A Novel Monte Carlo Simulation Framework in Julia for Generating Realistic Synthetic Diffusion MRI Signals

This research project builds upon previous simulation frameworks to develop a more complex and computationally efficient model for generating synthetic diffusion MRI signals in approximated white matter voxel spaces. The primary goal was to migrate previous simulation code to Julia to produce diffusion-weighted signals that closely resemble those obtained from real-world MRI scans.  Signal values are derived by tracking the phase variations of particle spins as they undergo random diffusion within a simulated voxel space populated with cylindrical barriers. The impacts of simulated diffusion gradients on their phase shifts is added to simulate the pulsed-gradient spin echo sequence, allowing for a realistic representation of signal decay with respect to varied gradient strengths.  To accommodate these increased computational demands that required a higher number of simulation steps and particle counts, the simulation code was migrated to Julia to take advantage of its improved computation speeds. This transition significantly reduces simulation runtimes compared to previous attempts using MATLAB, making large-scale particle simulations more feasible.  Results demonstrate that the synthetic signals follow the expected decay trend with increasing gradient strength, consistent with literature. Higher gradient strengths introduce greater phase variations among spins, leading to increased signal decay. These findings justify the accuracy of this new model in replicating diffusion-weighted MRI signals.  Overall, this project provides a Julia-based toolbox for diffusion MRI signal simulation, addressing a gap in the field by offering a fast and flexible framework for synthetic signal generation and diffusion simulation.