8 citations · 20 across the 9 of their papers we have counts for
10 papers
Region-based motion-compensated iterative reconstruction technique for dynamic computed tomography
Anh-Tuan Nguyen, Jens Renders, Jan Sijbers +1
Current state-of-the-art motion-based dynamic computed tomography reconstruction techniques estimate the deformation by considering motion models in the entire object volume althou…
DELTA-MRI: Direct deformation Estimation from LongiTudinally Acquired k-space data
Jens Renders, Banafshe Shafieizargar, Marleen Verhoye +3
Longitudinal MRI is an important diagnostic imaging tool for evaluating the effects of treatment and monitoring disease progression. However, MRI, and particularly longitudinal MRI…
Virtual grating approach for Monte Carlo simulations of edge illumination-based x-ray phase contrast imaging
Jonathan Sanctorum, Jan Sijbers, Jan De Beenhouwer
The design of new x-ray phase contrast imaging setups often relies on Monte Carlo simulations for prospective parameter studies. Monte Carlo simulations are known to be accurate bu…
Recurrent Inference Machines as inverse problem solvers for MR relaxometry
E. R. Sabidussi, S. Klein, M. W. A. Caan +5
In this paper, we propose the use of Recurrent Inference Machines (RIMs) to perform T1 and T2 mapping. The RIM is a neural network framework that learns an iterative inference proc…
To Recurse or not to Recurse,a Low Dose CT Study
Shabab Bazrafkan, Vincent Van Nieuwenhove, Jan Sijbers
Restoring high-quality CT images from low dose CT counterparts is an ill-posed, nonlinear problem to which Deep Learning approaches have been giving superior solutions compared to…
Deep Neural Network Assisted Iterative Reconstruction Method for Low Dose CT
Shabab Bazrafkan, Vincent Van Nieuwenhove, Joris Soons +2
Low Dose Computed Tomography suffers from a high amount of noise and/or undersampling artefacts in the reconstructed image. In the current article, a Deep Learning technique is exp…
Deep Learning Based Computed Tomography Whys and Wherefores
Shabab Bazrafkan, Vincent Van Nieuwenhove, Joris Soons +2
This is an article about the Computed Tomography (CT) and how Deep Learning influences CT reconstruction pipeline, especially in low dose scenarios.
A Generalized Bidiagonal-Tikhonov Method Applied To Differential Phase Contrast Tomography
Nick Schenkels, Jan Sijbers, Wim van Aarle +1
Phase contrast tomography is an alternative to classic absorption contrast tomography that leads to higher contrast reconstructions in many applications. We review how phase contra…
On parameter estimation in the physics lab based on inverting a slope regression coefficient
W. Jacquet, E. Ir. Nyssen, J. Sijbers
Measurement uncertainty is a non trivial aspect of the laboratory component of most undergraduate physics courses. Confusion about the application of statistical tools calls for th…
Imprecise k-space sampling and central brightening
R. A. Hanel, S. De Backer, J. Sijbers +1
In real-world sampling of k-space data, one generally makes a stochastic error not only in the value of the sample but in the effective position of the drawn sample. We refer to th…