181 citations · 270 across the 5 of their papers we have counts for
5 papers
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…
Physics-Aware Motion Simulation for T2*-Weighted Brain MRI
Hannah Eichhorn, Kerstin Hammernik, Veronika Spieker +4
In this work, we propose a realistic, physics-aware motion simulation procedure for T2*-weighted magnetic resonance imaging (MRI) to improve learning-based motion correction. As T2…
Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans and Bayesian Inference
Jana Lipkova, Panagiotis Angelikopoulos, Stephen Wu +11
Glioblastoma is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltr…
DeepASL: Kinetic Model Incorporated Loss for Denoising Arterial Spin Labeled MRI via Deep Residual Learning
Cagdas Ulas, Giles Tetteh, Stephan Kaczmarz +2
Arterial spin labeling (ASL) allows to quantify the cerebral blood flow (CBF) by magnetic labeling of the arterial blood water. ASL is increasingly used in clinical studies due to…
Accelerated Reconstruction of Perfusion-Weighted MRI Enforcing Jointly Local and Nonlocal Spatio-temporal Constraints
Cagdas Ulas, Christine Preibisch, Jonathan Sperl +3
Perfusion-weighted magnetic resonance imaging (MRI) is an imaging technique that allows one to measure tissue perfusion in an organ of interest through the injection of an intravas…