MRpro - open PyTorch-based MR reconstruction and processing package
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915418542702592 |
|---|---|
| author | Zimmermann, Felix Frederik Schuenke, Patrick Aigner, Christoph S. Bernhardt, Bill A. Guastini, Mara Hammacher, Johannes Jaitner, Noah Kofler, Andreas Lunin, Leonid Martin, Stefan Kranich, Catarina Redshaw Schattenfroh, Jakob Schote, David Wu, Yanglei Kolbitsch, Christoph |
| author_facet | Zimmermann, Felix Frederik Schuenke, Patrick Aigner, Christoph S. Bernhardt, Bill A. Guastini, Mara Hammacher, Johannes Jaitner, Noah Kofler, Andreas Lunin, Leonid Martin, Stefan Kranich, Catarina Redshaw Schattenfroh, Jakob Schote, David Wu, Yanglei Kolbitsch, Christoph |
| contents | We introduce MRpro, an open-source image reconstruction package built upon PyTorch and open data formats. The framework comprises three main areas. First, it provides unified data structures for the consistent manipulation of MR datasets and their associated metadata (e.g., k-space trajectories). Second, it offers a library of composable operators, proximable functionals, and optimization algorithms, including a unified Fourier operator for all common trajectories and an extended phase graph simulation for quantitative MR. These components are used to create ready-to-use implementations of key reconstruction algorithms. Third, for deep learning, MRpro includes essential building blocks such as data consistency layers, differentiable optimization layers, and state-of-the-art backbone networks and integrates public datasets to facilitate reproducibility. MRpro is developed as a collaborative project supported by automated quality control. We demonstrate the versatility of MRpro across multiple applications, including Cartesian, radial, and spiral acquisitions; motion-corrected reconstruction; cardiac MR fingerprinting; learned spatially adaptive regularization weights; model-based learned image reconstruction and quantitative parameter estimation. MRpro offers an extensible framework for MR image reconstruction. With reproducibility and maintainability at its core, it facilitates collaborative development and provides a foundation for future MR imaging research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_23129 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MRpro - open PyTorch-based MR reconstruction and processing package Zimmermann, Felix Frederik Schuenke, Patrick Aigner, Christoph S. Bernhardt, Bill A. Guastini, Mara Hammacher, Johannes Jaitner, Noah Kofler, Andreas Lunin, Leonid Martin, Stefan Kranich, Catarina Redshaw Schattenfroh, Jakob Schote, David Wu, Yanglei Kolbitsch, Christoph Image and Video Processing Computer Vision and Pattern Recognition Medical Physics We introduce MRpro, an open-source image reconstruction package built upon PyTorch and open data formats. The framework comprises three main areas. First, it provides unified data structures for the consistent manipulation of MR datasets and their associated metadata (e.g., k-space trajectories). Second, it offers a library of composable operators, proximable functionals, and optimization algorithms, including a unified Fourier operator for all common trajectories and an extended phase graph simulation for quantitative MR. These components are used to create ready-to-use implementations of key reconstruction algorithms. Third, for deep learning, MRpro includes essential building blocks such as data consistency layers, differentiable optimization layers, and state-of-the-art backbone networks and integrates public datasets to facilitate reproducibility. MRpro is developed as a collaborative project supported by automated quality control. We demonstrate the versatility of MRpro across multiple applications, including Cartesian, radial, and spiral acquisitions; motion-corrected reconstruction; cardiac MR fingerprinting; learned spatially adaptive regularization weights; model-based learned image reconstruction and quantitative parameter estimation. MRpro offers an extensible framework for MR image reconstruction. With reproducibility and maintainability at its core, it facilitates collaborative development and provides a foundation for future MR imaging research. |
| title | MRpro - open PyTorch-based MR reconstruction and processing package |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Medical Physics |
| url | https://arxiv.org/abs/2507.23129 |