18 citations · 24 across the 6 of their papers we have counts for
6 papers
Attention-aware non-rigid image registration for accelerated MR imaging
Aya Ghoul, Jiazhen Pan, Andreas Lingg +6
Accurate motion estimation at high acceleration factors enables rapid motion-compensated reconstruction in Magnetic Resonance Imaging (MRI) without compromising the diagnostic imag…
Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation
Veronika Spieker, Hannah Eichhorn, Jonathan K. Stelter +8
Neural implicit k-space representations have shown promising results for dynamic MRI at high temporal resolutions. Yet, their exclusive training in k-space limits the application o…
Propagation and Attribution of Uncertainty in Medical Imaging Pipelines
Leonhard F. Feiner, Martin J. Menten, Kerstin Hammernik +5
Uncertainty estimation, which provides a means of building explainable neural networks for medical imaging applications, have mostly been studied for single deep learning models th…
NISF: Neural Implicit Segmentation Functions
Nil Stolt-Ansó, Julian McGinnis, Jiazhen Pan +2
Segmentation of anatomical shapes from medical images has taken an important role in the automation of clinical measurements. While typical deep-learning segmentation approaches ar…
ICoNIK: Generating Respiratory-Resolved Abdominal MR Reconstructions Using Neural Implicit Representations in k-Space
Veronika Spieker, Wenqi Huang, Hannah Eichhorn +7
Motion-resolved reconstruction for abdominal magnetic resonance imaging (MRI) remains a challenge due to the trade-off between residual motion blurring caused by discretized motion…
The Challenge of Fetal Cardiac MRI Reconstruction Using Deep Learning
Denis Prokopenko, Kerstin Hammernik, Thomas Roberts +3
Dynamic free-breathing fetal cardiac MRI is one of the most challenging modalities, which requires high temporal and spatial resolution to depict rapid changes in a small fetal hea…