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OpenMRF: A Modular, Vendor-Neutral Open-Source Framework for Reproducible Magnetic Resonance Fingerprinting using

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    OpenMRF is a new framework for Magnetic Resonance Fingerprinting (MRF) that improves reproducibility and consistency across different MRI systems. This solution accelerates quantitative MRI research by enabling standardized method development and validation.

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    Area of Science:

    • Quantitative MRI
    • Biomedical Engineering
    • Medical Imaging Physics

    Background:

    • Magnetic Resonance Fingerprinting (MRF) adoption is hindered by fragmented tools and lack of unified frameworks.
    • Reproducibility and consistency challenges limit widespread MRF implementation and advancement.

    Purpose of the Study:

    • Introduce OpenMRF, a comprehensive Pulseq-based solution to overcome MRF adoption barriers.
    • Enable consistent, reproducible, and transferable MRF research across vendors, sites, and field strengths.

    Main Methods:

    • OpenMRF integrates modular Pulseq sequence design, Bloch-simulation dictionary creation, and iterative low-rank subspace reconstruction.
    • Evaluated using digital phantoms, multi-site phantom studies (0.55 T, 1.5 T, 3 T), and in vivo scans (liver, myocardium, brain).

    Main Results:

    • Simulations showed high mapping accuracy (T1: 0.03+/-0.32%, T2: 0.12+/-1.94%).
    • Multi-site phantom study yielded relaxation times consistent with reference values across field strengths.
    • In vivo acquisitions demonstrated high-quality parameter maps on various platforms.

    Conclusions:

    • OpenMRF offers a robust, open-source, end-to-end Pulseq solution for MRF.
    • Facilitates reproducible sequence implementation, accurate dictionary simulation, and advanced reconstruction.
    • Aims to accelerate reproducible and harmonized quantitative MRI research through a unified platform.