87 citations · 182 across the 19 of their papers we have counts for
4 papers · 1 filter
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,…
Deep unrolling for learning optimal spatially varying regularisation parameters for Total Generalised Variation
Thanh Trung Vu, Andreas Kofler, Kostas Papafitsoros
We extend a recently introduced deep unrolling framework for learning spatially varying regularisation parameters in inverse imaging problems to the case of Total Generalised Varia…
Learning Spatially Adaptive -Norms Weights for Convolutional Synthesis Regularization
Andreas Kofler, Luca Calatroni, Christoph Kolbitsch +1
We propose an unrolled algorithm approach for learning spatially adaptive parameter maps in the framework of convolutional synthesis-based regularization. More precisely,…
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…