Publications

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Thesis
Ramos-Llordén, G., "Improved MRI Relaxometry through Statistical Signal Processing", Dept. of Physics , vol. Doctor of Science, Antwerp, University of Antwerp, 02/2018. PDF icon Download thesis (19.25 MB)
Poot, D. H. J., "Advances in the reconstruction and statistical processing of Magnetic Resonance images", University of Antwerp, vol. PhD in Sciences: Physics, 2009. PDF icon Download thesis (6.18 MB)
Journal Article
Ramos-Llordén, G., A J. den Dekker, G. Van Steenkiste, B. Jeurissen, F. Vanhevel, J. Van Audekerke, M. Verhoye, and J. Sijbers, "A unified Maximum Likelihood framework for simultaneous motion and T1 estimation in quantitative MR T1 mapping", IEEE Transactions on Medical Imaging, vol. 36, issue 2, pp. 433 - 446, 2017.
Van Dyck, D., E. Bettens, J. Sijbers, M. Op de Beeck, A. van den Bos, A J. den Dekker, J. Jansen, and H. Zandbergen, "Towards quantitative structure determination through electron holographic methods", Materials Characterization, vol. 42, no. 4, pp. 265-281, 1999. PDF icon Download full paper (6.72 MB)
Van Steenkiste, G., D. H. J. Poot, B. Jeurissen, A J. den Dekker, F. Vanhevel, P. M. Parizel, and J. Sijbers, "Super-resolution T1 estimation: quantitative high resolution T1 mapping from a set of low resolution T1 weighted images with different slice orientations", Magnetic Resonance in Medicine, vol. 77, issue 5, pp. 1818–1830, 2017. PDF icon Download paper (3.3 MB)
Van Steenkiste, G., B. Jeurissen, J. Veraart, A J. den Dekker, P. M. Parizel, D. H. J. Poot, and J. Sijbers, "Super-resolution reconstruction of diffusion parameters from diffusion-weighted images with different slice orientations", Magnetic Resonance in Medicine, vol. 75, issue 1, pp. 181-195, 2016.
Fatermans, J., A J. den Dekker, K.. Müller-Caspary, I. Lobato, C.. M. O'Leary, P. D. Nellist, and S. Van Aert, "Single Atom Detection from Low Contrast-to-Noise Ratio Electron Microscopy Images", Phys. Rev. Lett., vol. 121, pp. 056101, Jul, 2018.
den Dekker, A J., and A. van den Bos, "Resolution: a survey", J. Opt. Soc. Am. A, vol. 14, no. 3: OSA, pp. 547–557, Mar, 1997.
Roelandts, T., K. J. Batenburg, A J. den Dekker, and J. Sijbers, "The reconstructed residual error: A novel segmentation evaluation measure for reconstructed images in tomography", Computer Vision and Image Understanding, vol. 126, pp. 28-37, 2014. PDF icon Download paper (4.58 MB)
Ramos-Llordén, G., A J. den Dekker, and J. Sijbers, "Partial Discreteness: a Novel Prior for Magnetic Resonance Image Reconstruction", IEEE Transactions on Medical Imaging, vol. 36, issue 5, pp. 1041 - 1053, 2017. PDF icon Download paper (3.72 MB)
Elberfeld, T., J. De Beenhouwer, A J. den Dekker, C. Heinzl, and J. Sijbers, "Parametric Reconstruction of Glass Fiber-reinforced Polymer Composites from X-ray Projection Data - A Simulation Study", Journal of Nondestructive Evaluation, vol. 37, issue 62, pp. 1573-4862, Jul, 2018.
Sijbers, J., A J. den Dekker, E. Raman, and D. Van Dyck, "Parameter estimation from magnitude MR images", International Journal of Imaging Systems and Technology, vol. 10, no. 2, pp. 109-114, 1999. PDF icon Download paper (350.44 KB)
Gonnissen, J., A. De Backer, A J. den Dekker, G. T. Martinez, A. Rosenauer, J. Sijbers, and S. Van Aert, "Optimal experimental design for the detection of light atoms from high-resolution scanning transmission electron microscopy images", Applied Physics Letters, vol. 105, issue 063116, 2014.
Poot, D. H. J., A J. den Dekker, E. Achten, M. Verhoye, and J. Sijbers, "Optimal experimental design for Diffusion Kurtosis Imaging", IEEE Transactions on Medical Imaging, vol. 29, no. 3, pp. 819-829, 2010. PDF icon Download paper (1.12 MB)
Ramos-Llordén, G., G. Vegas-Sánchez-Ferrero, M. Björk, F. Vanhevel, P. M. Parizel, R. San José Estépar, A J. den Dekker, and J. Sijbers, "NOVIFAST: A fast algorithm for accurate and precise VFA MRI T1 mapping", IEEE Transactions on Medical Imaging, vol. 37, issue 11, pp. 2414 - 2427, 2018. PDF icon Download paper (3.3 MB)
Sudeep, P. V., P. Palanisamy, C. Kesavadas, J. Sijbers, A J. den Dekker, and J. Rajan, "A nonlocal maximum likelihood estimation method for enhancing magnetic resonance phase maps", Signal Image and Video Processing, vol. 11, issue 5, pp. 913-920, 2017.
Rajan, J., A J. den Dekker, and J. Sijbers, "A new non local maximum likelihood estimation method for Rician noise reduction in Magnetic Resonance images using the Kolmogorov-Smirnov test", Signal Processing, vol. 103, pp. 16-23, 2014.
Bettens, E., D. Van Dyck, A J. den Dekker, J. Sijbers, and A. van den Bos, "Model-based two-object resolution from observations having counting statistics", Ultramicroscopy, vol. 77, no. 1, pp. 37-48, 1999. PDF icon Download full paper (226 KB)
Sijbers, J., and A J. den Dekker, "Maximum Likelihood estimation of signal amplitude and noise variance from MR data", Magnetic Resonance in Medicine, vol. 51, no. 3, pp. 586-594, 2004. PDF icon Download full paper (295.12 KB)
Sijbers, J., A J. den Dekker, P. Scheunders, and D. Van Dyck, "Maximum Likelihood estimation of Rician distribution parameters", IEEE Transactions on Medical Imaging, vol. 17, no. 3, pp. 357-361, 1998. PDF icon Download paper (106.26 KB)
Fatermans, J., S. Van Aert, and A J. den Dekker, "The maximum a posteriori probability rule for atom column detection from HAADF STEM images", Ultramicroscopy, vol. 201, pp. 81-91, 2019.
Sijbers, J., A J. den Dekker, and R. Bos, "A likelihood ratio test for functional MRI data analysis to account for colored noise", Lecture Notes in Computer Science, vol. 3708, pp. 538-546, September, 2005. PDF icon Download full paper (483.15 KB)
den Dekker, A J., D. H. J. Poot, R. Bos, and J. Sijbers, "Likelihood based hypothesis tests for brain activation detection from MRI data disturbed by colored noise: a simulation study", IEEE Transactions on Medical Imaging, vol. 28, no. 2, pp. 287-296, February, 2009.
Beirinckx, Q., G. Ramos-Llordén, B. Jeurissen, D. H. J. Poot, P. M. Parizel, M. Verhoye, J. Sijbers, and A J. den Dekker, "Joint Maximum Likelihood estimation of motion and T1 parameters from magnetic resonance images in a super-resolution framework: a simulation study", Fundamenta Informaticae, In Press.

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