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
2012
, “Improved analysis of brain connectivity using high angular resolution diffusion MRI”, University of Antwerp, 2012.
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, “The influence of complex white matter architecture on the mean diffusivity in diffusion tensor MRI of the human brain”, NeuroImage, vol. 59, no. 3, pp. 2208 - 2216, 2012.
, “Microstructural changes observed with DKI in a transgenic Huntington rat model: Evidence for abnormal neurodevelopment.”, NeuroImage, vol. 59, pp. 957-67, 2012.
, “Diffusion kurtosis imaging in the grading of gliomas”, Radiology, vol. 2, pp. 492-501, 2012.
, “A complementary DTI-histological study in a model of Huntingtons disease”, Neurobiology of Aging, vol. 33, pp. 945-959, 2012.
2011
, “Robust edge-directed interpolation of magnetic resonance images”, Physics in medicine and biology, vol. 56, pp. 7287-7303, 2011.
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, “Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution”, Human Brain Mapping, vol. 32, no. 3, pp. 461 - 479, 2011.
, “The effect of template selection on diffusion tensor voxel based analysis results”, NeuroImage, vol. 55, pp. 566-573, 2011.
, “Maximum likelihood estimation based denoising of magnetic resonance images using restricted local neighborhoods”, Physics in Medicine and Biology, vol. 56, pp. 5221-5234, 2011.
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, “Population-averaged diffusion tensor imaging atlas of the Sprague Dawley rat brain”, NeuroImage, vol. 58, no. 4, pp. 975-983, 2011.

