Purpose To systematically review the techniques that address undersampling artifacts in
accelerated quantitative magnetic resonance imaging (qMRI). Methods A literature search …

Three-dimensional MRI has gained increasing popularity in various clinical applications due
to its improved through-plane spatial resolution, which enhances the detection of subtle …

Diffusion magnetic resonance imaging (dMRI) tractography has yielded intriguing insights
into brain circuits and their relationship to behavior in response to gene mutations or …

Background The use of rigid multi-exponential models (with a priori predefined numbers of
components) is common practice for diffusion-weighted MRI (DWI) analysis of the kidney …

Mesoscale diffusion magnetic resonance imaging (MRI) endeavors to bridge the gap
between macroscopic white matter tractography and microscopic studies investigating the …

This work presents a deep-learning-based denoising technique to accelerate the acquisition
of high b-value diffusion-weighted MRI for rectal cancer. A denoising convolutional neural …

Compressed sensing (CS) is widely used to accelerate clinical diffusion MRI acquisitions,
but it is not widely used in preclinical settings yet. In this study, we optimized and compared …

The role of hippocampal connectivity in mesial temporal lobe epilepsy (mTLE) remains
poorly understood. The use of ex vivo hippocampal samples excised from patients with …

In this paper, we present a robust reconstruction scheme for diffusion MRI (dMRI) data
acquired using slice-interleaved diffusion encoding (SIDE). When combined with SIDE …

Deep learning has emerged as a promising approach for learning the nonlinear mapping
between diffusion-weighted MR images and tissue parameters, which enables automatic …