The discovery of the theory of compressed sensing brought the realisation that many inverse
problems can be solved even when measurements are “incomplete”. This is particularly …

We propose a surrogate function for efficient use of score-based priors for Bayesian inverse
imaging. Recent work turned score-based diffusion models into probabilistic priors for …

Variational regularization techniques are dominant in the field of mathematical imaging. A
drawback of these techniques is that they are dependent on a number of parameters which …

We propose a surrogate function for efficient yet principled use of score-based priors in
Bayesian imaging. We consider ill-posed inverse imaging problems in which one aims for a …

We present a framework to significantly reduce the acquisition time of diffusion-weighted
magnetic resonance (DW-MR) data. The proposed approach is based on multi-task …

Typically, magnetic resonance (MR) images are stored in k-space where the higher energy
samples, ie, the samples with maximum information are concentrated near the center only; …

Magnetic resonance imaging (MRI) is a widely used medical imaging tool where data
acquisition is performed in the k-space, ie, the Fourier transform domain. However, it has a …

This thesis investigated the potential of Compressed Sensing (CS) applied to Magnetic
Resonance Imaging (MRI). CS is a novel image reconstruction method that emerged from …

Tomografie is het reconstrueren van het inwendige van een object adhv externe metingen,
bv beelden verkregen met X-stralen of microgolven. Deze thesis bekijkt de specifieke …