8 citations · 8 across the 3 of their papers we have counts for
3 papers
eess.IV2025
Reliable Evaluation of MRI Motion Correction: Dataset and Insights
Kun Wang, Tobit Klug, Stefan Ruschke +2
Correcting motion artifacts in MRI is important, as they can hinder accurate diagnosis. However, evaluating deep learning-based and classical motion correction methods remains fund…
cs.CV2024
Resolution-Robust 3D MRI Reconstruction with 2D Diffusion Priors: Diverse-Resolution Training Outperforms Interpolation
Anselm Krainovic, Stefan Ruschke, Reinhard Heckel
Deep learning-based 3D imaging, in particular magnetic resonance imaging (MRI), is challenging because of limited availability of 3D training data. Therefore, 2D diffusion models t…
physics.med-ph2023★ 8 cited
Diffusion-weighted MR spectroscopy: consensus, recommendations and resources from acquisition to modelling
Clémence Ligneul, Chloé Najac, André Döring +24
Brain cell structure and function reflect neurodevelopment, plasticity and ageing, and changes can help flag pathological processes such as neurodegeneration and neuroinflammation.…