8 citations · 20 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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.