Diffusion Microscopist Simulator: A General Monte Carlo Simulation System for Diffusion Magnetic Resonance Imaging

Chun-Hung Yeh,Cyril Poupon,Ching-Po Lin,Benoît Schmitt,Denis Le Bihan,Jing-Rebecca Li-Schlittgen,Wang Zhan

doi:10.1371/journal.pone.0076626

Abstract

This article describes the development and application of an integrated, generalized, and efficient Monte Carlo simulation system for diffusion magnetic resonance imaging (dMRI), named Diffusion Microscopist Simulator (DMS). DMS comprises a random walk Monte Carlo simulator and an MR image synthesizer. The former has the capacity to perform large-scale simulations of Brownian dynamics in the virtual environments of neural tissues at various levels of complexity, and the latter is flexible enough to synthesize dMRI datasets from a variety of simulated MRI pulse sequences. The aims of DMS are to give insights into the link between the fundamental diffusion process in biological tissues and the features observed in dMRI, as well as to provide appropriate ground-truth information for the development, optimization, and validation of dMRI acquisition schemes for different applications. The validity, efficiency, and potential applications of DMS are evaluated through four benchmark experiments, including the simulated dMRI of white matter fibers, the multiple scattering diffusion imaging, the biophysical modeling of polar cell membranes, and the high angular resolution diffusion imaging and fiber tractography of complex fiber configurations. We expect that this novel software tool would be substantially advantageous to clarify the interrelationship between dMRI and the microscopic characteristics of brain tissues, and to advance the biophysical modeling and the dMRI methodologies.

Highlights

Diffusion magnetic resonance imaging came into existence in the mid-1980s [1,2,3], and during the past 25 years, diffusion magnetic resonance imaging (dMRI) has been extraordinarily successful, in MRI of the central nervous system
Diffusion Microscopist Simulator (DMS) aims at (i) bridging the gap between elementary diffusion processes occurring at a micrometer scale and the resulting diffusion signal measured at millimeter scale, providing better insights into the features observed in dMRI, and (ii) offering ground-truth information for optimization and validation of dMRI acquisition schemes for different applications
Since we incorporated each cell with a dynamic morphological evolution function to simulate cell swelling, the cells were expanding from the smallest sizes at the beginning (t = 0 ms) to the largest sizes at the end (t = 100 ms) of the simulation

Summary

Introduction

Diffusion magnetic resonance imaging (or dMRI) came into existence in the mid-1980s [1,2,3], and during the past 25 years, dMRI has been extraordinarily successful, in MRI of the central nervous system. Its major clinical domain of application has been neurological disorders, especially for the management of patients with acute ischemic stroke. It is rapidly becoming a standard for white matter (WM) disorders, as diffusion tensor (DT) imaging can reveal abnormalities in WM fiber structure and provide outstanding maps of brain connectivity [4,5]. The driving force of dMRI is to monitor natural microscopic displacements of water molecules that occur in brain tissues as part of the physical diffusion process. Water molecules are used as a probe that can reveal microscopic details about tissue architecture, either in a normal or diseased state

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