MRpro: Open Framework for Model-Based, Learned, and Quantitative MR Imaging-Application to Low-Field MRI.
Authors
Affiliations (4)
Affiliations (4)
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany.
- Max Planck Research Group MR Physics, Max Planck Institute for Human Development, Berlin, Germany.
- Berlin Center for Advanced Neuroimaging, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
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 reconstructed images and parameter maps. This facilitates integration into existing pipelines and the use of data from different devices. MRpro provides unified data structures for the consistent manipulation of MR datasets and associated metadata, together with a library of composable operators, proximable functionals, and optimization algorithms. The operator library includes a unified Fourier operator for all common trajectories and operators specifically developed for low-field applications, such as a B <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow><msub><mrow></mrow> <mrow><mn>0</mn></mrow> </msub> </mrow> </math> -correction operator. Together, these components underpin ready-to-use implementations of key reconstruction algorithms. For deep learning, MRpro includes data-consistency layers, differentiable optimization layers, state-of-the-art backbone networks, and access to public datasets to facilitate reproducibility. Automated quality control supports collaborative development. We demonstrate MRpro for automatic reconstruction, iterative SENSE, deep-learning-based reconstruction, and quantitative parameter estimation. For selected Cartesian and non-Cartesian tasks, iterative SENSE reconstructions from MRpro differed from those obtained with BART, SigPy, and MRIReco by less than 3% relative RMSE. Further applications use public and simulated datasets and measured low-field data acquired at 0.3 T, 0.6 T, and 47 mT. MRpro provides a reproducible and maintainable foundation for future MR imaging research.