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% (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% (T1ρ). 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.