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Real-time, inline quantitative MRI enabled by scanner-integrated machine learning: a proof of principle with NODDI.

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    Advanced quantitative MRI (qMRI) now runs in "clinical mode" with real-time neural network (NN) parameter estimation. This enables faster, integrated qMRI for wider clinical adoption.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence in Medicine
    • Neuroimaging

    Background:

    • Advanced quantitative MRI (qMRI) techniques are often limited to research settings due to complex, offline parameter estimation.
    • This hinders the clinical translation and adoption of powerful qMRI methods.

    Purpose of the Study:

    • To develop and integrate a real-time, inline parameter estimation system for qMRI using neural networks (NNs).
    • The goal is to enable "clinical mode" qMRI, facilitating broader clinical use.

    Main Methods:

    • Customized Siemens Image Calculation Environment (ICE) pipeline to deploy trained NNs via ONNX Runtime.
    • Trained two fully-connected NNs offline using the neurite orientation dispersion and density imaging (NODDI) model.
    • Demonstrated inline estimation in vivo and evaluated performance with synthetic data.

    Main Results:

    • Successfully integrated NNs into ICE for inline, whole-brain NODDI parameter estimation in under 10 seconds.
    • NNMLE estimates showed consistency with conventional methods and comparable accuracy with better noise robustness.
    • NNGT offered higher noise robustness but with compromised accuracy.

    Conclusions:

    • Real-time inline parameter estimation using this framework overcomes a major barrier to clinical qMRI adoption.
    • The generalizable approach allows efficient integration of advanced qMRI techniques into clinical workflows.