Author

A. Kofler

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Open access Jul 2025

MRpro: Open Framework for Model‐Based, Learned, and Quantitative MR Imaging—Application to Low‐Field MRI

ABSTRACT MRpro is an open‐source framework for MR image reconstruction and quantitative parameter estimation, with particular relevance to low‐field MRI. Built on PyTorch, it supports modern deep‐learning reconstructions and standardized data exchange through ISMRMRD for raw k‐space data and DICOM and NIfTI for reconst...

Felix Frederik Zimmermann, Patrick Schuenke, C. Aigner et al. · NMR in Biomedicine · 5 citations

Review Aug 2026

Learning spatially varying regularisation parameters of low regularity for image reconstruction

In this chapter, we review and discuss the regularity properties of spatially adaptive regularisation weight functions used in variational image reconstruction. Incorporating such weights into classical model-based regularisers, such as Total Variation (TV) and Total Generalised Variation (TGV), allows the regularisati...

Kostas Papafitsoros, Luca Calatroni, A. Kofler · 1 citation

Preprint Sep 2026

DREAM: Deep-Reparametrization of Adaptive Regularization Maps for Fast Zero-Shot Self-Supervised Learning

Adaptive regularization is an effective means of improving the flexibility of classical variational reconstruction methods while retaining their interpretability and mathematical structure. In this work, we propose an approach for learning deep-reparameterized adaptive regularization maps (DREAM) for Total Variation (T...

T. Vu, A. Biguri, Christoph Kolbitsch et al. · 0 citations