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.

Journal publications

2007

J. Sijbers, Poot, D. H. J., den Dekker, A. J., and Pintjens, W., “Automatic estimation of the noise variance from the histogram of a magnetic resonance image”, Physics in Medicine and Biology, vol. 52, pp. 1335-1348, 2007.PDF icon Download paper (297.18 KB)
W. Van Hecke, Leemans, A., D'Agostino, E., De Backer, S., Vandervliet, E., Parizel, P. M., and Sijbers, J., “Nonrigid Coregistration of Diffusion Tensor Images Using a Viscous Fluid Model and Mutual Information”, IEEE Transactions on Medical Imaging, vol. 26, pp. 1598-1612, 2007.PDF icon Download paper (1.85 MB)
A. Smolders, Martino, D. F., Staeren, N., Scheunders, P., Sijbers, J., Goebel, R., and Formisano, E., “Dissecting cognitive stages with time-resolved fMRI data: a comparison of fuzzy clustering and independent component analysis”, Magnetic Resonance Imaging, vol. 25, pp. 860-868, 2007.PDF icon Download paper (658.36 KB)

2006

A. Leemans, “Modeling and Processing of Diffusion Tensor Magnetic Resonance Images for Improved Analysis of Brain Connectivity”, 2006.
A. Leemans, “Modeling and Processing of Diffusion Tensor Magnetic Resonance Images for Improved Analysis of Brain Connectivity”, University of Antwerp, Antwerp, 2006.PDF icon Download thesis (6.77 MB)
A. Leemans, Sijbers, J., De Backer, S., Vandervliet, E., and Parizel, P. M., “Multiscale white matter fiber tract coregistration: a new feature-based approach to align diffusion tensor data”, Magnetic Resonance in Medicine, vol. 55, pp. 1414-1423, 2006.

2005

A. Leemans, Sijbers, J., De Backer, S., Vandervliet, E., and Parizel, P. M., “Affine Coregistration of Diffusion Tensor Magnetic Resonance Images Using Mutual Information”, Lecture Notes in Computer Science, vol. 3708, pp. 523-530, 2005.PDF icon Download full paper (1.26 MB)
A. Leemans, Sijbers, J., Verhoye, M., Van Der Linden, A., and Van Dyck, D., “Mathematical Framework for Simulating Diffusion Tensor MR Neural Fiber Bundles”, Magnetic Resonance in Medicine, vol. 53, pp. 944-953, 2005.PDF icon Download full paper (1.55 MB)
J. Sijbers, den Dekker, A. J., and Bos, R., “A likelihood ratio test for functional MRI data analysis to account for colored noise”, Lecture Notes in Computer Science, vol. 3708, pp. 538-546, 2005.PDF icon Download full paper (483.15 KB)
A. J. den Dekker and Sijbers, J., “Implications of the Rician distribution for fMRI generalized likelihood ratio tests”, Magnetic Resonance Imaging, vol. 23, pp. 953-959, 2005.PDF icon Download paper (1.36 MB)

Pages