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Input Layer Regularization and Automated Regularization Hyperparameter Tuning for Myelin Water Estimation Using Deep

Mirage Modi1, Shashank Sule2, Jonathan Palumbo1

  • 1National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA.

NMR in Biomedicine
|April 22, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces a deep learning framework to enhance myelin water fraction (MWF) estimation in brain MRI. Integrating classical regularization with deep learning improves accuracy for myelin content quantification.

Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Machine Learning

Background:

  • Myelin water fraction (MWF) is crucial for assessing brain tissue integrity.
  • Accurate MWF estimation from magnetic resonance relaxometry is challenging.
  • Biexponential signal modeling is a standard but complex method for MWF quantification.

Purpose of the Study:

  • To develop a deep learning framework for improved MWF estimation.
  • To integrate classical regularization and data preprocessing techniques.
  • To enhance the accuracy of myelin content quantification in the brain.

Main Methods:

  • A deep learning framework based on input layer regularization (ILR) was developed.
  • Optimal regularization hyperparameters were selected using a neural network or generalized cross-validation (GCV).
Keywords:
additive modelsbilevel optimizationinverse problemsmultiexponential analysis

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  • The framework was extended to directly estimate MWF and exponential time constants.

    • Main Results:

    • The proposed deep learning architecture outperformed conventional methods on synthetic data.
    • GCV-based hyperparameter selection showed slightly better performance than the neural network approach on in vivo data.
    • The framework demonstrated superior accuracy in MWF estimation compared to standard multilayer perceptrons.

    Conclusions:

    • Input layer regularization significantly enhances MWF estimation within the biexponential model.
    • Integrating classical regularization with deep learning substantially improves quantitative myelin content estimation.
    • The developed framework offers a more accurate approach for brain myelin quantification using MRI data.