Diffusion‐weighted magnetic resonance imaging (DW‐MRI) aims to disentangle multiple
biological signal sources in each imaging voxel, enabling the computation of innovative …

Purpose Supervised machine learning (ML) provides a compelling alternative to traditional
model fitting for parameter mapping in quantitative MRI. The aim of this work is to …

Abstract Digital Twins (DTs) are on the rise as innovative, powerful technologies to harness
the power of digitalisation in the WRRF sector. The lack of consensus and understanding …

Estimating intra-and extra-axonal microstructure parameters, such as volume fractions and
diffusivities, has been one of the major efforts in brain microstructure imaging with MRI. The …

Purpose To assess the robustness and repeatability of intravoxel incoherent motion model
(IVIM) parameter estimation for the diffusion‐weighted MRI in the abdominal organs under …

Diffusion-weighted magnetic resonance imaging (dMRI) of the brain offers unique
capabilities including noninvasive probing of tissue microstructure and structural …

Purpose Biophysical modeling of the diffusion MRI (dMRI) signal provides estimates of
specific microstructural tissue properties. Although non‐linear least squares (NLLS) is the …

This review offers a comprehensive examination of how stress and anxiety affect motor
behavior, particularly focusing on fine motor skills and gait adaptability. We explore the role …

Diffusion weighted MRI is an indispensable tool for routine patient screening and
diagnostics of pathology. Recently, several deep learning methods have been proposed to …

We proposed two deep neural network based methods to accelerate the estimation of
microstructural features of crossing fascicles in the white matter. Both methods focus on the …