19 citations · 52 across the 5 of their papers we have counts for
5 papers
Time-efficient, high-resolution 3T whole-brain relaxometry using Cartesian 3D MR-STAT with CSF suppression
Hongyan Liu, Edwin Versteeg, Miha Fuderer +4
Purpose: Current 3D Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) protocols use transient-state, gradient-spoiled gradient-echo sequences that are prone to cerebrospi…
A three-dimensional MR-STAT protocol for high-resolution multi-parametric quantitative MRI
Hongyan Liu, Oscar van der Heide, Edwin Versteeg +5
Magnetic Resonance Spin Tomography in Time-Domain (MR-STAT) is a multiparametric quantitative MR framework, which allows for simultaneously acquiring quantitative tissue parameters…
Generalizable synthetic MRI with physics-informed convolutional networks
Luuk Jacobs, Stefano Mandija, Hongyan Liu +3
In this study, we develop a physics-informed deep learning-based method to synthesize multiple brain magnetic resonance imaging (MRI) contrasts from a single five-minute acquisitio…
Acceleration Strategies for MR-STAT: Achieving High-Resolution Reconstructions on a Desktop PC within 3 minutes
Hongyan Liu, Oscar van der Heide, Stefano Mandija +2
MR-STAT is an emerging quantitative magnetic resonance imaging technique which aims at obtaining multi-parametric tissue parameter maps from single short scans. It describes the re…
Fast and Accurate Modeling of Transient-State Gradient-Spoiled Sequences by Recurrent Neural Networks
Hongyan Liu, Oscar van der Heide, Cornelis A. T. van den Berg +1
Fast and accurate modeling of MR signal responses are typically required for various quantitative MRI applications, such as MR Fingerprinting and MR-STAT. This work uses a new EPG-…