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Efficient DCE-MRI Parameter and Uncertainty Estimation Using a Neural Network.

Yannick Bliesener, Jay Acharya, Krishna S Nayak

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    This summary is machine-generated.

    This study introduces a fast neural network method to estimate tracer-kinetic parameters and their uncertainties from quantitative DCE-MRI. This approach accurately maps parameters and differentiates changes in brain tumor pharmacokinetics over time.

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    Area of Science:

    • Radiology
    • Medical Imaging
    • Computational Biology

    Background:

    • Quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) generates voxel-wise tracer-kinetic parameters crucial for health and disease assessment.
    • Existing methods for quantifying variability in these parameters are computationally expensive and often not reported.

    Purpose of the Study:

    • To develop a novel, computationally efficient method for simultaneously estimating tracer-kinetic parameters and their uncertainties from DCE-MRI data.
    • To validate the accuracy of the proposed method using a digital reference object and assess its utility in tracking changes in brain tumor pharmacokinetics.

    Main Methods:

    • A neural network was trained to estimate the joint posterior distribution of tracer-kinetic parameters, providing voxel-specific uncertainty estimates.
    • The method was evaluated by comparing predicted parameter ranges with uncertainties from noise and post-processing variations in a digital reference object.
    • The approach was applied to differentiate significant changes in brain tumor pharmacokinetics over time by resolving model singularities.

    Main Results:

    • The neural network approach achieved accurate estimation of tracer-kinetic parameter maps with significantly reduced computation time.
    • Predicted parameter ranges correlated well with those derived from varying noise levels and regression algorithms in a digital reference object.
    • The method successfully demonstrated the ability to distinguish meaningful pharmacokinetic changes in brain tumors over time.

    Conclusions:

    • This novel neural network-based method offers a rapid and accurate way to estimate tracer-kinetic parameters and their uncertainties from DCE-MRI.
    • The approach enhances the reliability of DCE-MRI analysis by accounting for intrinsic model uncertainties and variations.
    • The validated method holds promise for improved monitoring of therapeutic responses and disease progression in clinical settings, particularly for brain tumors.