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

2025

C. Smekens, Beirinckx, Q., Bosmans, F., Vanhevel, F., Snoeckx, A., Sijbers, J., Jeurissen, B., Janssens, T., and Van Dyck, P., “Deep Learning-Enhanced Accelerated 2D TSE and 3D Super-Resolution Dixon TSE for Rapid Comprehensive Knee Joint Assessment”, Investigative Radiology, vol. 60, no. 3, pp. 220-233, 2025.

2024

M. A. Zampini, “Development of spoiled gradient echo sequences for MRI relaxometry”, 2024.PDF icon Download PhD thesis (17.25 MB)
D. Giraldo, Khan, H., Pineda, G., Liang, Z., Lozano, A., Van Mijweersch, B., Woodruff, H. C., Lambin, P., Romero, E., Peeters, L. M., and Sijbers, J., “Perceptual super-resolution in multiple sclerosis MRI”, Frontiers in Neuroscience, vol. 18, 2024.
Q. Beirinckx, “Technological breakthroughs towards motion-robust super-resolution quantitative magnetic resonance imaging for improved detection of brain diseases”, University of Antwerp, 2024.PDF icon Download PhD Thesis (18.7 MB)
Q. Beirinckx, Bladt, P., van der Plas, M. C. E., van Osch, M. J. P., Jeurissen, B., den Dekker, A. J., and Sijbers, J., “Model-based super-resolution reconstruction for pseudo-continuous Arterial Spin Labeling”, NeuroImage, vol. 286, p. 120506, 2024.PDF icon Download paper (7.23 MB)PDF icon Download supplementary material (20.75 MB)
M. A. Zampini, Sijbers, J., Verhoye, M., and Garipov, R., “A preparation pulse for fast steady state approach in Actual Flip angle Imaging”, Medical Physics, vol. 51, no. 1, pp. 306-318, 2024.

2023

J. Renders, Jeurissen, B., Nguyen, A. - T., De Beenhouwer, J., and Sijbers, J., “ImWIP: open-source image warping toolbox with adjoints and derivatives”, SoftwareX, vol. 24, p. 101524, 2023.PDF icon Download paper (1 MB)
E. Ribeiro Sabidussi, Klein, S., Jeurissen, B., and Poot, D. H. J., “dtiRIM: A generalisable deep learning method for diffusion tensor imaging”, Neuroimage, vol. 269, 2023.PDF icon Download paper (5.11 MB)
B. Shafieizargar, Byanju, R., Sijbers, J., Klein, S., den Dekker, A. J., and Poot, D. H. J., “Systematic review of reconstruction techniques for accelerated quantitative MRI”, Magnetic Resonance in Medicine, vol. 90, no. 3, pp. 1172-1208, 2023.PDF icon Download paper (2.91 MB)
M. Siqueira Pinto, Winzeck, S., Kornaropoulos, E. N., Richter, S., Paolella, R., Correia, M. M., Glocker, B., Williams, G., Vik, A., Posti, J., Håberg, A. Kristine, Stenberg, J., Guns, P. - J., den Dekker, A. J., Menon, D. K., Sijbers, J., Van Dyck, P., and Newcombe, V. F. J., “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.

Pages