Artificial intelligence algorithms based on principles of deep learning (DL) have made a
large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the …

Osteoarthritis (OA) of the knee is a major source of disability that has no known treatment or
cure. Morphological and compositional MRI is commonly used for assessing the bone and …

Diffusion tensor magnetic resonance imaging (DTI) is a widely adopted neuroimaging
method for the in vivo mapping of brain tissue microstructure and white matter tracts …

Diffusion tensor magnetic resonance imaging (DTI) is unsurpassed in its ability to map tissue
microstructure and structural connectivity in the living human brain. Nonetheless, the …

Neurite orientation dispersion and density imaging (NODDI) has become a popular diffusion
MRI technique for investigating microstructural alternations during brain development …

Diffusion-weighted magnetic resonance imaging (DW-MRI) of fetal brain is challenged by
frequent fetal motion and signal to noise ratio that is much lower than non-fetal imaging. As a …

Estimation of white matter fiber orientation distribution function (fODF) is the essential first
step for reliable brain tractography and connectivity analysis. Most of the existing fODF …

Deep learning prediction of diffusion MRI (DMRI) data relies on the utilization of effective
loss functions. Existing losses typically measure the signal-wise differences between the …

Purpose To develop a deep learning–based reconstruction framework for ultrafast and
robust diffusion tensor imaging and fiber tractography. Methods SuperDTI was developed to …

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