@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}
}
@article {2297,
	title = {dtiRIM: A generalisable deep learning method for diffusion tensor imaging},
	journal = {Neuroimage},
	volume = {269},
	year = {2023},
	chapter = {119900},
	doi = {10.1016/j.neuroimage.2023.119900},
	author = {Emanoel Ribeiro Sabidussi and Stefan Klein and Ben Jeurissen and Dirk H J Poot}
}
@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 {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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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 {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}
}
@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}
}
@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}
}
@mastersthesis {1713,
	title = {Super-resolution estimation of quantitative MRI parameters},
	volume = {Doctor of Science},
	year = {2016},
	school = {University of Antwerp},
	type = {PhD thesis},
	author = {Gwendolyn Van Steenkiste}
}
@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 {Van_Steenkiste2015-va,
	title = {Combination of super-resolution reconstruction diffusion tensor imaging and track density imaging reveals song control system connectivity in zebra finches},
	year = {2015},
	pages = {2861},
	author = {Gwendolyn Van Steenkiste and Hamaide, Julie and Ben Jeurissen and Dirk H J Poot and Johan Van Audekerke and Jan Sijbers and Verhoye, Marleen}
}
@conference {1598,
	title = {Combination of super-resolution reconstruction diffusion tensor imaging and track density imaging reveals song control system connectivity in zebra finches},
	volume = {23},
	year = {2015},
	pages = {2861},
	author = {Gwendolyn Van Steenkiste and Hamaide, Julie and Ben Jeurissen and Dirk H J Poot and Johan Van Audekerke and Jan Sijbers and Marleen Verhoye}
}
@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}
}
@conference {1520,
	title = {Super-resolution structural connectivity and anatomy of the zebra finch brain },
	year = {2015},
	author = {Gwendolyn Van Steenkiste and Hamaide, Julie and Ben Jeurissen and Dirk H J Poot and Johan Van Audekerke and Jan Sijbers and Marleen Verhoye}
}
@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 {1483,
	title = {Super-resolution reconstruction of diffusion parameters from multi-oriented diffusion weighted images},
	year = {2014},
	month = {May},
	address = {Milan, Italy},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Paul M Parizel and Dirk H J Poot and Jan Sijbers}
}
@conference {1482,
	title = {Super-resolution reconstruction of diffusion parameters from multi-oriented diffusion weighted images},
	year = {2014},
	month = {january},
	address = {Maastricht, The Netherlands},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Dirk H J Poot and Paul M Parizel and Jan Sijbers}
}
@conference {1384,
	title = {Super resolution reconstruction from  differently oriented diffusion tensor data sets},
	year = {2013},
	month = {January},
	address = {Rotterdam, The Netherlands},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Jan Sijbers and Dirk H J Poot}
}
@conference {1385,
	title = {Super resolution reconstruction from differently oriented diffusion tensor data sets},
	year = {2013},
	month = {April},
	pages = {3186},
	address = {Salt Lake City, USA},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Jan Sijbers and Dirk H J Poot}
}
@conference {1425,
	title = {Super resolution reconstruction of diffusion tensor parameters from multi-oriented diffusion weighted images},
	year = {2013},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Dirk H J Poot and Jan Sijbers}
}
@article {1328,
	title = {Super-Resolution for Multislice Diffusion Tensor Imaging},
	journal = {Magnetic Resonance in Medicine},
	volume = {69},
	year = {2013},
	pages = {103{\textendash}113},
	abstract = {Diffusion weighted (DW) magnetic resonance images are often recorded with single shot multislice
imaging sequences, because of their short scanning times and robustness to motion. To minimize
noise and acquisition time, images are generally acquired with either anisotropic or isotropic
low resolution voxels, which impedes subsequent posterior image processing and visualization. In
this paper, we propose a super-resolution method for diffusion weighted imaging that combines
anisotropic multislice images to enhance the spatial resolution of diffusion tensor (DT) data. Each
DW image is reconstructed from a set of arbitrarily oriented images with a low through-plane
resolution. The quality of the reconstructed DW images was evaluated by DT metrics and tractography.
Experiments with simulated data, a hardware DTI phantom, as well as in vivo human
brain data were conducted. Our results show a significant increase in spatial resolution of the DT
data while preserving high signal to noise ratio.},
	doi = {10.1002/mrm.24233},
	author = {Dirk H J Poot and Ben Jeurissen and Yannick Bastiaensen and Jelle Veraart and Wim Van Hecke and Paul M Parizel and Jan Sijbers}
}
@article {1309,
	title = {Microstructural changes observed with DKI in a transgenic Huntington rat model: Evidence for abnormal neurodevelopment.},
	journal = {NeuroImage},
	volume = {59},
	number = {2},
	year = {2012},
	month = {2012 Jan 16},
	pages = {957-67},
	abstract = {Huntington Disease (HD) is a fatal neurodegenerative disorder, caused by a mutation in the Huntington gene. Although HD is most often diagnosed in mid-life, the key to its clinical expression may be found during brain maturation. In the present work, we performed in vivo diffusion kurtosis imaging (DKI) in order to study brain microstructure alterations in developing transgenic HD rat pups. Several developing brain regions, relevant for HD pathology (caudate putamen, cortex, corpus callosum, external capsule and anterior commissure anterior), were examined at postnatal days 15 (P15) and 30 (P30), and DKI results were validated with histology. At P15, we observed higher mean (MD) and radial (RD) diffusivity values in the cortex of transgenic HD rat pups. In addition, at the age of P30, lower axial kurtosis (AK) values in the caudate putamen of transgenic HD pups were found. At the level of the external capsule, higher MD values at P15 but lower MD and AD values at P30 were detected. The observed DKI results have been confirmed by myelin basic protein immunohistochemistry, which revealed a reduced fiber staining as well as less ordered fibers in transgenic HD rat pups. These results indicate that neuronal development in young transgenic HD rat pups occurs differently compared to controls and that the presence of mutant huntingtin has an influence on postnatal brain development. In this context, various diffusivity parameters estimated by the DKI model are a powerful tool to assess changes in tissue microstructure and detect developmental changes in young transgenic HD rat pups.},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2011.08.062},
	author = {Ines Blockx and G. De Groof and Marleen Verhoye and Johan Van Audekerke and Kerstin Raber and Dirk H J Poot and Jan Sijbers and Osmand, Alexander P and Von H{\"o}rsten, Stephan and Annemie Van Der Linden}
}
@conference {jveraartwvheckedpootjsijbers2011,
	title = {Constrained maximum likelihood estimator for more accurate diffusion kurtosis tensor estimates},
	year = {2011},
	month = {January},
	address = {Hoeven, The Netherlands},
	author = {Jelle Veraart and Wim Van Hecke and Dirk H J Poot and Jan Sijbers}
}
@article {R.AdriaanKimmverhoyedpootJoukeJ.HjsijbersOavdlinde2011,
	title = {Magnetic resonance imaging and spectroscopy reveal differential hippocampal changes in anhedonic and resilient subtypes of the chronic mild stress rat model},
	journal = {Biological psychiatry},
	volume = {70},
	number = {5},
	year = {2011},
	pages = {449-457},
	author = {Rafael  Delgado Y Palacios and Campo Adriaan and Henningsen Kim and Marleen Verhoye and Dirk H J Poot and Dijkstra Jouke and Johan Van Audekerke and H Benveniste and Jan Sijbers and O. Wiborg and Annemie Van Der Linden}
}
@article {jveraartdpootwvheckeBlockxavdlindemverhoyejsijbers2011,
	title = {More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging},
	journal = {Magnetic Resonance in Medicine},
	volume = {65},
	number = {1},
	year = {2011},
	month = {January},
	pages = {138-145},
	doi = {10.1002/mrm.22603},
	author = {Jelle Veraart and Dirk H J Poot and Wim Van Hecke and Ines Blockx and Annemie Van Der Linden and Marleen Verhoye and Jan Sijbers}
}
@inproceedings {Kudzinava_2011_ISBI,
	title = {Optimized Workflow for Diffusion Kurtosis Imaging of Newborns},
	booktitle = {ISBI, 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro},
	year = {2011},
	month = {April},
	pages = {922-926},
	address = {Chicago, USA},
	abstract = {Diffusional kurtosis imaging (DKI) is a recently proposed extension of the conventional DTI model. It has been shown to offer more sensitive characterization of neural tissues than DTI. So far, DKI has only been applied to adult human and small animal studies, but not yet to human newborns. In this work, we present an optimized workflow for the acquisition and processing of DKI images of newborns. First, optimal set of diffusion weighting gradients for DKI studies of newborn subjects is proposed. Optimized gradients allow to estimate DKI parameters with the highest precision. Next, preprocessing and segmentation of the DKI data is considered, including motion correction, eddy currents suppression, intensity modulation and gradients reorientation. Finally, statistics of estimated diffusion and kurtosis parameters for different neonatal brain tissues are calculated.},
	keywords = {diffusion and kurtosis parameters, DKI, motion  correction, newborns, optimal gradients, precision},
	doi = {10.1109/ISBI.2011.5872554},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=5872554},
	author = {Maryna Kudzinava and Dirk H J Poot and A. Plaisier and Jan Sijbers}
}
@inproceedings {1319,
	title = {General and Efficient Super-Resolution method for Multi-Slice MRI},
	booktitle = {Medical Image Computing and Computer Assisted Intervention},
	volume = {13},
	year = {2010},
	pages = {615-622},
	doi = {10.1007/978-3-642-15705-9_75},
	author = {Dirk H J Poot and V. Van Meir and Jan Sijbers}
}
@article {dpootwpintjenmverhoyeavdlindejsijbers2010,
	title = {Improved B0 field map estimation for high field EPI},
	journal = {Magnetic Resonance Imaging},
	volume = {28},
	number = {3},
	year = {2010},
	month = {April},
	pages = {441-450},
	author = {Dirk H J Poot and W. Pintjens and Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers}
}
@conference {jveraartwvheckedpootBlockxavdlindemverhoyejsijbers2010,
	title = {A more accurate and b-value independent estimation of diffusion parameters using Diffusion Kurtosis Imaging,},
	year = {2010},
	month = {May},
	address = {Stockholm, Sweden},
	author = {Jelle Veraart and Wim Van Hecke and Dirk H J Poot and Ines Blockx and Annemie Van Der Linden and Marleen Verhoye and Jan Sijbers}
}
@conference {jveraartwvheckedpootBlockxavdlindemverhoyejsijbers2010,
	title = {A more accurate and b-value independent estimation of diffusion parameters using Diffusion Kurtosis Imaging},
	year = {2010},
	month = {January},
	pages = {10},
	address = {Utrecht, the Netherlands},
	author = {Jelle Veraart and Wim Van Hecke and Dirk H J Poot and Ines Blockx and Annemie Van Der Linden and Marleen Verhoye and Jan Sijbers}
}
@article {1189,
	title = {Noise measurement from magnitude MRI using local estimates of variance and skewness.},
	journal = {Physics in medicine and biology},
	volume = {55},
	year = {2010},
	month = {2010 Aug 21},
	pages = {N441-9},
	abstract = {In this note, we address the estimation of the noise level in magnitude magnetic resonance (MR) images in the absence of background data. Most of the methods proposed earlier exploit the Rayleigh distributed background region in MR images to estimate the noise level. These methods, however, cannot be used for images where no background information is available. In this note, we propose two different approaches for noise level estimation in the absence of the image background. The first method is based on the local estimation of the noise variance using maximum likelihood estimation and the second method is based on the local estimation of the skewness of the magnitude data distribution. Experimental results on synthetic and real MR image datasets show that the proposed estimators accurately estimate the noise level in a magnitude MR image, even without background data.},
	keywords = {Algorithms, Artifacts, Brain, Data Interpretation, Statistical, Fourier Analysis, Humans, Image Processing, Computer-Assisted, Likelihood Functions, Magnetic Resonance Imaging, Models, Statistical, Myocardium, Normal Distribution, Reproducibility of Results},
	issn = {1361-6560},
	doi = {10.1088/0031-9155/55/16/N02},
	author = {Jeny Rajan and Dirk H J Poot 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}
}
@inproceedings {DBLP:conf/iciar/RajanPJS10,
	title = {Segmentation Based Noise Variance Estimation from Background MRI Data},
	booktitle = {ICIAR },
	volume = {6111},
	year = {2010},
	month = {2010},
	pages = {62-70},
	publisher = {Springer LNCS},
	organization = {Springer LNCS},
	address = {Porto, Portugal},
	author = {Jeny Rajan and Dirk H J Poot and Juntu, Jaber 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 {wvheckejsijberssdbackerdpootParizelaleemans2009,
	title = {On the construction of a ground truth framework for evaluating voxel-based diffusion tensor MRI analysis methods},
	journal = {NeuroImage},
	volume = {46},
	number = {3},
	year = {2009},
	month = {July},
	pages = {692-707},
	doi = {10.1016/j.neuroimage.2008.07.006},
	author = {Wim Van Hecke and Jan Sijbers and Steve De Backer and Dirk H J Poot and Paul M Parizel and Alexander Leemans}
}
@conference {BlockxmverhoyeGroofAudekerkeRaberdpootjsijbersHorstenavdlinde2009,
	title = {Diffusion Kurtosis Imaging (DKI) reveals an early phenotype (P30) in a transgenic rat model for Huntington{\textquoteright}s disease},
	volume = {2009},
	year = {2009},
	month = {April},
	pages = {359},
	author = {Ines Blockx and Marleen Verhoye and G. De Groof and Johan Van Audekerke and Kerstin Raber and Dirk H J Poot and Jan Sijbers and S. von Horsten and Annemie Van Der Linden}
}
@conference {PalaciosmverhoyeAudekerkedpootjsijbersWiborgavdlinde2009,
	title = {DKI visualizes hippocampal alterations in the chronic mild stress ratmodel},
	volume = {2009},
	year = {2009},
	month = {April},
	pages = {744},
	author = {Rafael  Delgado Y Palacios and Marleen Verhoye and Johan Van Audekerke and Dirk H J Poot and Jan Sijbers and O. Wiborg and Annemie Van Der Linden}
}
@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}
}
@conference {wpintjendpootmverhoyeavdlindejsijbers2008,
	title = {Improved EPI Correction: Upgrading An Ultrafast Imaging Technique},
	year = {2008},
	month = {March},
	author = {W. Pintjens and Dirk H J Poot and Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers}
}
@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}
}
@inproceedings {wpintjendpootmverhoyeavdlindejsijbers2008,
	title = {Susceptibility correction for improved tractography using high field DT-EPI},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {6914},
	year = {2008},
	month = {February},
	address = {San Diego, USA},
	author = {W. Pintjens and Dirk H J Poot and Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers}
}
@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}
}
