Convergence and parameter choice for Monte-Carlo simulations of diffusion MRI

Area of Science:

• Biophysics
• Medical Imaging
• Computational Science

Background:

• Diffusion Magnetic Resonance Imaging (dMRI) relies on accurate modeling of water diffusion.
• Existing simulation frameworks often lack flexibility for complex biological environments.
• Analytical models struggle to capture intricate tissue microstructures and dynamic changes.

Purpose of the Study:

• To develop a general and flexible Monte Carlo simulation framework for diffusing spins.
• To generate realistic synthetic data for dMRI, particularly in complex and irregular substrates.
• To investigate phenomena like swelling-induced restriction in biological tissues.

Main Methods:

• Implemented a Monte Carlo simulation framework adaptable to complex, irregular substrates.
• Compared simulation results against an analytical model of restricted diffusion to validate accuracy and precision.
• Optimized simulation parameters (number of spins vs. updates) for efficient and accurate data generation.
• Modeled tissue environments incorporating swelling, abutting, and deformation.

Main Results:

• The framework successfully simulates diffusing spins in complex substrates.
• Parameter optimization balances precision and accuracy for synthesized dMRI data.
• Simulations revealed significant departures from analytical models due to swelling-induced restriction in a tissue model.
• Observed effects may explain changes in apparent diffusion coefficients post-ischemic stroke.

Conclusions:

• The developed Monte Carlo framework provides a flexible tool for generating realistic dMRI data.
• It accurately captures complex diffusion behaviors, including swelling-induced restriction, beyond the scope of analytical models.
• Findings suggest swelling-induced restriction is a crucial factor in understanding diffusion changes after acute ischemic stroke.