Car-following behavior has been extensively studied using physics-based models, such as
Intelligent Driving Model (IDM). These models successfully interpret traffic phenomena …

Quantitative magnetic resonance imaging (qMRI) has been shown to provide many potential
advantages for personalized adaptive radiotherapy (RT). Deep learning models have …

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 …

For its robust predictive power (compared to pure physics-based models) and sample-
efficient training (compared to pure deep learning models), physics-informed deep learning …

Purpose Low magnetic field systems provide an important opportunity to expand MRI to new
and diverse clinical and research study populations. However, a fundamental limitation of …

At the typical spatial resolution of MRI in the human brain, approximately 60–90% of voxels
contain multiple fiber populations. Quantifying microstructural properties of distinct fiber …

Despite diffusion tensor imaging (DTI) evidence for widespread fractional anisotropy (FA)
reductions in the brain white matter of patients with bipolar disorder, questions remain …

Multi-component T 2 relaxometry allows probing tissue microstructure by assessing
compartment-specific T 2 relaxation times and water fractions, including the myelin water …

In spite of extensive work, inconsistent findings and lack of specificity in most neuroimaging
techniques used to examine age-and gender-related patterns in brain tissue microstructure …

Deep learning (DL) is gaining popularity as a parameter estimation method for quantitative
MRI. A range of competing implementations have been proposed, relying on either …