Magnetic resonance imaging (MRI) is a noninvasive diagnostic tool that provides excellent
soft-tissue contrast without the use of ionizing radiation. Compared to other clinical imaging …

Deep learning based techniques achieve state-of-the-art results in a wide range of image
reconstruction tasks like compressed sensing. These methods almost always have …

In this chapter, we introduce the basic terminology of machine learning. We outline basic
neural network layers and building blocks in both real-valued and complex-valued domains …

We propose a new, modular, open-source, Python-based 3D+ time fMRI data simulation
software,\emph {SNAKE-fMRI}, which stands for\emph {S} imulator from\emph {N} …

Regularization plays a crucial role in reliably utilizing imaging systems for scientific and
medical investigations. It helps to stabilize the process of computationally undoing any …

Compressed sensing and partial Fourier sampling are two methods for accelerating MRI
scans. Compressed sensing relies on the sparsity of the image in a separate domain to …

Abstract Magnetic Resonance Imaging (MRI) possesses the unique ability to capture a wide
range of physiological attributes with high spatial resolution. This flexibility has allowed …

This research work focuses on developing deep learning (DL) methods for the image
reconstruction problem encountered in magnetic resonance imaging (MRI). The data …

First, we address image-inverse problems. We review Plug-and-Play (PnP) algorithms,
where a proximal operator is replaced by a call of an arbitrary denoising algorithm. We apply …

Yearly, about 1 million MRI scans are acquired in the Netherlands [1]. However, a major
drawback of using MRI is the long duration of an MRI scan, leading to high costs, waiting …