21 March 2016 Fast and accurate simulations of diffusion-weighted MRI signals for the evaluation of acquisition sequences
Author Affiliations +
Abstract
Diffusion-weighted magnetic resonance imaging (DW-MRI) is a powerful tool to probe the diffusion of water through tissues. Through the application of magnetic gradients of appropriate direction, intensity and duration constituting the acquisition parameters, information can be retrieved about the underlying microstructural organization of the brain. In this context, an important and open question is to determine an optimal sequence of such acquisition parameters for a specific purpose. The use of simulated DW-MRI data for a given microstructural configuration provides a convenient and efficient way to address this problem. We first present a novel hybrid method for the synthetic simulation of DW-MRI signals that combines analytic expressions in simple geometries such as spheres and cylinders and Monte Carlo (MC) simulations elsewhere. Our hybrid method remains valid for any acquisition parameters and provides identical levels of accuracy with a computational time that is 90% shorter than that required by MC simulations for commonly-encountered microstructural configurations. We apply our novel simulation technique to estimate the radius of axons under various noise levels with different acquisition protocols commonly used in the literature. The results of our comparison suggest that protocols favoring a large number of gradient intensities such as a Cube and Sphere (CUSP) imaging provide more accurate radius estimation than conventional single-shell HARDI acquisitions for an identical acquisition time.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gaëtan Rensonnet, Gaëtan Rensonnet, Damien Jacobs, Damien Jacobs, Benoît Macq, Benoît Macq, Maxime Taquet, Maxime Taquet, } "Fast and accurate simulations of diffusion-weighted MRI signals for the evaluation of acquisition sequences", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97843L (21 March 2016); doi: 10.1117/12.2217422; https://doi.org/10.1117/12.2217422
PROCEEDINGS
9 PAGES


SHARE
Back to Top