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Arnold J. den Dekker
Person information
- affiliation: University of Antwerp, Department of Physics, Belgium
- affiliation (PhD 1997): Delft University of Technology, The Netherlands
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2020 – today
- 2024
- [j23]Quinten Beirinckx
, Piet Bladt
Model-based super-resolution reconstruction for pseudo-continuous Arterial Spin Labeling. NeuroImage 286: 120506 (2024) - 2023
- [c23]Diana L. Giraldo, Quinten Beirinckx
Super-Resolution Reconstruction of Multi-Slice T2-W FLAIR MRI Improves Multiple Sclerosis Lesion Segmentation. EMBC 2023: 1-4 - [c22]Jens Renders
Delta-MRI: Direct Deformation Estimation from Longitudinally Acquired K-Space Data. ISBI 2023: 1-4 - 2022
- [j22]Quinten Beirinckx
Model-based super-resolution reconstruction with joint motion estimation for improved quantitative MRI parameter mapping. Comput. Medical Imaging Graph. 100: 102071 (2022) - [j21]Vincenzo Anania
Improved diffusion parameter estimation by incorporating T2 relaxation properties into the DKI-FWE model. NeuroImage 256: 119219 (2022) - 2021
- [j20]Emanoel R. Sabidussi, Stefan Klein
Recurrent inference machines as inverse problem solvers for MR relaxometry. Medical Image Anal. 74: 102220 (2021) - [c21]Banafshe Shafieizargar, Ben Jeurissen
Multi-contrast multi-shot EPI for accelerated diffusion MRI. EMBC 2021: 3869-3872 - [i1]Emanoel R. Sabidussi, Stefan Klein, Matthan W. A. Caan, Shabab Bazrafkan, Arnold J. den Dekker, Jan Sijbers, Wiro J. Niessen, Dirk H. J. Poot:
Recurrent Inference Machines as inverse problem solvers for MR relaxometry. CoRR abs/2106.07379 (2021) - 2020
- [j19]Quinten Beirinckx
Joint Maximum Likelihood Estimation of Motion and T1 Parameters from Magnetic Resonance Images in a Super-resolution Framework: a Simulation Study. Fundam. Informaticae 172(2): 105-128 (2020)
2010 – 2019
- 2018
- [j18]Gabriel Ramos-Llordén
NOVIFAST: A Fast Algorithm for Accurate and Precise VFA MRI T1 Mapping. IEEE Trans. Medical Imaging 37(11): 2414-2427 (2018) - 2017
- [j17]P. V. Sudeep
A nonlocal maximum likelihood estimation method for enhancing magnetic resonance phase maps. Signal Image Video Process. 11(5): 913-920 (2017) - [j16]Gabriel Ramos-Llordén
A Unified Maximum Likelihood Framework for Simultaneous Motion and T1 Estimation in Quantitative MR T1 Mapping. IEEE Trans. Medical Imaging 36(2): 433-446 (2017) - [j15]Gabriel Ramos-Llordén
Partial Discreteness: A Novel Prior for Magnetic Resonance Image Reconstruction. IEEE Trans. Medical Imaging 36(5): 1041-1053 (2017) - 2016
- [c20]Piet Bladt, Gwendolyn Van Steenkiste, Gabriel Ramos-Llordén, Arnold J. den Dekker
Multi-voxel algorithm for quantitative bi-exponential MRI T1 estimation. Medical Imaging: Image Processing 2016: 978402 - 2015
- [c19]Gabriel Ramos-Llordén, Hilde Segers, Willem Jan Palenstijn, Arnold J. den Dekker
Partially discrete magnetic resonance tomography. ICIP 2015: 1653-1657 - [c18]Gabriel Ramos-Llordén, Arnold J. den Dekker
Simultaneous motion correction and T1 estimation in quantitative T1 mapping: An ML restoration approach. ICIP 2015: 3160-3164 - [c17]Gwendolyn Van Steenkiste, Dirk H. J. Poot, Ben Jeurissen, Arnold J. den Dekker
High resolution T1 estimation from multiple low resolution magnetic resonance images. ISBI 2015: 1036-1039 - 2014
- [j14]Pawel Miroslaw Stano, Arnold J. den Dekker
Convex saturated particle filter. Autom. 50(10): 2494-2503 (2014) - [j13]Tom Roelandts, Kees Joost Batenburg, Arnold J. den Dekker
The reconstructed residual error: A novel segmentation evaluation measure for reconstructed images in tomography. Comput. Vis. Image Underst. 126: 28-37 (2014) - [j12]Arturo Tejada
A comparison between minimum variance control and other online compensation methods for specimen drift in transmission electron microscopy. Multidimens. Syst. Signal Process. 25(2): 247-271 (2014) - [j11]Jeny Rajan
A new non-local maximum likelihood estimation method for Rician noise reduction in magnetic resonance images using the Kolmogorov-Smirnov test. Signal Process. 103: 16-23 (2014) - 2013
- [j10]Pawel Stano, Zsófia Lendek
Parametric Bayesian Filters for Nonlinear Stochastic Dynamical Systems: A Survey. IEEE Trans. Cybern. 43(6): 1607-1624 (2013) - [j9]Jan S. Erkelens, Arturo Tejada
Identification of Time Series Models From Segments - Application to Scanning Transmission Electron Microscopy Images. IEEE Trans. Instrum. Meas. 62(12): 3231-3242 (2013) - [c16]Jeny Rajan
A New Nonlocal Maximum Likelihood Estimation Method for Denoising Magnetic Resonance Images. PReMI 2013: 451-458 - 2012
- [j8]Arturo Tejada
Defocus Polar rOse Estimation Method (POEM): A Fast Defocus Estimation Method for STEM. IEEE Trans. Instrum. Meas. 61(10): 2723-2730 (2012) - 2011
- [c15]Arturo Tejada
Towards an adaptive minimum variance control scheme for specimen drift compensation in transmission electron microscopes. nDS 2011: 1-6 - 2010
- [j7]Dirk H. J. Poot, Arnold J. den Dekker
Optimal Experimental Design for Diffusion Kurtosis Imaging. IEEE Trans. Medical Imaging 29(3): 819-829 (2010) - [j6]Matthan W. A. Caan
Estimation of Diffusion Properties in Crossing Fiber Bundles. IEEE Trans. Medical Imaging 29(8): 1504-1515 (2010) - [c14]Matthan W. A. Caan
Adaptive Noise Filtering for Accurate and Precise Diffusion Estimation in Fiber Crossings. MICCAI (1) 2010: 167-174
2000 – 2009
- 2009
- [j5]Arnold J. den Dekker
Likelihood-Based Hypothesis Tests for Brain Activation Detection From MRI Data Disturbed by Colored Noise: A Simulation Study. IEEE Trans. Medical Imaging 28(2): 287-296 (2009) - [c13]Arturo Tejada
Towards automatic control of scanning transmission electron microscopes. CCA/ISIC 2009: 788-793 - [c12]Xavier Bombois, Arnold J. den Dekker
Effect of model structure and signal-to-noise ratio on finite-time uncertainty bounding in prediction error identification. CDC 2009: 494-499 - [c11]Arturo Tejada
Towards STEM control: Modeling framework and development of a sensor for defocus control. CDC 2009: 8310-8315 - 2008
- [c10]Dirk H. J. Poot, Jan Sijbers, Arnold J. den Dekker:
An exploration of spatial similarities in temporal noise spectra in fMRI measurements. Medical Imaging: Image Processing 2008: 69142F - 2006
- [c9]Jan Sijbers
Robust estimation of the noise variance from background MR data. Medical Imaging: Image Processing 2006: 61446B - 2005
- [j4]Jan Sijbers
Generalized likelihood ratio tests for complex fMRI data: a Simulation study. IEEE Trans. Medical Imaging 24(5): 604-611 (2005) - [c8]Jan Sijbers
A Likelihood Ratio Test for Functional MRI Data Analysis to Account for Colored Noise. ACIVS 2005: 538-546 - 2004
- [c7]Arnold J. den Dekker, Jan Sijbers:
Detection of brain activation from magnitude fMRI data using a Generalized Likelihood Ratio Test. EUSIPCO 2004: 233-236 - 2003
- [c6]Theo G. S. M. Rijks, Joost T. M. van Beek, M. J. E. Ulenaers, Jeroen De Coster, Robert Puers
Passive integration and RF MEMS: a toolkit for adaptive LC circuits. ESSCIRC 2003: 269-272 - 2002
- [j3]Sandra Van Aert
High-resolution electron microscopy: from imaging toward measuring. IEEE Trans. Instrum. Meas. 51(4): 611-615 (2002)
1990 – 1999
- 1999
- [j2]Jan Sijbers, Arnold J. den Dekker, Erik Raman, Dirk Van Dyck:
Parameter estimation from magnitude MR images. Int. J. Imaging Syst. Technol. 10(2): 109-114 (1999) - [c5]Jan Sijbers, Arnold Jan den Dekker, Marleen Verhoye
Adaptive anisotropic noise filtering for magnitude MR data. Medical Imaging: Image Processing 1999 - 1998
- [j1]Jan Sijbers, Arnold J. den Dekker
Maximum Likelihood Estimation of Rician Distribution Parameters. IEEE Trans. Medical Imaging 17(3): 357-361 (1998) - [c4]Arnold Jan den Dekker
Maximum-likelihood signal estimation in phase contrast magnitude MR images. Medical Imaging: Image Processing 1998 - [c3]Jan Sijbers
Optimal estimation of T2 maps from magnitude MR images. Medical Imaging: Image Processing 1998 - 1996
- [c2]Arnold J. den Dekker:
Coherent model-based optical resolution and SNR. EUSIPCO 1996: 1-4 - [c1]Arnold J. den Dekker:
Model-based optical two-point resolution. ICASSP 1996: 2395-2398
Coauthor Index
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last updated on 2024-10-07 22:11 CEST by the dblp team
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