81 citations · 204 across the 34 of their papers we have counts for
8 papers · 1 filter
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…
Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image Modeling
Jiazhen Pan, Suprosanna Shit, Özgün Turgut +6
In dynamic Magnetic Resonance Imaging (MRI), k-space is typically undersampled due to limited scan time, resulting in aliasing artifacts in the image domain. Hence, dynamic MR reco…
Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review
Veronika Spieker, Hannah Eichhorn, Kerstin Hammernik +4
Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since the MR signal is acquired in frequency space, any motion of the imaged object leads to comp…