Quantitative MRI (qMRI) acquired at the ultra-high field of 7 Tesla has been used in
visualizing and analyzing subcortical structures. qMRI relies on the acquisition of multiple …

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 …

In complex geologic settings and in the presence of sparse acquisition systems, seismic
migration images manifest as nonstationary blurred versions of the unknown subsurface …

Most quantitative magnetization transfer (qMT) imaging methods require acquiring
additional quantitative maps (such as T 1) for data fitting. A method based on multiple phase …

Objective. Bloch simulation constitutes an essential part of magnetic resonance imaging
(MRI) development. However, even with the graphics processing unit (GPU) acceleration …

Multi-slice (MS) super-resolution reconstruction (SRR) methods have been proposed to
improve the trade-off between resolution, signal-to-noise ratio and scan time in magnetic …

Two optimization criteria based on Cramér-Rao Bounds are compared between each other
and with non-optimized schedules for T1ρ mapping using synthetic data, model phantoms …

Deep learning methods show great potential for the efficient and precise estimation of
quantitative parameter maps from multiple magnetic resonance (MR) images. Current deep …

Quantitative MRI (qMRI) offers significant advantages over weighted images by providing
objective parameters related to tissue properties. Deep learning-based methods have …