Publications
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Author [ Type] Year Filters: Author is Arnold Jan den Dekker [Clear All Filters]
“Improved MRI Relaxometry through Statistical Signal Processing”, University of Antwerp, Antwerp, 2018. Download thesis (19.25 MB)
, , , “Joint motion correction and estimation for T1 mapping: proof of concept”, Medical Imaging Summer School 2014, Favignana, Italy. 2014.
, “Use of support vector machines approach via ComBat harmonized diffusion tensor imaging for the diagnosis and prognosis of mild traumatic brain injury: a CENTER-TBI study”, Journal of Neurotrauma, vol. 40, no. 13-14, pp. 1317-1338, 2023.
, “A unified Maximum Likelihood framework for simultaneous motion and T1 estimation in quantitative MR T1 mapping”, IEEE Transactions on Medical Imaging, vol. 36, no. 2, pp. 433 - 446, 2017.
, “Towards quantitative structure determination through electron holographic methods”, Materials Characterization, vol. 42, pp. 265-281, 1999. Download paper (6.71 MB)
, “To shift or to rotate? Comparison of acquisition strategies for multi-slice super-resolution magnetic resonance imaging”, Frontiers in Neuroscience, pp. 1-18, 2022. Download paper (5.96 MB)
, “Systematic review of reconstruction techniques for accelerated quantitative MRI”, Magnetic Resonance in Medicine, vol. 90, no. 3, pp. 1172-1208, 2023. Download paper (2.91 MB)
, “Supporting measurements or more averages? How to quantify cerebral blood flow most reliably in 5 minutes by arterial spin labeling”, Magnetic Resonance in Medicine, vol. 84, no. 5, pp. 2523-2536, 2020. Download paper (1.3 MB)
, “Super-resolution T1 estimation: quantitative high resolution T1 mapping from a set of low resolution T1 weighted images with different slice orientations”, Magnetic Resonance in Medicine, vol. 77, no. 5, pp. 1818–1830, 2017. Download paper (3.3 MB)
, “Super-resolution reconstruction of diffusion parameters from diffusion-weighted images with different slice orientations”, Magnetic Resonance in Medicine, vol. 75, no. 1, pp. 181-195, 2016.
, “Single Atom Detection from Low Contrast-to-Noise Ratio Electron Microscopy Images”, Phys. Rev. Lett., vol. 121, p. 056101, 2018.
, “Resolution: a survey”, J. Opt. Soc. Am. A, vol. 14, pp. 547–557, 1997.
, “Recurrent Inference Machines as inverse problem solvers for MR relaxometry”, Medical Image Analysis, vol. 74, pp. 1-11, 2021. Download paper (2.26 MB)
, “The reconstructed residual error: A novel segmentation evaluation measure for reconstructed images in tomography”, Computer Vision and Image Understanding, vol. 126, pp. 28-37, 2014. Download paper (4.58 MB)
, “Partial Discreteness: a Novel Prior for Magnetic Resonance Image Reconstruction”, IEEE Transactions on Medical Imaging, vol. 36, no. 5, pp. 1041 - 1053, 2017. Download paper (3.72 MB)
, “Parametric Reconstruction of Glass Fiber-reinforced Polymer Composites from X-ray Projection Data - A Simulation Study”, Journal of Nondestructive Evaluation, vol. 37, no. 62, pp. 1573-4862, 2018.
, “Parameter estimation from magnitude MR images”, International Journal of Imaging Systems and Technology, vol. 10, pp. 109-114, 1999. Download paper (350.44 KB)
, “Optimal experimental design for the detection of light atoms from high-resolution scanning transmission electron microscopy images”, Applied Physics Letters, vol. 105, no. 063116, 2014.
, “Optimal experimental design for Diffusion Kurtosis Imaging”, IEEE Transactions on Medical Imaging, vol. 29, pp. 819-829, 2010. Download paper (1.12 MB)
, “Optimal experimental design and estimation for q-space trajectory imaging”, Human Brain Mapping, vol. 44, no. 4, pp. 1793-1809, 2023. Download paper (5.87 MB)
, “NOVIFAST: A fast algorithm for accurate and precise VFA MRI T1 mapping”, IEEE Transactions on Medical Imaging, vol. 37, no. 11, pp. 2414 - 2427, 2018. Download paper (3.3 MB)
, “A nonlocal maximum likelihood estimation method for enhancing magnetic resonance phase maps”, Signal Image and Video Processing, vol. 11, no. 5, pp. 913-920, 2017.
, “A new non local maximum likelihood estimation method for Rician noise reduction in Magnetic Resonance images using the Kolmogorov-Smirnov test”, Signal Processing, vol. 103, pp. 16-23, 2014.
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