of EEG electrode markers on 3D MR data, in SPIE Medical Imaging: Image Processing, San Diego CA, USA, 2000, vol. 3979, pp. 1476-1481.
J. Sijbers and Postnov, A., Reduction of ring artifacts in high resolution micro-CT reconstructions, Physics in Medicine and Biology, vol. 49, pp. 247-253, 2004.
J. Sijbers, Scheunders, P., Van Dyck, D., and Raman, E., Noise quantification prior to image restoration, in Meeting of the Dutch Society for Pattern Recognition and Image Processing, Best, The Netherlands, 1997.
J. Sijbers, Van Der Linden, A., Scheunders, P., Van Audekerke, J., Van Dyck, D., and Raman, E., Volume quantization of the mouse cerebellum by semi-automatic 3D segmentation of MR images, in Proceedings of SPIE Medical Imaging, Newport Beach CA, USA, 1996, vol. 2710, pp. 553-560.
J. Sijbers, Poot, D. H. J., den Dekker, A. J., and Pintjens, W., Automatic estimation of the noise variance from the histogram of a magnetic resonance image, Physics in Medicine and Biology, vol. 52, pp. 1335-1348, 2007.PDF icon Download paper (297.18 KB)

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