Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical
diagnoses and research which underpin many recent breakthroughs in medicine and …

The use of MRI for prostate cancer diagnosis and treatment is increasing rapidly. However,
identifying the presence and extent of cancer on MRI remains challenging, leading to high …

Exploiting 4D-flow magnetic resonance imaging (MRI) data to quantify hemodynamics
requires an adequate spatio-temporal vector field resolution at a low noise level. To address …

Convolutional networks are successful, but they have recently been outperformed by new
neural networks that are equivariant under rotations and translations. These new networks …

Abstract Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) explores the random
motion of diffusing water molecules in biological tissue and can provide information on the …

Diffusion-weighted magnetic resonance imaging (dMRI) permits a detailed in-vivo analysis
of neuroanatomical microstructure, invaluable for clinical and population studies. However …

Diffusion MRI (dMRI), while powerful for characterization of tissue microstructure, suffers
from long acquisition time. In this paper, we present a method for effective diffusion MRI …

Diffusion-weighted MRI is nowadays performed routinely due to its prognostic ability, yet the
quality of the scans are often unsatisfactory which can subsequently hamper the clinical …

Diffusion magnetic resonance imaging (dMRI) often suffers from low spatial and angular
resolution due to inherent limitations in imaging hardware and system noise, adversely …

Deep learning-based super-resolution (SR) has shown great potential in improving the
resolution of diffusion-weighted imaging (DWI), which is useful in clinical diagnosis and …