OpenMRF: A Modular, Vendor-Neutral Open-Source Framework for Reproducible Magnetic Resonance Fingerprinting using Pulseq
Abstract
Purpose: Widespread adoption and methodological advancement of Magnetic Resonance Fingerprinting (MRF) are limited by the lack of unified, reproducible implementation frameworks and fragmented open-source tools. To address these barriers, we introduce OpenMRF - a comprehensive Pulseq-based solution - designed to enable consistent, reproducible, and transferable MRF research across vendors, sites, and field strengths. Methods: OpenMRF integrates modular Pulseq-based sequence design, Bloch-simulation-based dictionary creation directly from .seq files, and iterative low-rank subspace reconstruction. The framework was evaluated through digital phantom simulations, a multi-site ISMRM/NIST phantom study on Siemens MRI systems at 0.55 T, 1.5 T, and 3 T as well as GE and United Imaging 3 T platforms, and representative in vivo acquisitions in the liver (0.55 T), myocardium (1.5 T), and brain (3 T). Results: Simulations demonstrated high mapping accuracy in an ISMRM/NIST-like digital phantom, with low-rank reconstruction yielding deviations of 0.03+/-0.32 % (T1) and 0.12+/-1.94 % (T2). The multi-site phantom study yielded relaxation times consistent with reference values at all field strengths, with mean deviations of -0.1+/-2.9 % (T1), -1.5+/-8.7 % (T2), and -4.0+/-7.2 % (T1rho). In vivo acquisitions produced high-quality parameter maps across platforms and field strengths. Conclusion: OpenMRF provides a robust, open-source, end-to-end Pulseq-based solution for MRF that enables reproducible sequence implementation, physics-accurate dictionary simulation, and advanced reconstruction across vendors and field strengths. By providing a unified platform for method development, comparison, and multi-site validation, OpenMRF aims to accelerate reproducible and harmonized quantitative MRI research within the community.
Cite
@article{arxiv.2604.22713,
title = {OpenMRF: A Modular, Vendor-Neutral Open-Source Framework for Reproducible Magnetic Resonance Fingerprinting using Pulseq},
author = {Tom Griesler and Jannik Stebani and Sydney Kaplan and Ivaylo Angelov and Petra Albert and Martin Blaimer and Tobias Wech and Xiang Wang and Qingping Chen and Maxim Zaitsev and Zhibo Zhu and Qi Liu and Peter Martin and Jon-Fredrik Nielsen and Jesse I Hamilton and Peter Nordbeck and Nicole Seiberlich and Maximilian Gram},
journal= {arXiv preprint arXiv:2604.22713},
year = {2026}
}