156 citations · 826 across the 30 of their papers we have counts for
30 papers
Rapid, High-resolution and Distortion-free Mapping of Fetal Brain using Multi-echo Radial FLASH and Model-based Reconstruction
Xiaoqing Wang, Hongli Fan, Zhengguo Tan +7
Purpose: To develop a rapid, high-resolution and distortion-free technique for simultaneous water-fat separation, and mapping of the fetal brain at 3T. Methods:…
Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRI
Guanxiong Luo, Shoujin Huang, Martin Uecker
Magnetic resonance imaging (MRI) is a widely used non-invasive imaging modality. However, a persistent challenge lies in balancing image quality with imaging speed. This trade-off…
Model-Based Reconstruction for Joint Estimation of , and Field Maps Using Single-Shot Inversion-Recovery Multi-Echo Radial FLASH
Xiaoqing Wang, Nick Scholand, Zhengguo Tan +5
Purpose: To develop a model-based nonlinear reconstruction for simultaneous water-specific , , field and/or fat fraction (FF) mapping using single-shot in…
Self-Supervised Learning for Improved Calibrationless Radial MRI with NLINV-Net
Moritz Blumenthal, Chiara Fantinato, Christina Unterberg-Buchwald +3
Purpose: To develop a neural network architecture for improved calibrationless reconstruction of radial data when no ground truth is available for training. Methods: NLINV-Net is a…
Rational Approximation of Golden Angles: Accelerated Reconstructions for Radial MRI
Nick Scholand, Philip Schaten, Christina Graf +6
Purpose: To develop a generic radial sampling scheme that combines the advantages of golden ratio sampling with simplicity of equidistant angular patterns. The irrational angle bet…
Assessment of Deep Learning Segmentation for Real-Time Free-Breathing Cardiac Magnetic Resonance Imaging at Rest and Under Exercise Stress
Martin Schilling, Christina Unterberg-Buchwald, Joachim Lotz +1
In recent years, a variety of deep learning networks for cardiac MRI (CMR) segmentation have been developed and analyzed. However, nearly all of them are focused on cine CMR under…