@article {2423,
	title = {Framework for optimal design of a multi-source rectangular X-ray cargo scanning system},
	journal = {Radiation Physics and Chemistry},
	volume = {239},
	year = {2026},
	pages = {113332},
	doi = {https://doi.org/10.1016/j.radphyschem.2025.113332 },
	author = {Caroline Bossuyt and Arnold Jan den Dekker and Domenico Iuso and Jan De Beenhouwer and Jan Sijbers}
}
@article {2412,
	title = {DAMMER: Direct Adaptive Multi-resolution MEsh Reconstruction from X-ray measurements},
	journal = {IEEE Transactions on Computational Imaging },
	volume = {11},
	year = {2025},
	pages = {926 - 941},
	doi = {10.1109/TCI.2025.3587408},
	author = {Jannes Merckx and Arnold Jan den Dekker and Jan Sijbers and Jan De Beenhouwer}
}
@article {2406,
	title = {Harmonization of diffusion MRI on healthy subjects using NeuroCombat and LongCombat: a B-Q MINDED Brain Intra-and Inter-Scanner Study},
	journal = {Frontiers in Neuroscience},
	volume = {19},
	year = {2025},
	doi = {10.3389/fnins.2025.1591169},
	author = {Maira Siqueira Pinto and Vincenzo Anania and Roberto Paolella and C{\'e}line Smekens and Thibo Billiet and Thomas Janssens and Arnold Jan den Dekker and Jan Sijbers and Pieter-Jan Guns and Pieter Van Dyck}
}
@inproceedings {2374,
	title = {Deep image prior for sparse-view reconstruction in static, rectangular multi-source x-ray CT systems for cargo scanning},
	booktitle = {SPIE Developments in X-Ray Tomography XV},
	volume = {13152},
	year = {2024},
	doi = {https://doi.org/10.1117/12.3027668},
	author = {Caroline Bossuyt and Arnold Jan den Dekker and Domenico Iuso and T. Le Hoang and Julie Escoda and Marius Costin and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2375,
	title = {Fast and efficient tetrahedral volume mesh reconstruction with CAD-ASTRA},
	booktitle = {SPIE Developments in X-Ray Tomography XV},
	volume = {13152},
	year = {2024},
	address = {San Diego, USA},
	doi = {https://doi.org/10.1117/12.3027610},
	author = {Jannes Merckx and Arnold Jan den Dekker and Jan De Beenhouwer and Jan Sijbers}
}
@article {2381,
	title = {Longitudinal Magnetic Resonance Imaging Method},
	number = {WO2024100240A1},
	year = {2024},
	chapter = {WO2024100240A1},
	author = {Jan Sijbers and Arnold Jan den Dekker}
}
@article {2325,
	title = {Model-based super-resolution reconstruction for pseudo-continuous Arterial Spin Labeling},
	journal = {NeuroImage},
	volume = {286},
	year = {2024},
	month = {01/2024},
	pages = {120506},
	abstract = {Arterial spin labeling (ASL) is a promising, non-invasive perfusion magnetic resonance imaging technique for quantifying cerebral blood flow (CBF). Unfortunately, ASL suffers from an inherently low signal-to-noise ratio (SNR) and spatial resolution, undermining its potential. Increasing spatial resolution without significantly sacrificing SNR or scan time represents a critical challenge towards routine clinical use. In this work, we propose a model-based super-resolution reconstruction (SRR) method with joint motion estimation that breaks the traditional SNR/resolution/scan-time trade-off. From a set of differently oriented 2D multi-slice pseudo-continuous ASL images with a low through-plane resolution, 3D-isotropic, high resolution, quantitative CBF maps are estimated using a Bayesian approach. Experiments on both synthetic whole brain phantom data, and on in vivo brain data, show that the proposed SRR Bayesian estimation framework outperforms state-of-the-art ASL quantification.},
	keywords = {Arterial spin labeling, CBF mapping, Model-based reconstruction, Perfusion, Quantitative MRI, super-resolution},
	issn = {1053-8119},
	doi = {10.1016/j.neuroimage.2024.120506},
	author = {Quinten Beirinckx and Piet Bladt and Merlijn C E van der Plas and M.J.P van Osch and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@article {2256,
	title = {ADEPT: Accurate Diffusion EPI with multi-contrast shoTs},
	journal = {Magnetic Resonance in Medicine},
	volume = {89},
	year = {2023},
	pages = {396-410},
	doi = {10.1002/mrm.29398},
	author = {Banafshe Shafieizargar and Ben Jeurissen and Dirk H J Poot and Stefan Klein and Johan Van Audekerke and Verhoye, Marleen and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {2283,
	title = {DELTA-MRI: Direct deformation Estimation from LongiTudinally Acquired k-space data},
	booktitle = {IEEE International Symposium on Biomedical Imaging},
	year = {2023},
	pages = {1-4},
	doi = {10.1109/ISBI53787.2023.10230697},
	author = {Jens Renders and Banafshe Shafieizargar and Marleen Verhoye and Jan De Beenhouwer and Arnold Jan den Dekker and Jan Sijbers}
}
@article {2274,
	title = {Optimal experimental design and estimation for q-space trajectory imaging},
	journal = {Human Brain Mapping},
	volume = {44},
	year = {2023},
	pages = {1793-1809},
	abstract = {Tensor-valued diffusion encoding facilitates data analysis by q-space trajectory imaging. By modeling the diffusion signal of heterogeneous tissues with a diffusion tensor distribution (DTD) and modulating the encoding tensor shape, this novel approach allows disentangling variations in diffusivity from microscopic anisotropy, orientation dispersion, and mixtures of multiple isotropic diffusivities. To facilitate the estimation of the DTD parameters, a parsimonious acquisition scheme coupled with an accurate and precise estimation of the DTD is needed. In this work, we create two precision-optimized acquisition schemes: one that maximizes the precision of the raw DTD parameters, and another that maximizes the precision of the scalar measures derived from the DTD. The improved precision of these schemes compared to a naive sampling scheme is demonstrated in both simulations and real data. Furthermore, we show that the weighted linear least squares (WLLS) estimator that uses the squared reciprocal of the noisy signal as weights can be biased, whereas the iteratively WLLS estimator with the squared reciprocal of the predicted signal as weights outperforms the conventional unweighted linear LS and nonlinear LS estimators in terms of accuracy and precision. Finally, we show that the use of appropriate constraints can considerably increase the precision of the estimator with only a limited decrease in accuracy.},
	doi = {10.1002/hbm.26175},
	author = {Jan Morez and Szczepankiewicz, Filip and Arnold Jan den Dekker and Floris Vanhevel and Jan Sijbers and Ben Jeurissen}
}
@inproceedings {2288,
	title = {Super-resolution reconstruction of multi-slice T2-w FLAIR MRI improves Multiple Sclerosis lesion segmentation},
	booktitle = {45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
	year = {2023},
	address = {Sydney, Australia},
	abstract = {Due to acquisition time constraints, T2-w FLAIR MRI of Multiple Sclerosis (MS) patients is often acquired with multi-slice 2D protocols with a low through-plane resolution rather than with high-resolution 3D protocols. Automated lesion segmentation on such low-resolution (LR) images, however, performs poorly and leads to inaccurate lesion volume estimates. Super-resolution reconstruction (SRR) methods can then be used to obtain a high-resolution (HR) image from multiple LR images to serve as input for lesion segmentation. In this work, we evaluate the effect on MS lesion segmentation of three SRR approaches: one based on interpolation, a state-of-the-art self-supervised CNN-based strategy, and a recently proposed model-based SRR method. These SRR strategies were applied to LR acquisitions simulated from 3D T2-w FLAIR MRI of MS patients. Each SRR method was evaluated in terms of image reconstruction quality and posterior lesion segmentation performance. When compared to segmentation on LR images, the three considered SRR strategies demonstrate improved lesion segmentation. Furthermore, in some scenarios, SRR achieves a similar segmentation performance compared to segmentation of HR images.},
	author = {Diana Giraldo and Quinten Beirinckx and Arnold Jan den Dekker and Ben Jeurissen and Jan Sijbers}
}
@article {2289,
	title = {Systematic review of reconstruction techniques for accelerated quantitative MRI},
	journal = {Magnetic Resonance in Medicine},
	volume = {90},
	year = {2023},
	pages = {1172-1208},
	doi = {https://doi.org/10.1002/mrm.29721},
	author = {Banafshe Shafieizargar and Riwaj Byanju and Jan Sijbers and Stefan Klein and Arnold Jan den Dekker and Dirk H J Poot}
}
@article {2286,
	title = {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 = {Journal of Neurotrauma},
	volume = {40},
	year = {2023},
	pages = {1317-1338},
	doi = {https://doi.org/10.1089/neu.2022.0365},
	author = {Maira Siqueira Pinto and Stefan Winzeck and Evgenios N. Kornaropoulos and Sophie Richter and Roberto Paolella and Marta M. Correia and Ben Glocker and Guy Williams and Anne Vik and Jussi Posti and Asta Kristine H{\r a}berg and Jonas Stenberg and Pieter-Jan Guns and Arnold Jan den Dekker and David K. Menon and Jan Sijbers and Pieter Van Dyck and Virginia F. J. Newcombe}
}
@article {2235,
	title = {Improved diffusion parameter estimation by incorporating T2 relaxation properties into the DKI-FWE model},
	journal = {NeuroImage},
	volume = {256},
	year = {2022},
	pages = {119219},
	doi = {https://doi.org/10.1016/j.neuroimage.2022.119219},
	author = {Vincenzo Anania and Quinten Collier and Jelle Veraart and Annemieke Eline Buikema and Floris Vanhevel and Thibo Billiet and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@article {2247,
	title = {Model-based super-resolution reconstruction with joint motion estimation for improved quantitative MRI parameter mapping},
	journal = {Computerized Medical Imaging and Graphics},
	volume = {100},
	year = {2022},
	month = {09/2022},
	pages = {1-16},
	chapter = {102071},
	abstract = {Quantitative Magnetic Resonance (MR) imaging provides reproducible measurements of biophysical parameters, and has become an essential tool in clinical MR studies. Unfortunately, 3D isotropic high resolution (HR) parameter mapping is hardly feasible in clinical practice due to prohibitively long acquisition times. Moreover, accurate and precise estimation of quantitative parameters is complicated by inevitable subject motion, the risk of which increases with scanning time. In this paper, we present a model-based super-resolution reconstruction (SRR) method that jointly estimates HR quantitative parameter maps and inter-image motion parameters from a set of 2D multi-slice contrast-weighted images with a low through-plane resolution. The method uses a Bayesian approach, which allows to optimally exploit prior knowledge of the tissue and noise statistics. To demonstrate its potential, the proposed SRR method is evaluated for a T1 and T2 quantitative mapping protocol. Furthermore, the method{\textquoteright}s performance in terms of precision, accuracy, and spatial resolution is evaluated using simulated as well as real brain imaging experiments. Results show that our proposed fully flexible, quantitative SRR framework with integrated motion estimation outperforms state-of-the-art SRR methods for quantitative MRI.},
	issn = {0895-6111},
	doi = {https://doi.org/10.1016/j.compmedimag.2022.102071},
	author = {Quinten Beirinckx and Ben Jeurissen and Michele Nicastro and Dirk H J Poot and Marleen Verhoye and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {2233,
	title = {Optimal acquisition settings for simultaneous diffusion kurtosis, free water fraction and T2 estimation},
	year = {2022},
	author = {Vincenzo Anania and Ben Jeurissen and Jan Morez and Annemieke Eline Buikema and Thibo Billiet and Jan Sijbers and Arnold Jan den Dekker}
}
@article {2275,
	title = {To shift or to rotate? Comparison of acquisition strategies for multi-slice super-resolution magnetic resonance imaging},
	journal = {Frontiers in Neuroscience},
	year = {2022},
	pages = {1-18},
	doi = {https://doi.org/10.3389/fnins.2022.1044510},
	author = {Michele Nicastro and Ben Jeurissen and Quinten Beirinckx and C{\'e}line Smekens and Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker}
}
@conference {2184,
	title = {Accelerated multi-shot diffusion weighted imaging with joint estimation of diffusion and phase parameters},
	volume = {34},
	year = {2021},
	pages = {S57-S58},
	doi = {10.1007/s10334-021-00947-8},
	author = {Banafshe Shafieizargar and Ben Jeurissen and Dirk H J Poot and Johan Van Audekerke and Marleen Verhoye and Arnold Jan den Dekker and Jan Sijbers}
}
@inbook {2154,
	title = {Chapter Eight - General conclusions and future perspectives},
	booktitle = {Advances in Imaging and Electron Physics},
	volume = {217},
	year = {2021},
	publisher = {Science Direct Elsevier},
	organization = {Science Direct Elsevier},
	chapter = {Chapter Eight - General conclusions and future perspectives},
	doi = {10.1016/bs.aiep.2021.01.008},
	author = {Annick De Backer and Jarmo Fatermans and Arnold Jan den Dekker and Sandra Van Aert}
}
@inbook {2152,
	title = {Chapter Five - Optimal experiment design for nanoparticle atom counting from ADF STEM images},
	booktitle = {Advances in Imaging and Electron Physics},
	volume = {217},
	year = {2021},
	publisher = {Science Direct Elsevier},
	organization = {Science Direct Elsevier},
	chapter = {Chapter Five - Optimal experiment design for nanoparticle atom counting from ADF STEM images},
	doi = {10.1016/bs.aiep.2021.01.005},
	author = {Annick De Backer and Jarmo Fatermans and Arnold Jan den Dekker and Sandra Van Aert}
}
@inbook {2150,
	title = {Chapter Four - Atom counting},
	booktitle = {Advances in Imaging and Electron Physics,},
	volume = {217},
	year = {2021},
	publisher = {Science Direct Elsevier},
	organization = {Science Direct Elsevier},
	chapter = {Chapter Four - Atom counting},
	doi = {10.1016/bs.aiep.2021.01.004},
	author = {Annick De Backer and Jarmo Fatermans and Arnold Jan den Dekker and Sandra Van Aert}
}
@inbook {2149,
	title = {Chapter One - Introduction},
	booktitle = {Advances in Imaging and Electron Physics},
	volume = {217},
	year = {2021},
	publisher = {Science Direct Elsevier},
	organization = {Science Direct Elsevier},
	chapter = {One-introduction},
	doi = {10.1016/bs.aiep.2021.01.001},
	author = {Annick De Backer and Jarmo Fatermans and Arnold Jan den Dekker and Sandra Van Aert}
}
@inbook {2153,
	title = {Chapter Seven - Image-quality evaluation and model selection with maximum a posteriori probability},
	booktitle = {Advances in Imaging and Electron Physics},
	volume = {217},
	year = {2021},
	publisher = {Science Direct Elsevier},
	organization = {Science Direct Elsevier},
	chapter = {Chapter Seven - Image-quality evaluation and model selection with maximum a posteriori probability},
	doi = {10.1016/bs.aiep.2021.01.007},
	author = {Jarmo Fatermans and Annick De Backer and Arnold Jan den Dekker and Sandra Van Aert}
}
@inbook {2156,
	title = {Chapter Six - Atom column detection},
	booktitle = {Advances in Imaging and Electron Physics},
	volume = {217},
	year = {2021},
	publisher = {Science Direct Elsevier},
	organization = {Science Direct Elsevier},
	chapter = {Chapter Six - Atom column detection},
	doi = {10.1016/bs.aiep.2021.01.006},
	author = {Jarmo Fatermans and Annick De Backer and Arnold Jan den Dekker and Sandra Van Aert}
}
@inbook {2155,
	title = {Chapter Three - Efficient fitting algorithm},
	booktitle = {Advances in Imaging and Electron Physics},
	volume = {217},
	year = {2021},
	publisher = {Science Direct Elsevier},
	organization = {Science Direct Elsevier},
	chapter = {Chapter Three - Efficient fitting algorithm},
	doi = {10.1016/bs.aiep.2021.01.003},
	author = {Annick De Backer and Jarmo Fatermans and Arnold Jan den Dekker and Sandra Van Aert}
}
@inbook {2151,
	title = {Chapter Two - Statistical parameter estimation theory: principles and simulation studies},
	booktitle = {Advances in Imaging and Electron Physics},
	volume = {217},
	year = {2021},
	publisher = {Science Direct Elsevier},
	organization = {Science Direct Elsevier},
	chapter = {Chapter Two - Statistical parameter estimation theory: principles and simulation studies},
	doi = {10.1016/bs.aiep.2021.01.002},
	author = {Annick De Backer and Jarmo Fatermans and Arnold Jan den Dekker and Sandra Van Aert}
}
@inproceedings {2159,
	title = {Comparison of MR acquisition strategies for super-resolution reconstruction using the Bayesian Mean Squared Error},
	booktitle = { International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine},
	year = {2021},
	abstract = {In multi-slice super-resolution reconstruction (MS-SRR), a high resolution image, referred to as SRR image, is estimated from a series of multi-slice images with a low through-plane resolution. This work proposes a framework based on the Bayesian mean squared error of the Maximum A Posteriori estimator of a SRR image to compare the accuracy and precision of two commonly adopted MR acquisition strategies in MS-SRR. The first strategy consists in acquiring a set of multi-slice
images, where each image is shifted in the through-plane direction by a different, sub-pixel distance. The latter consists in acquiring a set of multi-slice images, where each image is rotated around the frequency or phase-encoding axis by a different rotation angle. Results show that MS-SRR based on rotated multi-slice images outperforms MS-SRR based on shifted multi-slice images in terms of accuracy, precision and mean squared error of the reconstructed image.},
	author = {Michele Nicastro and Ben Jeurissen and Quinten Beirinckx and C{\'e}line Smekens and Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker}
}
@conference {2205,
	title = {High-resolution T2* mapping of the knee based on UTE Spiral VIBE MRI},
	volume = {34},
	year = {2021},
	pages = {S53-S54},
	author = {C{\'e}line Smekens and Quinten Beirinckx and Floris Vanhevel and Pieter Van Dyck and Arnold Jan den Dekker and Jan Sijbers and Thomas Janssens and Ben Jeurissen}
}
@inproceedings {2189,
	title = {Multi-contrast multi-shot EPI for accelerated diffusion MRI},
	booktitle = {43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
	year = {2021},
	pages = {3869-3872},
	doi = {10.1109/EMBC46164.2021.9630069},
	author = {Banafshe Shafieizargar and Ben Jeurissen and Dirk H J Poot and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {2182,
	title = {Optimal experimental design for the T2-weighted diffusion kurtosis imaging free water elimination model},
	volume = {34},
	year = {2021},
	pages = {S54-S55},
	doi = {https://doi.org/10.1007/s10334-021-00947-8},
	author = {Vincenzo Anania and Ben Jeurissen and Jan Morez and Annemieke Eline Buikema and Thibo Billiet and Jan Sijbers and Arnold Jan den Dekker}
}
@conference {2174,
	title = {Outcome prediction in Mild Traumatic Brain Injury patients using conventional and diffusion MRI via Support Vector Machine: A CENTER-TBI study},
	year = {2021},
	author = {Maira Siqueira Pinto and Stefan Winzeck and Marta M. Correia and Evgenios N. Kornaropoulos and David K. Menon and Ben Glocker and Arnold Jan den Dekker and Jan Sijbers and Pieter-Jan Guns and Pieter Van Dyck and Virginia F. J. Newcombe}
}
@conference {2185,
	title = {Outcome prediction of mild traumatic brain injury using support vector machine based on longitudinal MRdiffusion imaging from CENTER-TBI},
	volume = {34},
	year = {2021},
	pages = {S54},
	doi = {10.1007/s10334-021-00947-8},
	author = {Maira Siqueira Pinto and Stefan Winzeck and S. Richter and Marta M. Correia and Evgenios N. Kornaropoulos and David K. Menon and Ben Glocker and Pieter-Jan Guns and Arnold Jan den Dekker and Jan Sijbers and Virginia F. J. Newcombe and Pieter Van Dyck}
}
@inproceedings {2169,
	title = {Recurrent Inference Machines as Inverse Problem Solvers for MR Relaxometry},
	booktitle = { MIDL 2021 - Medical Imaging with Deep Learning},
	year = {2021},
	author = {Emanoel Ribeiro Sabidussi and Matthan Caan and Shabab Bazrafkan and Arnold Jan den Dekker and Jan Sijbers and Wiro J Niessen and Dirk H J Poot}
}
@article {2190,
	title = {Recurrent Inference Machines as inverse problem solvers for MR relaxometry},
	journal = {Medical Image Analysis},
	volume = {74},
	year = {2021},
	pages = {1-11},
	doi = {https://doi.org/10.1016/j.media.2021.102220},
	author = {Emanoel Ribeiro Sabidussi and Stefan Klein and Matthan Caan and Shabab Bazrafkan and Arnold Jan den Dekker and Jan Sijbers and Wiro J Niessen and Dirk H J Poot}
}
@conference {2181,
	title = {Rotated or shifted sets of multi-slice MR images for super-resolution reconstruction? A Bayesian answer},
	volume = {34},
	year = {2021},
	pages = {S56-S57},
	doi = {10.1007/s10334-021-00947-8},
	author = {Michele Nicastro and Ben Jeurissen and Quinten Beirinckx and C{\'e}line Smekens and Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker}
}
@conference {2160,
	title = {Super-resolution T2* mapping of the knee using UTE Spiral VIBE MRI},
	year = {2021},
	pages = {3920},
	abstract = {T2* mapping using ultrashort echo time (UTE) MRI allows for quantitative evaluation of collagen-rich knee structures with short mean transverse relaxation times. However, acquisitions with low through-plane resolution are commonly used to obtain T2* maps within reasonable scan times, affecting the accuracy of the estimations because of partial volume effects. In this work, model-based super-resolution reconstructions based on UTE Spiral VIBE MRI were performed to obtain high-resolution T2* maps of knee structures within a reasonable scan time. The obtained T2* maps are comparable to maps generated with direct 3D UTE Spiral VIBE acquisitions while requiring approximately 25\% less scan time.},
	author = {C{\'e}line Smekens and Quinten Beirinckx and Floris Vanhevel and Pieter Van Dyck and Arnold Jan den Dekker and Jan Sijbers and Thomas Janssens and Ben Jeurissen}
}
@conference {2081,
	title = {3D Atomic Scale Quantification of Nanostructures and their Dynamics Using Model-based STEM},
	year = {2020},
	author = {Sandra Van Aert and Annick De Backer and De wael, A and Jarmo Fatermans and Friedrich, T and Ivan Lobato and O{\textquoteright}Leary, C M. and Varambhia, A and Thomas Altantzis and Jones, L and Arnold Jan den Dekker and Peter D Nellist and Sara Bals}
}
@mastersthesis {2069,
	title = {Accurate and precise perfusion parameter estimation in pseudo-continuous arterial spin labeling MRI},
	volume = {PhD in Sciences/Physics},
	year = {2020},
	type = {PhD thesis},
	author = {Piet Bladt}
}
@article {2063,
	title = {Atom column detection from simultaneously acquired ABF and ADF STEM images},
	journal = {Ultramicroscopy},
	volume = {219},
	year = {2020},
	pages = {113046},
	doi = {https://doi.org/10.1016/j.ultramic.2020.113046},
	author = {Fatermans, J. and Arnold Jan den Dekker and M{\"u}ller-Caspary, K. and N Gauquelin and Jo Verbeeck and Sandra Van Aert}
}
@conference {2091,
	title = {Bayesian model selection for atom column detection from ABF-ADF STEM images},
	year = {2020},
	author = {Fatermans, J. and Arnold Jan den Dekker and N Gauquelin and Jo Verbeeck and Sandra Van Aert}
}
@article {1956,
	title = {The costs and benefits of estimating T1 of tissue alongside cerebral blood flow and arterial transit time in pseudo-continuous arterial spin labeling},
	journal = {NMR in Biomedicine},
	volume = {33},
	year = {2020},
	pages = {1-17},
	doi = {https://doi.org/10.1002/nbm.4182},
	author = {Piet Bladt and Arnold Jan den Dekker and Clement, Patricia and Eric Achten and Jan Sijbers}
}
@article {2037,
	title = {Harmonisation of Brain Diffusion MRI: Concepts and Methods},
	journal = {Frontiers in Neuroscience },
	volume = {14},
	year = {2020},
	month = {03/2020},
	pages = {1-17},
	doi = {https://doi.org/10.3389/fnins.2020.00396},
	author = {Maira Siqueira Pinto and Roberto Paolella and Thibo Billiet and Pieter Van Dyck and Pieter-Jan Guns and Ben Jeurissen and Annemie Ribbens and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {2021,
	title = {Improved voxel-wise quantification of diffusion and kurtosis metrics in the presence of noise and intensity outliers},
	year = {2020},
	url = {https://www.ismrm-benelux.org/wp-content/uploads/2020/01/Proceedings2020.pdf},
	author = {Vincenzo Anania and Thibo Billiet and Ben Jeurissen and Annemie Ribbens and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {2025,
	title = {Joint estimation of phase and diffusion tensor parameters from multi-shot k-q-space data: a proof of concept},
	volume = {12},
	year = {2020},
	abstract = {To address the issue of phase induced artifacts in multi-shot diffusion weighted imaging, we propose a model-based framework which enables the joint estimation of diffusion and phase parameters
directly from the multi-shot k-q-space. In a simulation study, we show that using this framework, diffusion parameters can be estimated more accurately and precisely than with the conventional
method (image reconstruction followed by voxel-wise model fitting) that ignores phase differences.},
	keywords = {Diffusion MRI, MRI, Phase estimation},
	author = {Banafshe Shafieizargar and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {2024,
	title = {Joint estimation of phase and diffusion tensor parameters from multi-shot k-q-space data: a proof of concept},
	volume = {28},
	year = {2020},
	abstract = {To address the issue of phase induced artifacts in multi-shot diffusion weighted imaging, we propose a model-based framework which enables the joint estimation of diffusion and phase parameters
directly from the multi-shot k-q-space. In a simulation study, we show that using this framework, diffusion parameters can be estimated more accurately and precisely than with the conventional
method (image reconstruction followed by voxel-wise model fitting) that ignores phase differences.},
	keywords = {Diffusion MRI, MRI, Phase estimation},
	author = {Banafshe Shafieizargar and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1971,
	title = {Joint Maximum Likelihood estimation of motion and T1 parameters from magnetic resonance images in a super-resolution framework: a simulation study},
	journal = {Fundamenta Informaticae},
	volume = {172},
	year = {2020},
	pages = {105{\textendash}128},
	abstract = {Magnetic resonance imaging (MRI) based T1 mapping allows spatially resolved quantification of the tissue-dependent spin-lattice relaxation time constant T1, which is a potential biomarker of various neurodegenerative diseases, including Multiple Sclerosis, Alzheimer disease, and Parkinson{\textquoteright}s disease. In conventional T1 MR relaxometry, a quantitative T1 map is obtained from a series of T1-weighted MR images. Acquiring such a series, however, is time consuming. This has sparked the development of more efficient T1 mapping methods, one of which is a super-resolution reconstruction (SRR) framework in which a set of low resolution (LR) T1-weighted images is acquired and from which a high resolution (HR) T1 map is directly estimated. In this paper, the SRR T1 mapping framework is augmented with motion estimation. That is, motion between the acquisition of the LR T1-weighted images is modeled and the motion parameters are estimated simultaneously with the T1 parameters. Based on Monte Carlo simulation experiments, we show that such an integrated motion/relaxometry estimation approach yields more accurate T1 maps compared to a previously reported SRR based T1 mapping approach.},
	doi = {10.3233/FI-2020-1896},
	author = {Quinten Beirinckx and Gabriel Ramos-Llord{\'e}n and Ben Jeurissen and Dirk H J Poot and Paul M Parizel and Marleen Verhoye and Jan Sijbers and Arnold Jan den Dekker}
}
@conference {2019,
	title = {Optimal design of a T1 super-resolution reconstruction experiment: a simulation study},
	year = {2020},
	author = {Michele Nicastro and Quinten Beirinckx and Piet Bladt and Ben Jeurissen and Stefan Klein and Jan Sijbers and Dirk H J Poot and Arnold Jan den Dekker}
}
@conference {2094,
	title = {Strategies for quantifying the 3D atomic structure and the dynamics of nanomaterials using model-based STEM},
	year = {2020},
	author = {Sandra Van Aert and Annick De Backer and De wael, A and Jarmo Fatermans and Arslan Irmak, E and Friedrich, T and Ivan Lobato and Jones, L and Arnold Jan den Dekker and Peter D Nellist and Sara Bals}
}
@conference {2139,
	title = {Super-resolution reconstruction of single-PLD pseudo-continuous ASL images},
	year = {2020},
	pages = {3293},
	abstract = {Super-resolution reconstruction (SRR) allows for 3D high-resolution image reconstruction from a set of low-resolution multi-slice images with different orientations. Arterial spin labeling (ASL) is an interesting albeit complicated candidate for SRR, as it relies on subtraction. SRR-ASL can be performed on low-SNR subtracted or on low-contrast unsubtracted ASL data. Different ASL-SRR implementations were applied to single-PLD PCASL data and validated against traditional ASL-scans. Combining motion correction, super-resolution post-processing and pairwise subtraction of label-control pairs in a single framework yielded comparable CBF maps as with traditional HR-ASL. Furthermore, in certain slices, SRR-ASL appears to reconstruct the anatomical structure with higher fidelity.},
	author = {Piet Bladt and Quinten Beirinckx and Merlijn C E van der Plas and Sophie Schmid and Wouter M Teeuwisse and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers and M.J.P van Osch}
}
@conference {Bladt2020-mp,
	title = {Super-resolution reconstruction of single-PLD pseudo-continuous ASL images},
	year = {2020},
	pages = {3293},
	author = {Bladt, P and Beirinckx, Q and Van der Plas, M and Schmid, S and Teeuwisse, W and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers and van Osch, M}
}
@conference {2015,
	title = {Super-resolution strategies for single-PLD pseudo-continuous ASL},
	year = {2020},
	address = {Arnhem, The Netherlands},
	abstract = {Super-resolution reconstruction (SRR) allows for 3D high-resolution image reconstruction from a set of low-resolution multi-slice images with different orientations. Arterial spin labeling (ASL) is an interesting albeit complicated candidate for SRR, as it relies on subtraction. SRR-ASL can be performed on low-SNR subtracted or on low-contrast unsubtracted ASL data. Different ASL-SRR implementations were applied to single-PLD PCASL data and validated against traditional ASL-scans. Combining motion correction, super-resolution post-processing and pairwise subtraction of label-control pairs in a single framework yielded comparable CBF maps as with traditional HR-ASL. Furthermore, in certain slices, SRR-ASL appears to reconstruct the anatomical structure with higher fidelity.},
	author = {Quinten Beirinckx and Piet Bladt and Merlijn C E van der Plas and Sophie Schmid and Wouter M Teeuwisse and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers and M.J.P van Osch}
}
@article {2051,
	title = {Supporting measurements or more averages? How to quantify cerebral blood flow most reliably in 5 minutes by arterial spin labeling},
	journal = {Magnetic Resonance in Medicine},
	volume = {84},
	year = {2020},
	pages = {2523-2536},
	doi = {10.1002/mrm.28314},
	author = {Piet Bladt and M.J.P van Osch and Clement, Patricia and Eric Achten and Jan Sijbers and Arnold Jan den Dekker}
}
@conference {1974,
	title = {Atom column detection from STEM images using the maximum a posteriori probability rule},
	year = {2019},
	author = {J Fatermans and Arnold Jan den Dekker and O{\textquoteright}Leary, C M. and Peter D Nellist and Sandra Van Aert}
}
@conference {1975,
	title = {Atom detection from electron microscopy images},
	year = {2019},
	pages = {15},
	author = {J Fatermans and Arnold Jan den Dekker and Sandra Van Aert}
}
@conference {1993,
	title = {Beyond the consensus: is sacrificing part of the PCASL scan time for measurement of labeling efficiency and T1 of blood beneficial?},
	year = {2019},
	author = {Piet Bladt and M.J.P van Osch and Eric Achten and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1994,
	title = {Beyond the consensus: should measurement of T1 of blood and labeling efficiency be included and should a single- or multi-PLD protocol be used in a five-minute protocol for PCASL?},
	year = {2019},
	author = {Piet Bladt and M.J.P van Osch and Eric Achten and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1992,
	title = {Beyond the consensus: what to include when 5 minutes are available for perfusion imaging by PCASL?},
	year = {2019},
	author = {Piet Bladt and M.J.P van Osch and Eric Achten and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {2090,
	title = {CAD-based defect inspection with optimal view angle selection based on polychromatic X-ray projection images},
	booktitle = {9th Conference on Industrial Computed Tomography},
	year = {2019},
	pages = {1-5},
	address = {Padova, Italy},
	author = {Alice Presenti and Jan Sijbers and Arnold Jan den Dekker and Jan De Beenhouwer}
}
@conference {1984,
	title = {A deep learning approach to T1 mapping in quantitative MRI},
	volume = {32 (Suppl. 1)},
	number = {S09.05},
	year = {2019},
	publisher = {Magn Reson Mater Phy},
	abstract = {Quantitative MRI aims to measure biophysical tissue parameters through the analysis of the MR signal. Conventional parameter estimation methods, which often rely on a voxel-wise mapping, ignores spatial redundancies. In this work, a deep learning method for T1 mapping is proposed to overcome this limitation.},
	doi = {10.1007/s10334-019-00754-2},
	author = {Ribeiro Sabidussi, Emanoel and Michele Nicastro and Shabab Bazrafkan and Quinten Beirinckx and Ben Jeurissen and Jan Sijbers and Arnold Jan den Dekker and Stefan Klein and Dirk H J Poot}
}
@conference {1981,
	title = {Diffusion time dependence in the mid-time regime: a simulation study using PGSE.},
	number = {3572},
	year = {2019},
	address = {Montreal, Canada},
	abstract = {The purpose of this work is to study the effect of varying the diffusion time on the estimation of the parameters of the two-compartment diffusion tensor model in the mid-time regime. Simulation results show that the precision of the diffusion time-dependent compartmental parameter estimates increases when a variable echo time acquisition scheme is used. At low SNR, however, including diffusion time-dependence may lead to a high bias and variance compared to the more conventional non diffusion time-dependent model.},
	author = {Annelinde E. Buikema and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1921,
	title = {Effect of diffusion time dependence on parameter estimation in the clinical time frame: a simulation study using PGSE},
	year = {2019},
	address = {Leiden, the Netherlands},
	abstract = {Fieremans et al. modelled the diffusion time dependence of diffusion longitudinal and transverse to white matter tracts, which can be interpreted as an effect of structural disorder at the mesoscopic scale. To our knowledge, this diffusion time-dependent (DT-dependent) model has not yet been studied in the mid-time regime (Δ=20-180ms) with varying echo times, which would be valuable for clinical practice. In this simulation study, we use the pulsed-gradient spin-echo (PGSE) sequence5 and study the effects of varying the diffusion time on the parameter estimation in a mid-time regime.},
	author = {Annelinde E. Buikema and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1925,
	title = {The maximum a posteriori probability rule for atom column detection from HAADF STEM images},
	journal = {Ultramicroscopy},
	volume = {201},
	year = {2019},
	pages = {81-91},
	abstract = {Recently, the maximum a posteriori (MAP) probability rule has been proposed as an objective and quantitative method to detect atom columns and even single atoms from high-resolution high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) images. The method combines statistical parameter estimation and model-order selection using a Bayesian framework and has been shown to be especially useful for the analysis of the structure of beam-sensitive nanomaterials. In order to avoid beam damage, images of such materials are usually acquired using a limited incoming electron dose resulting in a low contrast-to-noise ratio (CNR) which makes visual inspection unreliable. This creates a need for an objective and quantitative approach. The present paper describes the methodology of the MAP probability rule, gives its step-by-step derivation and discusses its algorithmic implementation for atom column detection. In addition, simulation results are presented showing that the performance of the MAP probability rule to detect the correct number of atomic columns from HAADF STEM images is superior to that of other model-order selection criteria, including the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Moreover, the MAP probability rule is used as a tool to evaluate the relation between STEM image quality measures and atom detectability resulting in the introduction of the so-called integrated CNR (ICNR) as a new image quality measure that better correlates with atom detectability than conventional measures such as signal-to-noise ratio (SNR) and CNR.},
	keywords = {Atom detectability, Atom detection, Model selection, Scanning transmission electron microscopy (STEM)},
	issn = {0304-3991},
	doi = {https://doi.org/10.1016/j.ultramic.2019.02.003},
	url = {http://www.sciencedirect.com/science/article/pii/S0304399118304236},
	author = {J Fatermans and Sandra Van Aert and Arnold Jan den Dekker}
}
@conference {1982,
	title = {Optimal design of a blended diffusion/relaxometry experiment},
	year = {2019},
	address = {Rotterdam, the Netherlands},
	author = {Annelinde E. Buikema and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1959,
	title = {Quantifying 3D atomic structures of nanomaterials and their dynamics using model-based scanning transmission electron microscopy},
	year = {2019},
	author = {Sandra Van Aert and De wael, A and J Fatermans and Ivan Lobato and Annick De Backer and Jones, L and Arnold Jan den Dekker and Peter D Nellist}
}
@conference {2020,
	title = {Robust outlier detection for diffusion kurtosis MRI based on IRLLS},
	volume = {32},
	number = {1},
	year = {2019},
	url = {https://link.springer.com/article/10.1007/s10334-019-00756-0},
	author = {Vincenzo Anania and Thibo Billiet and Ben Jeurissen and Jan Sijbers and Arnold Jan den Dekker}
}
@conference {1958,
	title = {Strategies for quantifying 3D atomic structures of nanomaterials and their dynamics using dose-efficient ADF STEM},
	year = {2019},
	author = {Sandra Van Aert and De wael, A and J Fatermans and Ivan Lobato and Annick De Backer and Jones, L and Arnold Jan den Dekker and Peter D Nellist}
}
@conference {1983,
	title = {Super-resolution T1 mapping with integrated motion compensation in a joint maximum likelihood framework},
	volume = {32 (Suppl. 1)},
	number = {S14.05},
	year = {2019},
	publisher = {Magn Reson Mater Phy},
	abstract = {To date, 3D high resolution (HR) quantitative T1 mapping is not feasible in clinical practice due to prohibitively long acquisition times. Recent work has shown that super-resolution reconstruction (SRR), in which a 3D HR T1 map is directly estimated from a set of low through-plane resolution (LR) multi-slice (ms) T1-weighted (T1w) images with different slice orientations, can improve the trade-off between SNR, spatial resolution, and acquisition time. In that work, however, inter-image motion compensation for SRR is performed in a preprocessing step in which the transformation parameters of each LR image are updated after image registration. As a result, potential registration errors might propagate in the T1 estimation as no feedback mechanism is in place. Moreover, due to missing subvoxel accuracy no HR information is readily available during preprocessing. In the current work, we explore the potential of an improved SRR T1 mapping method that aims at more accurate T1 maps by combining T1 and motion estimation in a joint Maximum Likelihood estimation (jMLE) framework.
},
	doi = {10.1007/s10334-019-00754-2},
	author = {Quinten Beirinckx and Ben Jeurissen and Marleen Verhoye and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {2022,
	title = {Voxelwise harmonisation of FA on a cohort of 605 healthy subjects using ComBat: an exploratory study},
	year = {2019},
	author = {Maira Siqueira Pinto and Roberto Paolella and Thibo Billiet and Pieter Van Dyck and Pieter-Jan Guns and Ben Jeurissen and Annemie Ribbens and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1995,
	title = {Absolute CBF quantification in multi-time point ASL: the T1 issue},
	year = {2018},
	author = {Piet Bladt and Arnold Jan den Dekker and Clement, Patricia and Eric Achten and Jan Sijbers}
}
@conference {1827,
	title = {Accurate and precise MRI relaxometry: the often disregarded but critical role of statistical parameter estimation},
	year = {2018},
	pages = {5664},
	address = {Paris, France},
	author = {Gabriel Ramos-Llord{\'e}n and Quinten Beirinckx and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1890,
	title = {Bayesian analysis of noisy scanning transmission electron microscopy images for single atom detection},
	year = {2018},
	pages = {95},
	author = {J Fatermans and Arnold Jan den Dekker and M{\"u}ller-Caspary, K and Ivan Lobato and Sandra Van Aert}
}
@article {1820,
	title = {Diffusion kurtosis imaging with free water elimination: a Bayesian estimation approach},
	journal = {Magnetic Resonance in Medicine},
	volume = {80},
	year = {2018},
	pages = {802-813},
	doi = {10.1002/mrm.27075},
	author = {Quinten Collier and Jelle Veraart and Ben Jeurissen and Floris Vanhevel and Pim Pullens and Paul M Parizel and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1828,
	title = {An educational presentation on accurate and precise MRI relaxometry: the often disregarded but critical role of statistical parameter estimation},
	year = {2018},
	address = {Antwerp, Belgium},
	abstract = {MRI relaxometry holds the promise of providing biomarkers for monitoring, staging and follow up of diseases. Imperative to meet minimum standards for objective, reproducible, and reliable biomarkers is the need for accurate, precise, quantitative parameters maps, such as T1~or T2. While unrealistic physical modelling is often argued as the main cause of lack of accuracy, little effort has been made on discussing the impact that inadequate parameter estimation methods have on the accuracy and precision of MRI relaxometry techniques. This educational poster attempts to introduce young MR students/researchers into the basics of modern statistical parameter estimation theory, and its application for accurate and precise relaxometry. 
},
	author = {Gabriel Ramos-Llord{\'e}n and Quinten Beirinckx and Arnold Jan den Dekker and Jan Sijbers}
}
@mastersthesis {1832,
	title = {Improved MRI Relaxometry through Statistical Signal Processing},
	volume = { Doctor of Science},
	year = {2018},
	month = {02/2018},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	abstract = {Magnetic Resonance Imaging (MRI) relaxometry is a quantitative MRI modality that deals with the estimation of the spin-lattice, T1, and the spin-spin, T2, relaxation times. Both relaxation times are fundamental parameters that describe the spin dynamics within a tissue during the relaxation process of the Nuclear Magnetic Resonance (NMR) phenomenon. During the last decades, spatial T1 and T2 maps have been analyzed to study and monitor the states of a multitude of human diseases. Those studies have shown that MRI relaxometry holds the promise of generating robust, objective image-based biomarkers for central nervous system pathologies, cardiovascular diseases and beyond. Unfortunately, quantitative biomarkers derived from MRI relaxometry are not yet sufficiently specific, sensitive, and robust to be routinely used in clinical practice. On top of that, high-resolution relaxation maps demand a clinically unfeasible long scanning time.

This PhD thesis tries to reduce the obstacles that preclude MRI relaxometry from being a fast, accurate, and precise quantitative MRI modality for clinical use by improving the way MR relaxometry data are acquired, processed and analyzed. In particular, by adopting a statistical signal processing approach, three contributions that address common problems of the field are given. Firstly, we present a unified relaxometry-based processing framework where the T1 map estimation and motion correction are accounted for in a synergistic manner, being both the T1 map and the motion parameters simultaneously estimated with a Maximum-Likelihood estimator. It is demonstrated that substantially more accurate T1 maps are obtained with our proposed integrated approach in comparison to the yet typical but suboptimal two-step approach: T1 model fitting after image registration.  Secondly, we developed a fast, robust, T1 estimator for Variable Flip Angle (VFA) T1 mapping that can provide statistically optimal T1 estimates with unprecedentedly short computation time, thereby enabling optimal, real-time, VFA T1 mapping.  Finally, we were able to reduce the long overall scanning time of MRI relaxometry studies using our novel k-space reconstruction technique that permits the reconstruction of individual MR images with less number of samples than it is commonly required.},
	author = {Gabriel Ramos-Llord{\'e}n}
}
@conference {1957,
	title = {Maximising dose efficiency in quantitative STEM to reveal the 3D atomic structure of nanomaterials},
	year = {2018},
	author = {Sandra Van Aert and J Fatermans and Annick De Backer and van den Bos, K. H. W. and O{\textquoteright}Leary, C M. and M{\"u}ller-Caspary, K and Jones, L and Ivan Lobato and B{\'e}ch{\'e}, A and Arnold Jan den Dekker and Sara Bals and Peter D Nellist}
}
@conference {1891,
	title = {The maximum a posteriori probability rule to detect single atoms from low signal-to-noise ratio scanning transmission electron microscopy images},
	year = {2018},
	author = {J Fatermans and Arnold Jan den Dekker and M{\"u}ller-Caspary, K and Ivan Lobato and Sandra Van Aert}
}
@article {1842,
	title = {NOVIFAST: A fast algorithm for accurate and precise VFA MRI T1 mapping},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {37},
	year = {2018},
	pages = {2414 - 2427},
	doi = {10.1109/TMI.2018.2833288},
	author = {Gabriel Ramos-Llord{\'e}n and Gonzalo Vegas-S{\'a}nchez-Ferrero and Marcus Bj{\"o}rk and Floris Vanhevel and Paul M Parizel and Raul San Jos{\'e} Est{\'e}par and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1847,
	title = {Parametric Reconstruction of Advanced Glass Fiber-reinforced Polymer Composites from X-ray Images},
	year = {2018},
	address = {Wels, Austria},
	abstract = {A novel approach to the reconstruction of glass fiber-reinforced polymers (GFRP) from X-ray micro-computed tomography
(μCT) data is presented. The traditional fiber analysis workflow requires complete sample reconstruction, pre-processing and
segmentation, followed by the analysis of fiber distribution, orientation, and other features of interest. Each step in the chain
introduces errors that propagate through the pipeline and impair the accuracy of the estimation of those features. In the
approach presented in this paper, we combine iterative reconstruction techniques and a priori knowledge about the sample, to
reconstruct the volume and estimate the orientation of the fibers simultaneously. Fibers are modeled using rigid cylinders in
space whose orientation and position is then iteratively refined. The output of the algorithm is a non voxel-based dataset of the
fibers{\textquoteright} parametric representation, allowing to directly assess fiber features and distribution characteristics and to simulate the resulting material properties.},
	keywords = {GFRP, Materials Science, Modeling of Microstructures, Parametric Reconstruction, Tomography, {\textmu}CT},
	author = {Tim Elberfeld and Jan De Beenhouwer and Arnold Jan den Dekker and Christoph Heinzl and Jan Sijbers}
}
@article {1859,
	title = {Parametric Reconstruction of Glass Fiber-reinforced Polymer Composites from X-ray Projection Data - A Simulation Study},
	journal = {Journal of Nondestructive Evaluation},
	volume = {37},
	year = {2018},
	month = {Jul},
	pages = {1573-4862},
	abstract = {We present a new approach to estimate geometry parameters of glass fibers in glass fiber-reinforced polymers from simulated X-ray micro-computed tomography scans. Traditionally, these parameters are estimated using a multi-step procedure including image reconstruction, pre-processing, segmentation and analysis of features of interest. Each step in this chain introduces errors that propagate through the pipeline and impair the accuracy of the estimated parameters. In the approach presented in this paper, we reconstruct volumes from a low number of projection angles using an iterative reconstruction technique and then estimate position, direction and length of the contained fibers incorporating a priori knowledge about their shape, modeled as a geometric representation, which is then optimized. Using simulation experiments, we show that our method can estimate those representations even in presence of noisy data and only very few projection angles available.},
	keywords = {GFRP, Glass fiber reinforced polymer, Materials Science, Modeling of micro-structures, Parametric model, Tomography, {\textmu}CT},
	doi = {10.1007/s10921-018-0514-0},
	url = {https://doi.org/10.1007/s10921-018-0514-0},
	author = {Tim Elberfeld and Jan De Beenhouwer and Arnold Jan den Dekker and Christoph Heinzl and Jan Sijbers}
}
@conference {1920,
	title = {Simultaneous T2/diffusion estimation: do we need a diffusion time dependent diffusion model?},
	year = {2018},
	address = {Antwerp, Belgium},
	abstract = {Joint estimation of the transverse relaxation (T2) and the diffusion tensor allows for probing white matter integrity in a more efficient way compared to estimating these parameters from sequential relaxometry and diffusion imaging protocols. For simultaneous estimation of diffusion and relaxation parameters, short echo times are needed and, as a consequence, short diffusion times. Therefore we need a flexible diffusion model that accounts for diffusion time dependence. Currently used diffusion models do not account for this dependence. An acknowledged model describing the properties of diffusion in coherently oriented fiber populations is the diffusion tensor. Alternative models are proposed, which allow to resolve multiple fiber directions, such as multiple tensor fitting and estimation of the fiber orientation distribution function using high angular resolution data acquisition. When the diffusion time is kept short enough, the diffusion behavior reflects that of free diffusion, meaning that the diffusion tensor is isotropic. With increasing diffusion time, diffusion is hindered by extracellular and intracellular components and the diffusion tensor evolves towards an ellipsoid. To the authors{\textquoteright} knowledge there is no model available to correct for diffusion time dependence in clinically short range diffusion times, allowing joint estimation of relaxation and diffusion properties. This work provides an overview of the state-of-the-art and challenges of diffusion time dependent diffusion modeling.},
	author = {Annelinde E. Buikema and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1878,
	title = {Single Atom Detection from Low Contrast-to-Noise Ratio Electron Microscopy Images},
	journal = {Phys. Rev. Lett.},
	volume = {121},
	year = {2018},
	month = {Jul},
	pages = {056101},
	abstract = {Single atom detection is of key importance to solving a wide range of scientific and technological problems. The strong interaction of electrons with matter makes transmission electron microscopy one of the most promising techniques. In particular, aberration correction using scanning transmission electron microscopy has made a significant step forward toward detecting single atoms. However, to overcome radiation damage, related to the use of high-energy electrons, the incoming electron dose should be kept low enough. This results in images exhibiting a low signal-to-noise ratio and extremely weak contrast, especially for light-element nanomaterials. To overcome this problem, a combination of physics-based model fitting and the use of a model-order selection method is proposed, enabling one to detect single atoms with high reliability.},
	doi = {10.1103/PhysRevLett.121.056101},
	url = {https://link.aps.org/doi/10.1103/PhysRevLett.121.056101},
	author = {J Fatermans and Arnold Jan den Dekker and M{\"u}ller-Caspary, K. and Ivan Lobato and O{\textquoteright}Leary, C. M. and Peter D Nellist and Sandra Van Aert}
}
@article {1712,
	title = {Atom-counting in High Resolution Electron Microscopy: TEM or STEM - that{\textquoteright}s the question},
	journal = {Ultramicroscopy},
	volume = {147},
	year = {2017},
	pages = {112{\textendash}120},
	doi = {http://dx.doi.org/10.1016/j.ultramic.2016.10.011},
	author = {Julie Gonnissen and Annick De Backer and Arnold Jan den Dekker and Jan Sijbers and Sandra Van Aert}
}
@conference {1889,
	title = {Detection of atomic columns from noisy STEM images},
	year = {2017},
	pages = {445-446},
	author = {J Fatermans and M{\"u}ller-Caspary, K and Arnold Jan den Dekker and Sandra Van Aert}
}
@conference {1996,
	title = {Maximizing precision in PCASL MRI using an optimized sampling strategy},
	year = {2017},
	author = {Piet Bladt and Arnold Jan den Dekker and Clement, Patricia and Eric Achten and Jan Sijbers}
}
@article {1775,
	title = {A nonlocal maximum likelihood estimation method for enhancing magnetic resonance phase maps},
	journal = {Signal Image and Video Processing},
	volume = {11},
	year = {2017},
	pages = { 913-920},
	author = {P V Sudeep and Palanisamy, P and Chandrasekharan Kesavadas and Jan Sijbers and Arnold Jan den Dekker and Jeny Rajan}
}
@conference {1997,
	title = {Optimal sampling strategy for pseudo-continuous arterial spin labeling MRI},
	year = {2017},
	author = {Piet Bladt and Arnold Jan den Dekker and Clement, Patricia and Eric Achten and Jan Sijbers}
}
@article {1722,
	title = {Partial Discreteness: a Novel Prior for Magnetic Resonance Image Reconstruction},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {36},
	year = {2017},
	pages = {1041 - 1053},
	doi = {10.1109/TMI.2016.2645122},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1764,
	title = {Solving the Free Water Elimination Estimation Problem by Incorporating T2 Relaxation Properties},
	year = {2017},
	author = {Quinten Collier and Jelle Veraart and Arnold Jan den Dekker and Floris Vanhevel and Paul M Parizel and Jan Sijbers}
}
@conference {1818,
	title = {Statistically optimal separation of multi-component MR signals with a Majorize-Minimize approach: application to MWF estimation},
	year = {2017},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Piet Bladt and A. Cuyt and Jan Sijbers}
}
@conference {1826,
	title = {Super-resolution multi-PLD PCASL: a simulation study},
	volume = {30 (Suppl. 1)},
	number = {S396},
	year = {2017},
	publisher = {Magn Reson Mater Phy},
	abstract = {Cerebral blood flow (CBF) can be estimated non-invasively with arterial spin labeling (ASL). Multi-post-labeling-delay (PLD) pseudo-continuous ASL (PCASL) allows for accurate CBF estimation by sampling the dynamic perfusion signal at different PLDs and fitting a model to the perfusion data. Unfortunately, ASL difference images have a low SNR. Therefore, CBF estimation in multi-PLD PCASL is imprecise, unless a large number of images is acquired (long scan time) or spatial resolution is lowered significantly. It has been shown that model-based super-resolution reconstruction (SRR) techniques can improve the trade-off between SNR, spatial resolution and acquisition time. The results presented in this work show the promising potential of SRR ASL to outperform conventional ASL readout schemes in terms of achievable precision of HR perfusion measurements in a given acquisition time.},
	doi = {10.1007/s10334-017-0634-z},
	author = {Piet Bladt and Quinten Beirinckx and Gwendolyn Van Steenkiste and Ben Jeurissen and Eric Achten and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1668,
	title = {Super-resolution T1 estimation: quantitative high resolution T1 mapping from a set of low resolution T1 weighted images with different slice orientations},
	journal = {Magnetic Resonance in Medicine},
	volume = {77},
	year = {2017},
	pages = {1818{\textendash}1830},
	doi = {10.1002/mrm.26262},
	author = {Gwendolyn Van Steenkiste and Dirk H J Poot and Ben Jeurissen and Arnold Jan den Dekker and Floris Vanhevel and Paul M Parizel and Jan Sijbers}
}
@article {1706,
	title = {A unified Maximum Likelihood framework for simultaneous motion and T1 estimation in quantitative MR T1 mapping},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {36},
	year = {2017},
	pages = {433 - 446},
	doi = {10.1109/TMI.2016.2611653},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Gwendolyn Van Steenkiste and Ben Jeurissen and Floris Vanhevel and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@conference {1784,
	title = {A workflow to reconstruct grating-based X-ray phase contrast CT images: application to CFRP samples},
	year = {2017},
	pages = {139-140},
	address = {Z{\"u}rich, Switzerland},
	abstract = {Carbon fiber reinforced polymer (CFRP) is an extremely strong and lightweight plastic of which the strength depends on the distribution of its fibers. Fiber bundles can be visualized by means of phase contrast X-ray computed tomography (PCCT) based on grating-based interferometry (GBI). However, many steps are involved in the reconstruction of GBI-PCCT images. In this abstract, a workflow for the reconstruction of 3D CFRP phase contrast images based on GBI projection data is presented.},
	keywords = {Carbon fiber reinforced polymer, Image processing, Phase contrast, Tomography, X-rays},
	url = {https://indico.psi.ch/getFile.py/access?resId=1\&materialId=2\&confId=5055},
	author = {Jonathan Sanctorum and Eline Janssens and Arnold Jan den Dekker and Sascha Senck and Christoph Heinzl and Jan De Beenhouwer and Jan Sijbers}
}
@article {VanAert:gq5005,
	title = {Advanced electron crystallography through model-based imaging},
	journal = {IUCrJ},
	volume = {3},
	number = {1},
	year = {2016},
	month = {Jan},
	abstract = {The increasing need for precise determination of the atomic arrangement of non-periodic structures in materials design and the control of nanostructures explains the growing interest in quantitative transmission electron microscopy. The aim is to extract precise and accurate numbers for unknown structure parameters including atomic positions, chemical concentrations and atomic numbers. For this purpose, statistical parameter estimation theory has been shown to provide reliable results. In this theory, observations are considered purely as data planes, from which structure parameters have to be determined using a parametric model describing the images. As such, the positions of atom columns can be measured with a precision of the order of a few picometres, even though the resolution of the electron microscope is still one or two orders of magnitude larger. Moreover, small differences in average atomic number, which cannot be distinguished visually, can be quantified using high-angle annular dark-field scanning transmission electron microscopy images. In addition, this theory allows one to measure compositional changes at interfaces, to count atoms with single-atom sensitivity, and to reconstruct atomic structures in three dimensions. This feature article brings the reader up to date, summarizing the underlying theory and highlighting some of the recent applications of quantitative model-based transmisson electron microscopy.},
	keywords = {experimental design, quantitative analysis, statistical parameter estimation, structure refinement, transmission electron microscopy},
	doi = {10.1107/S2052252515019727},
	url = {http://dx.doi.org/10.1107/S2052252515019727},
	author = {Sandra Van Aert and Annick De Backer and Martinez, Gerardo T. and Arnold Jan den Dekker and Dirk Van Dyck and Sara Bals and Van Tendeloo, Gustaaf}
}
@conference {1888,
	title = {Bayesian model-order selection in electron microscopy to detect atomic columns in noisy images},
	year = {2016},
	pages = {53},
	author = {J Fatermans and Sandra Van Aert and Arnold Jan den Dekker}
}
@article {1688,
	title = {Detecting and locating light atoms from high-resolution STEM images: the quest for a single optimal design Ultramicroscopy},
	journal = {Ultramicroscopy},
	volume = {170},
	year = {2016},
	pages = {128-138},
	doi = {http://dx.doi.org/10.1016/j.ultramic.2016.07.014},
	author = {Julie Gonnissen and Annick De Backer and Arnold Jan den Dekker and Jan Sijbers and Sandra Van Aert}
}
@conference {Van_Steenkiste2016-fy,
	title = {High Resolution Diffusion Tensor Reconstruction from Simultaneous Multi-Slice Acquisitions in a Clinically Feasible Scan Time},
	year = {2016},
	pages = {2},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Baete, Steven and Arnold Jan den Dekker and Dirk H J Poot and Boada, Fernando and Jan Sijbers}
}
@conference {1642,
	title = {In vivo high resolution diffusion tensor imaging in a clinically acceptable scan time by combining super resolution reconstruction with simultaneous multi-slice acquisition},
	volume = {8},
	number = {P-036},
	year = {2016},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Steven Baete and Arnold Jan den Dekker and Dirk H J Poot and Fernando Boada and Jan Sijbers}
}
@inproceedings {1653,
	title = {Multi-voxel algorithm for quantitative bi-exponential MRI T1 estimation},
	booktitle = {SPIE Medical Imaging},
	volume = {9784},
	year = {2016},
	pages = {978402},
	address = {San Diego, California, United States of America},
	abstract = {In this work, we propose a joint multi-voxel bi-exponential estimator (JMBE) for quantitative bi-exponential T1 estimation in magnetic resonance imaging, to account for partial volume effects and to yield more accurate results compared to single-voxel bi-exponential estimators (SBEs). Using a numerical brain phantom with voxels containing two tissues, the minimal signal-to-noise ratio (SNR) needed to estimate both T1 values with sufficient accuracy was investigated. Compared to the SBE, and for clinically achievable single-voxel SNRs, the JMBE yields accurate T1 estimates if four or more neighboring voxels are used in the joint estimation framework, in which case it is also efficient.},
	doi = {http://dx.doi.org/10.1117/12.2216831},
	author = {Piet Bladt and Gwendolyn Van Steenkiste and Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1647,
	title = {NOVIFAST: A fast non-linear least squares method for accurate and precise estimation of T1 from SPGR signals},
	year = {2016},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Marcus Bj{\"o}rk and Marleen Verhoye and Jan Sijbers}
}
@conference {Collier2016-tu,
	title = {Robust DKI Parameter Estimation in Case of CSF Partial Volume Effects},
	year = {2016},
	pages = {1044},
	author = {Collier, Quinten and Arnold Jan den Dekker and Ben Jeurissen and Jan Sijbers}
}
@conference {1672,
	title = {Robust DKI parameter estimation in case of CSF partial volume effects},
	year = {2016},
	abstract = {Diffusion kurtosis imaging (DKI) suffers from partial volume effects caused by cerebrospinal fluid (CSF). We propose a DKI+CSF model combined with a framework to robustly estimate the DKI parameters. Since the estimation problem is ill-conditioned, a Bayesian estimation approach with a shrinkage prior is incorporated. Both simulation and real data experiments suggest that the use of this prior leads to a more accurate, precise and robust estimation of the DKI+CSF model parameters. Finally, we show that not correcting for the CSF compartment can lead to severe biases in the parameter estimations.},
	author = {Quinten Collier and Arnold Jan den Dekker and Ben Jeurissen and Jan Sijbers}
}
@conference {1671,
	title = {A robust framework for combined estimation of DKI and CSF partial volume fraction parameters},
	year = {2016},
	abstract = {Diffusion kurtosis imaging (DKI) suffers from partial volume effects caused by cerebrospinal fluid (CSF). We propose a DKI+CSF model combined with a framework to robustly estimate the DKI parameters. Since the estimation problem is ill-conditioned, a Bayesian estimation approach with a shrinkage prior is incorporated. Both simulation and real data experiments suggest that the use of this prior leads to a more accurate, precise and robust estimation of the DKI+CSF model parameters. Finally, we show that not correcting for the CSF compartment can lead to severe biases in the parameter estimations.},
	author = {Quinten Collier and Arnold Jan den Dekker and Ben Jeurissen and Jan Sijbers}
}
@article {1536,
	title = {Super-resolution reconstruction of diffusion parameters from diffusion-weighted images with different slice orientations},
	journal = {Magnetic Resonance in Medicine},
	volume = {75},
	year = {2016},
	pages = {181-195},
	doi = {10.1002/mrm.25597},
	url = {http://onlinelibrary.wiley.com/doi/10.1002/mrm.25597/abstract},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Jelle Veraart and Arnold Jan den Dekker and Paul M Parizel and Dirk H J Poot and Jan Sijbers}
}
@conference {1624,
	title = {CSF partial volume modeling in diffusion kurtosis imaging: a comparative parameter estimation study},
	year = {2015},
	month = {11/2015},
	doi = {10.3389/conf.fninf.2015.19.00039},
	url = {http://www.frontiersin.org/myfrontiers/events/abstractdetails.aspx?abs_doi=10.3389/conf.fninf.2015.19.00039},
	author = {Quinten Collier and Jelle Veraart and Arnold Jan den Dekker and Ben Jeurissen and Jan Sijbers}
}
@conference {1633,
	title = {High resolution diffusion tensor imaging in a clinically feasible scan time},
	year = {2015},
	doi = {10.3389/conf.fninf.2015.19.00016},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Steven Baete and Arnold Jan den Dekker and Dirk H J Poot and Fernando Boada and Jan Sijbers}
}
@inproceedings {1548,
	title = {High resolution T1 estimation from multiple low resolution magnetic resonance images},
	booktitle = {IEEE International Symposium on Biomedical Imaging (ISBI): From nano to macro},
	volume = {12},
	year = {2015},
	pages = {1036-1039},
	doi = {10.1109/ISBI.2015.7164048},
	author = {Gwendolyn Van Steenkiste and Dirk H J Poot and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1492,
	title = {Iterative Reweighted Linear Least Squares for Accurate, Fast, and Robust Estimation of Diffusion Magnetic Resonance Parameters},
	journal = {Magnetic Resonance in Medicine},
	volume = {73},
	year = {2015},
	pages = {2174{\textendash}2184},
	abstract = {Purpose: Diffusion-weighted magnetic resonance imaging suffers from physiological noise, such as artifacts caused by motion or system instabilities. Therefore, there is a need for robust diffusion parameter estimation techniques. In the past, several techniques have been proposed, including RESTORE and iRESTORE. However, these techniques are based on nonlinear estimators and are consequently computationally intensive.
Method: In this work, we present a new, robust, iteratively reweighted linear least squares (IRLLS) estimator. IRLLS performs a voxel-wise identification of outliers in diffusion-weighted magnetic resonance images, where it exploits the natural skewness of the data distribution to become more sensitive to both signal hyperintensities and signal dropouts. 
Results: Both simulations and real data experiments were conducted to compare IRLLS with other state-of-the-art techniques. While IRLLS showed no significant loss in accuracy or precision, it proved to be substantially faster than both RESTORE and iRESTORE. In addition, IRLLS proved to be even more robust when considering the overestimation of the noise level or when the signal-to-noise ratio is low. 
Conclusion: The substantially shortened calculation time in combination with the increased robustness and accuracy, make IRLLS a practical and reliable alternative to current state-of-theart
techniques for the robust estimation of diffusion-weighted magnetic resonance parameters.},
	keywords = {diffusion tensor imaging, MRI, outlier detection, robust, weighted linear least squares},
	doi = {10.1002/mrm.25351},
	author = {Quinten Collier and Jelle Veraart and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1561,
	title = {Partial discreteness: a new type of prior knowledge for MRI reconstruction},
	volume = {23},
	year = {2015},
	pages = {3417},
	author = {Gabriel Ramos-Llord{\'e}n and Segers, Hilde and Willem Jan Palenstijn and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {7351081,
	title = {Partially discrete magnetic resonance tomography},
	booktitle = {2015 IEEE International Conference on Image Processing (ICIP)},
	year = {2015},
	month = {Sept},
	pages = {1653-1657},
	keywords = {Bayes methods, Bayesian segmentation, Bayesian segmentation regularization, biomedical MRI, Breast, breast implant MR images, computerised tomography, Discrete tomography, image reconstruction, image representation, image segmentation, Implants, medical image processing, MR angiography images, MR image reconstruction, MRI, partially discrete magnetic resonance tomography, reconstruction, Tomography, TV},
	doi = {10.1109/ICIP.2015.7351081},
	author = {Gabriel Ramos-Llord{\'e}n and Segers, Hilde and Willem Jan Palenstijn and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1537,
	title = {Simultaneous group-wise rigid registration and T1 ML estimation for T1 mapping},
	year = {2015},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Gwendolyn Van Steenkiste and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@conference {1537,
	title = {Simultaneous group-wise rigid registration and T1 ML estimation for T1 mapping},
	volume = {23},
	year = {2015},
	pages = {447},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Gwendolyn Van Steenkiste and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@inproceedings {7351386,
	title = {Simultaneous motion correction and T1 estimation in quantitative T1 mapping: An ML restoration approach},
	booktitle = {2015 IEEE International Conference on Image Processing (ICIP)},
	year = {2015},
	month = {Sept},
	pages = {3160-3164},
	keywords = {alignment, Approximation methods, biomedical MRI, image restoration, interpolation effect, Magnetic Resonance Imaging, maximum likelihood approach, maximum likelihood estimation, medical image processing, ML approach, ML restoration approach, motion estimation, motion model parameters, quantitative T1 mapping, registration, relaxometry, Rician channels, Signal to noise ratio, simultaneous motion correction, Standards, T1 estimation, T1 mapping, T1-weighted images, tissue spin-lattice relaxation time, voxel-wise estimation},
	doi = {10.1109/ICIP.2015.7351386},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Gwendolyn Van Steenkiste and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@conference {1596,
	title = {Super-resolution T1 mapping: a simulation study},
	volume = {23},
	year = {2015},
	pages = {1679},
	author = {Gwendolyn Van Steenkiste and Dirk H J Poot and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1545,
	title = {Theoretical study of the free water elimination model},
	volume = {23},
	year = {2015},
	pages = {2757},
	abstract = {Partial volume effects caused by cerebrospinal fluid are an important issue in diffusion MRI. In this work, we study the free water elimination model by analyzing the Cram{\'e}r-Rao lower bound (CRLB) of its parameters. We show that through optimizing the acquisition protocol by minimizing the trace of the CRLB, a significant gain in the precision of the parameter estimation can be achieved. Moreover, further analysis indicates that regularization and/or constraints are necessary for parameter estimation from voxels with large CSF fractions and/or low FA values. These theoretical findings are confirmed by both simulation and real data experiments.},
	author = {Quinten Collier and Jelle Veraart and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1544,
	title = {Theoretical study of the free water elimination model},
	year = {2015},
	pages = {78},
	abstract = {Partial volume effects caused by cerebrospinal fluid are an important issue in diffusion MRI. In this work, we study the free water elimination model by analyzing the Cram{\'e}r-Rao lower bound (CRLB) of its parameters. We show that through optimizing the acquisition protocol by minimizing the trace of the CRLB, a significant gain in the precision of the parameter estimation can be achieved. Moreover, further analysis indicates that regularization and/or constraints are necessary for parameter estimation from voxels with large CSF fractions and/or low FA values. These theoretical findings are confirmed by both simulation and real data experiments.},
	author = {Quinten Collier and Jelle Veraart and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {Collier2015-pa,
	title = {Theoretical study of the free water elimination model},
	volume = {15},
	year = {2015},
	pages = {2757},
	author = {Collier, Quinten and Veraart, Jelle and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1476,
	title = {Data distributions in magnetic resonance images: a review},
	journal = {Physica Medica},
	volume = {30},
	year = {2014},
	pages = {725{\textendash}741},
	doi = {http://dx.doi.org/10.1016/j.ejmp.2014.05.002},
	author = {Arnold Jan den Dekker and Jan Sijbers}
}
@booklet {1538,
	title = {Joint motion correction and estimation for T1 mapping: proof of concept},
	howpublished = {Medical Imaging Summer School 2014, Favignana, Italy},
	year = {2014},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1441,
	title = {A new non local maximum likelihood estimation method for Rician noise reduction in Magnetic Resonance images using the Kolmogorov-Smirnov test},
	journal = {Signal Processing},
	volume = {103},
	year = {2014},
	pages = {16-23},
	doi = {http://dx.doi.org/10.1016/j.sigpro.2013.12.018},
	author = {Jeny Rajan and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1497,
	title = {Optimal experimental design for the detection of light atoms from high-resolution scanning transmission electron microscopy images},
	journal = {Applied Physics Letters},
	volume = {105},
	year = {2014},
	doi = {10.1063/1.4892884},
	author = {Julie Gonnissen and Annick De Backer and Arnold Jan den Dekker and G T Martinez and A. Rosenauer and Jan Sijbers and Sandra Van Aert}
}
@article {1480,
	title = {The reconstructed residual error: A novel segmentation evaluation measure for reconstructed images in tomography},
	journal = {Computer Vision and Image Understanding},
	volume = {126},
	year = {2014},
	pages = {28-37},
	doi = {10.1016/j.cviu.2014.05.007},
	author = {Roelandts, Tom and Kees Joost Batenburg and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1395,
	title = {Estimation of unknown structure parameters from high-resolution (S)TEM images: what are the limits?},
	journal = {Ultramicroscopy},
	volume = {134},
	year = {2013},
	pages = {34-43},
	doi = {http://dx.doi.org/10.1016/j.ultramic.2013.05.017},
	url = {http://www.sciencedirect.com/science/article/pii/S0304399113001368},
	author = {Arnold Jan den Dekker and Julie Gonnissen and Annick De Backer and Jan Sijbers and Sandra Van Aert}
}
@inproceedings {1439,
	title = {A New Nonlocal Maximum Likelihood Estimation Method for Denoising Magnetic Resonance Images},
	booktitle = {5th International Conference, PReMI 2013, Kolkata, India, December 10-14, 2013. Proceedings},
	volume = {8251},
	year = {2013},
	month = {2013},
	edition = {Lecture Notes in Computer Science},
	doi = {10.1007/978-3-642-45062-4_62},
	author = {Jeny Rajan and Arnold Jan den Dekker and Juntu, Jaber and Jan Sijbers}
}
@article {dpootajdendekAchtenmverhoyejsijbers2010,
	title = {Optimal experimental design for Diffusion Kurtosis Imaging},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {29},
	number = {3},
	year = {2010},
	pages = {819-829},
	doi = {http://dx.doi.org/10.1109/TMI.2009.2037915},
	author = {Dirk H J Poot and Arnold Jan den Dekker and Eric Achten and Marleen Verhoye and Jan Sijbers}
}
@mastersthesis {1290,
	title = {Advances in the reconstruction and statistical processing of Magnetic Resonance images},
	volume = {PhD in Sciences: Physics},
	year = {2009},
	type = {PhD Thesis},
	author = {Dirk H J Poot}
}
@article {ajdendekdpootBosjsijbers2009,
	title = {Likelihood based hypothesis tests for brain activation detection from MRI data disturbed by colored noise: a simulation study},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {28},
	number = {2},
	year = {2009},
	month = {February},
	pages = {287-296},
	author = {Arnold Jan den Dekker and Dirk H J Poot and R. Bos and Jan Sijbers}
}
@conference {dpootajdendekmverhoyeBlockxAudekerkeavdlindejsijbers2009,
	title = {Optimizing the Diffusion Weighting Gradients for Diffusion-Kurtosis Imaging},
	volume = {2009},
	year = {2009},
	month = {April},
	pages = {1394},
	author = {Dirk H J Poot and Arnold Jan den Dekker and Marleen Verhoye and Ines Blockx and Johan Van Audekerke and Annemie Van Der Linden and Jan Sijbers}
}
@conference {dpootajdendekjsijbers2009,
	title = {Pearson Set of Distributions as Improved Signal Model for Diffusion Kurtosis Imaging},
	year = {2009},
	month = {April},
	pages = {1383},
	publisher = {ISMRM},
	author = {Dirk H J Poot and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {dpootjsijbersajdendek2008,
	title = {An exploration of spatial similarities in temporal noise spectra in fMRI measurements},
	booktitle = {Proceedings of SPIE Medical Imaging 2008},
	volume = {6914},
	year = {2008},
	month = {February},
	pages = {69142},
	address = {San Diego, CA, USA},
	author = {Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {dpootjsijbersajdendek2008,
	title = {Optimizing the Diffusion Kurtosis imaging acquisition},
	booktitle = {European Society for Magnetic Resonance in Medicine and Biology},
	year = {2008},
	month = {October},
	address = {Valencia, Spain},
	author = {Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker}
}
@article {jsijbersdpootajdendekwpintjen2007,
	title = {Automatic estimation of the noise variance from the histogram of a magnetic resonance image},
	journal = {Physics in Medicine and Biology},
	volume = {52},
	number = {5},
	year = {2007},
	month = {February},
	pages = {1335-1348},
	doi = {10.1088/0031-9155/52/5/009},
	author = {Jan Sijbers and Dirk H J Poot and Arnold Jan den Dekker and W. Pintjens}
}
@inproceedings {dpootjsijbersDekkerwpintjen2006,
	title = {Automatic estimation of the noise variance from the histogram of a magnetic resonance image},
	booktitle = {IEEE/EBMS Benelux Symposium proceedings},
	year = {2006},
	month = {December},
	pages = {135-138},
	publisher = {IEEE/EMBS},
	organization = {IEEE/EMBS},
	author = {Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker and W. Pintjens}
}
@inproceedings {dpootjsijbersajdendekBos2006,
	title = {Estimation of the noise variance from the background histogram mode of an MR image},
	booktitle = {Proceedings of the 25th Benelux Meeting on Systems and Control},
	series = {TuM06-5},
	year = {2006},
	month = {March},
	address = {Heeze, The Netherlands},
	author = {Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker and R. Bos}
}
@inproceedings {dpootjsijbersajdendekBos2006,
	title = {Estimation of the noise variance from the background histogram mode of an MR image},
	booktitle = {Proceedings of SPS-DARTS 2006 (The second annual IEEE BENELUX/DSP Valley Signal Processing Symposium)},
	year = {2006},
	month = {March},
	pages = {159-162},
	address = {Antwerp, Belgium},
	author = {Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker and R. Bos}
}
@inproceedings {jsijbersajdendekdpootBosmverhoyeCampavdlinde2006,
	title = {Robust estimation of the noise variance from background MR data},
	booktitle = {Proceedings of SPIE Medical Imaging: Image Processing},
	volume = {6144},
	year = {2006},
	month = {February},
	pages = {2018-2028},
	address = {San Diego, CA, USA},
	author = {Jan Sijbers and Arnold Jan den Dekker and Dirk H J Poot and R. Bos and Marleen Verhoye and N. Van Camp and Annemie Van Der Linden}
}
@inbook {ajdendekjsijbers2005,
	title = {Advanced Image Processing in Magnetic Resonance Imaging},
	booktitle = {Series: Signal Processing and Communications},
	volume = {27},
	year = {2005},
	note = {ISBN: 0824725425},
	month = {October},
	pages = {85-143},
	publisher = {Marcel Dekker},
	organization = {Marcel Dekker},
	chapter = {4},
	author = {Arnold Jan den Dekker and Jan Sijbers},
	editor = {L. Landini}
}
@inproceedings {ajdendekjsijbersBosasmolder2005,
	title = {Brain activation detection from functional magnetic resonance imaging data using likelihood based hypothesis tests},
	booktitle = {Abstracts of the 24th Benelux Meeting on Systems and Control},
	year = {2005},
	month = {March},
	address = {Houffalize, Belgium},
	author = {Arnold Jan den Dekker and Jan Sijbers and R. Bos and Alain Smolders}
}
@article {jsijbersajdendek2005,
	title = {Generalized likelihood Ratio tests for complex fMRI data: a simulation study},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {24},
	number = {5},
	year = {2005},
	month = {May},
	pages = {604-611},
	doi = {10.1109/TMI.2005.844075},
	author = {Jan Sijbers and Arnold Jan den Dekker}
}
@article {ajdendekjsijbers2005,
	title = {Implications of the Rician distribution for fMRI generalized likelihood ratio tests},
	journal = {Magnetic Resonance Imaging},
	volume = {23},
	number = {9},
	year = {2005},
	pages = {953-959},
	doi = {10.1016/j.mri.2005.07.008},
	author = {Arnold Jan den Dekker and Jan Sijbers}
}
@article {jsijbersajdendekBos2005,
	title = {A likelihood ratio test for functional MRI data analysis to account for colored noise},
	journal = {Lecture Notes in Computer Science},
	volume = {3708},
	year = {2005},
	month = {September},
	pages = {538-546},
	author = {Jan Sijbers and Arnold Jan den Dekker and R. Bos}
}
@inproceedings {ajdendekjsijbers2004,
	title = {Brain activation detection from magnitude fMRI data using a generalized likelihood ratio test},
	booktitle = {Abstracts of the 23rd Benelux Meeting on Systems and Control},
	year = {2004},
	month = {March},
	address = {Helvoirt, The Netherlands},
	author = {Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {jsijbersajdendek2004,
	title = {Construction of a likelihood ratio test for magnitude fMRI data},
	booktitle = {19th Annual Symposium of the Belgian Hospital Physicists Association},
	year = {2004},
	month = {January},
	author = {Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {jsijbersCampaleemansajdendekmverhoyeavdlinde2004,
	title = {Coregistration of Micro-MRI, microCT and microPET},
	booktitle = {Workshop on non Invasive 3D Microscopy},
	year = {2004},
	month = {August},
	pages = {16},
	address = {University of Antwerp, Belgium},
	author = {Jan Sijbers and N. Van Camp and Alexander Leemans and Arnold Jan den Dekker and Marleen Verhoye and Annemie Van Der Linden}
}
@inproceedings {ajdendekjsijbers2004,
	title = {Detection of brain activation from magnitude fMRI data using a generalized likelihood ratio test},
	booktitle = {Proceedings of the 12th European Signal Processing Conference},
	year = {2004},
	month = {September},
	pages = {233-236},
	publisher = {ISBN 3-200-00165-8},
	organization = {ISBN 3-200-00165-8},
	address = {Vienna, Austria},
	author = {Arnold Jan den Dekker and Jan Sijbers},
	editor = {F. Hlawatsch and B. Wistawel}
}
@inproceedings {jsijbersajdendek2004,
	title = {Generalized likelihood ratio test for complex fMRI data},
	booktitle = {SPIE Medical Imaging: Physiology, Function, and Structure from Medical Images},
	volume = {5369},
	year = {2004},
	month = {February},
	pages = {652-663},
	address = {San Diego, California, USA},
	author = {Jan Sijbers and Arnold Jan den Dekker},
	editor = {Amir. A. Amini}
}
@article {jsijbersajdendek2004,
	title = {Maximum Likelihood estimation of signal amplitude and noise variance from MR data},
	journal = {Magnetic Resonance in Medicine},
	volume = {51},
	number = {3},
	year = {2004},
	pages = {586-594},
	doi = {10.1002/mrm.10728},
	author = {Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {jsijbersajdendek2004,
	title = {The performance of generalized likelihood ratio tests for complex functional MRI data in the presence of phase model misspecification},
	booktitle = {European Society of Magnetic Resonance in Medicine (ESMRMB)},
	year = {2004},
	month = {September},
	pages = {96},
	address = {Copenhagen, Denmark},
	author = {Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {jsijbersajdendek2003,
	title = {Mapping a polyhedron onto a sphere: application to Fourier descriptors},
	booktitle = {22nd Benelux Meeting on Systems and Control},
	year = {2003},
	month = {March},
	address = {Lommel, Belgium},
	author = {Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {ajdendekjsijbers2003,
	title = {Maximum Likelihood estimation of signal amplitude and noise variance},
	booktitle = {13th IFAC Symposium on System Identification (SYSID-2003)},
	year = {2003},
	month = {August},
	pages = {126-131},
	address = {Rotterdam, The Netherlands},
	author = {Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {jsijbersajdendekmverhoyeavdlindedvandyck1999,
	title = {Adaptive anisotropic noise filtering for magnitude MR data},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {3661},
	year = {1999},
	month = {February},
	pages = {1418-1425},
	address = {San Diego, USA},
	author = {Jan Sijbers and Arnold Jan den Dekker and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck},
	editor = {Kenneth M. Hanson}
}
@article {jsijbersajdendekmverhoyeavdlindedvandyck1999,
	title = {Adaptive anisotropic noise filtering for magnitude MR data},
	journal = {Magnetic Resonance Imaging},
	volume = {17},
	number = {10},
	year = {1999},
	pages = {1533-1539},
	doi = {10.1016/S0730-725X(99)00088-0},
	author = {Jan Sijbers and Arnold Jan den Dekker and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck}
}
@article {ajdendekjsijbersdvandyck1999,
	title = {How to optimize the design of a quantitative HREM experiment so as to attain the highest precision},
	journal = {Journal of Microscopy},
	volume = {194},
	number = {1},
	year = {1999},
	pages = {95-104},
	author = {Arnold Jan den Dekker and Jan Sijbers and Dirk Van Dyck}
}
@article {ebettensdvandyckajdendekjsijbersBos1999,
	title = {Model-based two-object resolution from observations having counting statistics},
	journal = {Ultramicroscopy},
	volume = {77},
	number = {1},
	year = {1999},
	pages = {37-48},
	doi = {https://doi.org/10.1016/S0304-3991(99)00006-6},
	author = {E. Bettens and Dirk Van Dyck and Arnold Jan den Dekker and Jan Sijbers and A. van den Bos}
}
@article {jsijbersajdendekeramandvandyck1999,
	title = {Parameter estimation from magnitude MR images},
	journal = {International Journal of Imaging Systems and Technology},
	volume = {10},
	number = {2},
	year = {1999},
	pages = {109-114},
	doi = {10.1002/(SICI)1098-1098(1999)10:2<109::AID-IMA2>3.0.CO;2-R},
	author = {Jan Sijbers and Arnold Jan den Dekker and Erik Raman and Dirk Van Dyck}
}
@inproceedings {ajdendekjsijbersdvandyck1999,
	title = {Quantitative HREM: viewpoints on resolution, precision, and experimental design},
	booktitle = {Acta Cryst. A55 Supplement, Abstract M11.OE.OO5},
	year = {1999},
	month = {August},
	address = {IUCR Glasgow, Scotland},
	author = {Arnold Jan den Dekker and Jan Sijbers and Dirk Van Dyck}
}
@article {dvandyckebettensjsijbersBeeckBosajdendekJansenZandbergen1999,
	title = {Towards quantitative structure determination through electron holographic methods},
	journal = {Materials Characterization},
	volume = {42},
	number = {4},
	year = {1999},
	pages = {265-281},
	doi = {https://doi.org/10.1016/S1044-5803(99)00020-0},
	author = {Dirk Van Dyck and E. Bettens and Jan Sijbers and M. Op de Beeck and A. van den Bos and Arnold Jan den Dekker and J. Jansen and H. Zandbergen}
}
@inproceedings {dvandyckajdendekjsijbersebettens1998,
	title = {Dose Limited Resolution},
	booktitle = {Proceedings Microscopy and Microanalysis},
	volume = {2},
	number = {2},
	year = {1998},
	month = {July},
	pages = {802-803},
	address = {Atlanta, Georgia, U.S.A},
	author = {Dirk Van Dyck and Arnold Jan den Dekker and Jan Sijbers and E. Bettens}
}
@inproceedings {jsijbersajdendekdvandyckeraman1998,
	title = {Estimation of signal and noise from Rician distributed data},
	booktitle = {Proceedings of the IASTED International Conference on Signal Processing and Communications},
	year = {1998},
	month = {February},
	pages = {140-143},
	address = {Canary Islands, Spain},
	author = {Jan Sijbers and Arnold Jan den Dekker and Dirk Van Dyck and Erik Raman}
}
@article {jsijbersajdendekAudekerkemverhoyedvandyck1998,
	title = {Estimation of the noise in magnitude MR images},
	journal = {Magnetic Resonance Imaging},
	volume = {16},
	number = {1},
	year = {1998},
	pages = {87-90},
	doi = {10.1016/S0730-725X(97)00199-9},
	author = {Jan Sijbers and Arnold Jan den Dekker and Johan Van Audekerke and Marleen Verhoye and Dirk Van Dyck}
}
@inproceedings {ajdendekjsijbersdvandyck1998,
	title = {How to design an HREM experiment so as to attain the highest precision?},
	booktitle = {ICEM14: 14th International Congress on Electron Microscopy},
	volume = {1},
	year = {1998},
	month = {September},
	pages = {621-622},
	address = {Cancun, Mexico},
	author = {Arnold Jan den Dekker and Jan Sijbers and Dirk Van Dyck}
}
@article {jsijbersajdendekpscheunddvandyck1998,
	title = {Maximum Likelihood estimation of Rician distribution parameters},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {17},
	number = {3},
	year = {1998},
	pages = {357-361},
	doi = {10.1109/42.712125},
	author = {Jan Sijbers and Arnold Jan den Dekker and Paul Scheunders and Dirk Van Dyck}
}
@inproceedings {ajdendekjsijbersmverhoyedvandyck1998,
	title = {Maximum Likelihood estimation of the signal component magnitude in phase contrast MR images},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {3338},
	year = {1998},
	month = {February},
	pages = {408-415},
	address = {San Diego, California, USA},
	author = {Arnold Jan den Dekker and Jan Sijbers and Marleen Verhoye and Dirk Van Dyck}
}
@inproceedings {jsijbersajdendekmverhoyedvandyck1998,
	title = {Optimal estimation of T2 maps from magnitude MR data},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {3338},
	year = {1998},
	month = {February},
	pages = {384-390},
	address = {San Diego, CA, USA},
	author = {Jan Sijbers and Arnold Jan den Dekker and Marleen Verhoye and Dirk Van Dyck},
	editor = {Kenneth M. Hanson}
}
@inproceedings {ajdendekjsijbersdvandyck1998,
	title = {Optimizing the design of an HREM experiment so as to attain the highest resolution},
	booktitle = {Proceedings of FEMMS98: Frontiers of Electron Microscopy in Material Science},
	year = {1998},
	month = {April},
	address = {Kloster Irsee, Germany},
	author = {Arnold Jan den Dekker and Jan Sijbers and Dirk Van Dyck}
}
@inproceedings {ajdendekjsijbersdvandyck1998,
	title = {Optimizing the setting of an electron microscope for highest resolution using statistical parameter estimation theory},
	booktitle = {Workshop: Towards Atomic Resolution Analysis 98},
	year = {1998},
	month = {September},
	address = {Port Ludlow, WA, U.S.A},
	author = {Arnold Jan den Dekker and Jan Sijbers and Dirk Van Dyck}
}
@inproceedings {ebettensajdendekjsijbersdvandyck1998,
	title = {Ultimate resolution in the framework of parameter estimation},
	booktitle = {IASTED International Conference - Signal and Image Processing (SIP{\textquoteright}98)},
	year = {1998},
	month = {October},
	pages = {229-233},
	address = {Las Vegas, Nevada, USA},
	author = {E. Bettens and Arnold Jan den Dekker and Jan Sijbers and Dirk Van Dyck}
}
@article {dvandyckebettensjsijbersBeeckajdendekBos1997,
	title = {From High Resolution Image to Atomic Structure: how fare are we?},
	journal = {Scanning Microscopy, Special Issue on Image Processing},
	volume = {11},
	year = {1997},
	note = {ISSN: 0891-7035},
	pages = {467-478},
	author = {Dirk Van Dyck and E. Bettens and Jan Sijbers and M. Op de Beeck and Arnold Jan den Dekker and A. van den Bos}
}
@article {denDekker:97,
	title = {Resolution: a survey},
	journal = {J. Opt. Soc. Am. A},
	volume = {14},
	number = {3},
	year = {1997},
	month = {Mar},
	pages = {547{\textendash}557},
	publisher = {OSA},
	abstract = {Past and present approaches to the concept of optical resolution are reviewed.},
	doi = {10.1364/JOSAA.14.000547},
	url = {http://josaa.osa.org/abstract.cfm?URI=josaa-14-3-547},
	author = {Arnold Jan den Dekker and A. van den Bos}
}
@inbook {dvandyckebettensjsijbersajdendekBosBeeckJansenZandbergen1997,
	title = {Resolving atoms: what do we have? what do we want?},
	booktitle = {Institute of Physics Conference Series},
	volume = {153},
	number = {3},
	year = {1997},
	month = {December},
	pages = {95-100},
	publisher = {Institute of Physics Ltd},
	organization = {Institute of Physics Ltd},
	address = {Cambridge, UK},
	author = {Dirk Van Dyck and E. Bettens and Jan Sijbers and Arnold Jan den Dekker and A. van den Bos and M. Op de Beeck and J. Jansen and H. Zandbergen},
	editor = {J. M. Rodenburg}
}
@inproceedings {jsijbersajdendekpscheunderamandvandyck1997,
	title = {Unbiased signal estimation in magnitude MR images},
	booktitle = {Proceedings of the European Society for Magnetic Resonance in Medicine and Biology},
	volume = {2},
	number = {2},
	year = {1997},
	month = {September},
	pages = {174},
	address = {Brussels, Belgium},
	author = {Jan Sijbers and Arnold Jan den Dekker and Paul Scheunders and Erik Raman and Dirk Van Dyck}
}
