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

2015

T. Roine, Jeurissen, B., Perrone, D., Aelterman, J., Philips, W., Leemans, A., and Sijbers, J., “Informed constrained spherical deconvolution (iCSD)”, Medical Image Analysis, vol. 24, no. 1, pp. 269–281, 2015.PDF icon Download accepted manuscript (987.95 KB)
U. Roine, Salmi, J., Roine, T., Nieminen-von Wendt, T., Leppämäki, S., Rintahaka, P., Tani, P., Leemans, A., and Sams, M., “Constrained spherical deconvolution-based tractography and tract-based spatial statistics show abnormal microstructural organization in Asperger syndrome”, Molecular Autism, vol. 6, p. 4, 2015.PDF icon Download paper (1.75 MB)
Q. Collier, Veraart, J., Jeurissen, B., den Dekker, A. J., and Sijbers, J., “Iterative Reweighted Linear Least Squares for Accurate, Fast, and Robust Estimation of Diffusion Magnetic Resonance Parameters”, Magnetic Resonance in Medicine, vol. 73, no. 6, pp. 2174–2184, 2015.

2014

B. Jeurissen, Tournier, J. - D., Dhollander, T., Connelly, A., and Sijbers, J., “Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data”, NeuroImage, vol. 103, pp. 411–426, 2014.
B. Jeurissen, Leemans, A., and Sijbers, J., “Automated correction of improperly rotated diffusion gradient orientations in diffusion-weighted MRI”, Medical Image Analysis, vol. 18, pp. 953-962, 2014.
A. J. den Dekker and Sijbers, J., “Data distributions in magnetic resonance images: a review”, Physica Medica, vol. 30, no. 7, pp. 725–741, 2014.PDF icon Download paper (410.33 KB)PDF icon Download paper (protected) (505.58 KB)
T. Roine, Jeurissen, B., Perrone, D., Aelterman, J., Leemans, A., Philips, W., and Sijbers, J., “Isotropic non-white matter partial volume effects in constrained spherical deconvolution”, Frontiers in Neuroinformatics, vol. 8, pp. 1-9, 2014.PDF icon Download paper (1.79 MB)
J. Rajan, den Dekker, A. J., and Sijbers, J., “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.

2013

T. De Bondt, Jacquemyn, Y., Van Hecke, W., Sijbers, J., Sunaert, S., and Parizel, P. M., “Regional gray matter volume differences and sex-hormone correlations as a function of menstrual cycle phase and hormonal contraceptives use.”, Brain research, vol. 1530, pp. 22-31, 2013.
J. Veraart, “Optimal estimation of diffusion MRI parameters”, 2013.PDF icon Download thesis (15.42 MB)

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