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    A new method called auto-regressive discrete acquisition points transformation (ADAPT) can identify the number of exponential components in diffusion-weighted MRI data without prior assumptions. This approach shows strong correlations with intravoxel incoherent motion parameters, offering potential for new biomarkers.

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    Area of Science:

    • Medical Imaging
    • Biophysics
    • Data Analysis

    Background:

    • Diffusion-weighted magnetic resonance imaging (DW-MRI) is crucial for characterizing tissue microstructure.
    • Fitting multi-exponential models to DW-MRI data is challenging due to an unknown number of components.
    • Existing methods often require prior assumptions about the number of components.

    Purpose of the Study:

    • To introduce and evaluate a novel method, auto-regressive discrete acquisition points transformation (ADAPT), for fitting DW-MRI data with an unknown number of multi-exponential components.
    • To assess ADAPT's ability to determine the number of exponential components without prior assumptions.
    • To investigate the correlation between ADAPT coefficients and parameters from the intravoxel incoherent motion (IVIM) model.

    Main Methods:

    • The auto-regressive discrete acquisition points transformation (ADAPT) method was developed, adapting an auto-regressive moving average system.
    • ADAPT was tested on simulated DW-MRI data to evaluate its component identification and modeling capabilities.
    • The optimized ADAPT model was applied to human brain DW-MRI data, and its coefficients were correlated with established IVIM parameters.

    Main Results:

    • ADAPT successfully identified the correct number of exponential components and accurately modeled the data.
    • Significant correlations were observed between ADAPT coefficients and IVIM parameters: ADAPT(1,1)-β0 with IVIM-D (ρ=0.708), ADAPT(1,1)-α1 with IVIM-f (ρ=0.667), and ADAPT(1,1)-β1 with IVIM-D* (ρ=0.741).
    • All correlations were statistically significant (P < 0.001).

    Conclusions:

    • ADAPT provides a robust method for analyzing DW-MRI data with an unknown number of multi-exponential components.
    • The method enables the determination of potential complex diffusion biomarkers by identifying meaningful coefficients without prior information.
    • ADAPT offers a generalized fitting approach for discrete multi-exponential data with significant potential in medical imaging research.