81 citations · 204 across the 34 of their papers we have counts for
5 papers · 1 filter
Neural Implicit k-Space for Binning-free Non-Cartesian Cardiac MR Imaging
Wenqi Huang, Hongwei Li, Jiazhen Pan +3
In this work, we propose a novel image reconstruction framework that directly learns a neural implicit representation in k-space for ECG-triggered non-Cartesian Cardiac Magnetic Re…
Learning-based and unrolled motion-compensated reconstruction for cardiac MR CINE imaging
Jiazhen Pan, Daniel Rueckert, Thomas Küstner +1
Motion-compensated MR reconstruction (MCMR) is a powerful concept with considerable potential, consisting of two coupled sub-problems: Motion estimation, assuming a known image, an…
A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis
Inês P. Machado, Esther Puyol-Antón, Kerstin Hammernik +10
Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation. However, cine CMR acquisition is inherently slow and in recent decade…
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging
Kerstin Hammernik, Thomas Küstner, Burhaneddin Yaman +4
Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new limits.…
Differentially private training of residual networks with scale normalisation
Helena Klause, Alexander Ziller, Daniel Rueckert +2
The training of neural networks with Differentially Private Stochastic Gradient Descent offers formal Differential Privacy guarantees but introduces accuracy trade-offs. In this wo…