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eess.IV2026
Learning spatially adaptive sparsity level maps for arbitrary convolutional dictionaries
Joshua Schulz, David Schote, Christoph Kolbitsch +2
State-of-the-art learned reconstruction methods often rely on black-box modules that, despite their strong performance, raise questions about their interpretability and robustness.…
eess.IV2025
MRpro: open framework for model-based, learned, and quantitative MR imaging
Felix Frederik Zimmermann, Patrick Schuenke, Christoph S. Aigner +12
We preseent an open-source image reconstruction package built upon PyTorch, enabling modern deep-learning reconstructions. It uses open data formats for input and output (ISMRMRD,…
eess.IV2025
MR imaging in the low-field: Leveraging the power of machine learning
Andreas Kofler, Dongyue Si, David Schote +3
Recent innovations in Magnetic Resonance Imaging (MRI) hardware and software have reignited interest in low-field () and ultra-low-field MRI (). T…