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Microstructure.jl: A Julia package for probabilistic microstructure model fitting with diffusion MRI.

Ting Gong1, Anastasia Yendiki1

  • 1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States.

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Summary
This summary is machine-generated.

Microstructure.jl is a new Julia package for estimating tissue microstructure from MRI data. It offers flexible modeling and robust estimation methods for improved neuroimaging analysis.

Keywords:
Bayesian estimationJuliadiffusion MRIdiffusion–relaxometrymicrostructure

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biophysics

Background:

  • Diffusion MRI and diffusion-relaxometry are crucial for non-invasively probing tissue microstructure.
  • Accurate estimation of microstructural parameters requires sophisticated modeling and robust fitting techniques.
  • Existing tools may lack flexibility or comprehensive uncertainty quantification for advanced analyses.

Purpose of the Study:

  • Introduce Microstructure.jl, a Julia package for probabilistic estimation of tissue microstructural parameters.
  • Provide a flexible framework for defining compartment models and implementing advanced estimation methods.
  • Demonstrate the package's utility in optimizing acquisition protocols and analyzing in vivo/ex vivo MRI data.

Main Methods:

  • Developed Microstructure.jl in Julia, offering a flexible and extensible framework.
  • Implemented established models like Spherical Mean Technique (SMT), Standard Model Imaging (SMI), and Soma and Neurite Density Imaging (SANDI).
  • Integrated probabilistic estimation methods including Markov Chain Monte Carlo (MCMC) and neural network-based Monte Carlo dropout.

Main Results:

  • Demonstrated Microstructure.jl's capability in optimizing acquisition protocols using synthesized data.
  • Evaluated fitting performance for axon diameter index estimation.
  • Successfully applied the package to analyze in vivo human connectome project data (SMT) and high-gradient scanner data (SANDI), and ex vivo Prisma scanner data (SMI with free water).

Conclusions:

  • Microstructure.jl provides a powerful, unified, and flexible platform for microstructural parameter estimation and uncertainty quantification.
  • The package supports various MRI acquisition types and scanner capabilities, applicable to both in vivo and ex vivo data.
  • It facilitates advanced neuroimaging research by enabling robust analysis of complex tissue microstructures.