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Recent Advances in Parameter Inference for Diffusion MRI Signal Models.

Yoshitaka Masutani1

  • 1Graduate School of Information Sciences, Hiroshima City University.

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Summary
This summary is machine-generated.

This review covers quantitative biological feature extraction using diffusion MRI. It details parametric models like DTI, DKI, and NODDI, and explores parameter inference from fitting to machine learning approaches.

Keywords:
Q-space learningdiffusion magnetic resonance imagingparameter inferencesignal models

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Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Quantitative Biology

Background:

  • Diffusion MRI enables quantitative biological feature extraction.
  • Understanding its history, applications, and development is crucial.
  • Parametric models are key to interpreting diffusion MRI data.

Purpose of the Study:

  • To review the fundamentals and recent advancements in quantitative biological feature extraction using diffusion MRI.
  • To provide a comprehensive overview of established and emerging diffusion MRI models and parameter inference techniques.
  • To discuss future directions in diffusion MRI modeling and simulation.

Main Methods:

  • Review of diffusion MRI history, applications, and development.
  • Introduction to parametric models: diffusion tensor imaging (DTI), diffusional kurtosis imaging (DKI), and neurite orientation dispersion diffusion imaging (NODDI).
  • Discussion of mathematical generalization and methodologies for model parameter inference, including conventional fitting and Q-space learning (QSL).

Main Results:

  • Established parametric models like DTI, DKI, and NODDI are presented with various classifications.
  • Model parameter inference methodologies range from traditional fitting to advanced machine learning (QSL).
  • The review synthesizes current knowledge on quantitative biological feature extraction via diffusion MRI.

Conclusions:

  • Diffusion MRI offers powerful tools for quantitative biological insights.
  • Advancements in modeling and parameter inference, including QSL, enhance data interpretation.
  • Future research should focus on imaging modeling and simulation for further progress.