OpenMRF : A Modular, Vendor‐Neutral Open‐Source Framework for Magnetic Resonance Fingerprinting Using Pulseq

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 standardized and transferable MRF research across vendors, sites, and field strengths. Methods OpenMRF integrates modular Pulseq‐based sequence design, Bloch‐equation‐based dictionary generation 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, 1.5, 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% (T 1 ) and 0.12% ± 1.94% (T 2 ). The multi‐site phantom study yielded relaxation times consistent with reference values at all field strengths, with mean deviations of −0.1% ± 2.9% (T 1 ), −1.5% ± 8.7% (T 2 ), and −4.0% ± 7.2% (T 1ρ ). In vivo acquisitions produced high‐quality parameter maps across different anatomical applications and field strengths. Conclusion OpenMRF provides a robust, open‐source, end‐to‐end Pulseq‐based solution for MRF designed to enable 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 cross‐vendor application, OpenMRF aims to accelerate reproducible and harmonized quantitative MRI research within the community.