4 papers · 1 filter
Physics-informed denoising method for image reconstruction in quantitative low-field MRI
Catarina Redshaw Kranich, Claudia Prieto, Christoph Kolbitsch +1
Low-field magnetic resonance imaging (MRI) is becoming increasingly important for medical imaging because it can reduce healthcare costs while ensuring high diagnostic output. Neve…
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.…
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,…
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…