Magnetic Resonance Imaging
Magnetic resonance imaging (MRI) is a biomedical imaging technique used to visualize detailed internal structures. The Quantitative MRI group of the Vision Lab develops novel reconstruction, processing and analysis algorithms to process anatomical, functional or diffusion-weighted MRI data. These methods rely on profound knowledge of the MR imaging principles. The core competence of the group is quantitative, statistical parameter estimation, which is the basis for developing novel techniques for image reconstruction, image denoising, higher order diffusion parameter estimation (DTI, DKI, ...), and fiber tractography.
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Journal publications
2018
“Robust estimation of diffusion tensor and diffusion kurtosis imaging parameters”, University of Antwerp, Antwerp, 2018. ,
“Brain Tissue–Volume Changes in Cosmonauts”, New England Journal of Medicine, vol. 379, no. 17, pp. 1678 - 1680, 2018. ,
“A three-dimensional digital neurological atlas of the mustached bat (Pteronotus parnellii)”, NeuroImage, vol. 183, pp. 300-313, 2018. ,
“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.
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“Improved MRI Relaxometry through Statistical Signal Processing”, University of Antwerp, Antwerp, 2018.
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“White matter microstructural organization of interhemispheric pathways predicts different stages of bimanual coordination learning in young and older adults”, European Journal of Neuroscience , vol. 47, no. 5, pp. 446–459, 2018. ,
“Diffusion kurtosis imaging with free water elimination: a Bayesian estimation approach”, Magnetic Resonance in Medicine, vol. 80, no. 2, pp. 802-813, 2018.
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“Diffusion kurtosis imaging allows the early detection and longitudinal follow-up of amyloid β-induced pathology.”, Alzheimer's Research & Therapy , vol. 10, no. 1, pp. 1-16, 2018. ,
“The role of whole-brain diffusion MRI as a tool for studying human in vivo cortical segregation based on a measure of neurite density”, Magnetic Resonance in Medicine, vol. 79, no. 5, pp. 2738–2744, 2018. ,