Towards a high-density packing white matter substrate generator for Monte-Carlo simulations

One main goal of Diffusion-Weighted Magnetic Resonance Imaging is to infer microscopic tissue properties from the measured signal. The use of Monte-Carlo Simulations for DW-MRI on realistic substrates will help the study of DWI-MRI signals in controlled environments and to investigate, extract, and validate approaches for the understanding of white matter features. In this work, we present a novel framework for creating complex white matter phantoms with unprecedented characteristics like high packing density greater than 90% intra-axonal volume fraction and voxel size of (140 um)3.

Towards microstructure fingerprinting: Estimation of tissue properties from a dictionary of Monte Carlo diffusion MRI simulations

Gabriel Girard, Benoît Macq, Gaëtan Olivier D Rensonnet, Jean-Philippe Thiran

Many closed-form analytical models have been proposed to relate the diffusion-weighted magnetic resonance imaging (DW-MRI) signal to microstructural features of white matter tissues. These models generally make assumptions about the tissue and the diffusion processes which often depart from the biophysical reality, limiting their reliability and interpretability in practice. Monte Carlo simulations of the random walk of water molecules are widely recognized to provide near groundtruth for DW-MRI signals. However, they have mostly been limited to the validation of simpler models rather than used for the estimation of microstructural properties. This work proposes a general framework which leverages Monte Carlo simulations for the estimation of physically interpretable microstructural parameters, both in single and in crossing fascicles of axons. Monte Carlo simulations of DW-MRI signals, or fingerprints, are pre-computed for a large collection of microstructural configurations. At every voxel, the microstructural parameters are estimated by optimizing a sparse combination of these fingerprints. Extensive synthetic experiments showed that our approach achieves accurate and robust estimates in the presence of noise and uncertainties over fixed or input parameters. In an in vivo rat model of spinal cord injury, our approach provided microstructural parameters that showed better correspondence with histology than five closed-form models of the diffusion signal: MMWMD, NODDI, DIAMOND, WMTI and MAPL. On whole-brain in vivo data from the human connectome project (HCP), our method exhibited spatial distributions of apparent axonal radius and axonal density indices in keeping with ex vivo studies. This work paves the way for microstructure fingerprinting with Monte Carlo simulations used directly at the modeling stage and not only as a validation tool.

2018

Towards non-parametric reconstruction of axon diameter distributions using diffusion MRI and regularized discrete linear modeling

David Paul Roger Romascano

The distribution of axon diameters (ADD) is an important white matter feature: it influences action potential speed, it has been shown to evolve during development, and also to be affected during pathological processes. Diffusion Magnetic Resonance Imaging is a powerful non-invasive tool that is sensitive to the displacement of water molecules. In theory, as axon diameters are well bellow the imaging resolution, the ADD can be estimated indirectly by fitting a biophysical model to the measured signal. Such an approach is the basis of microstructure imaging. Microstructure imaging usually relies on compartment models, splitting the signal into the intra-axonal or the extra-axonal compartments. However, in practice, ADD reconstruction remains a challenging problem, mainly due to model degeneracy (different solutions can have similar signals), the diameter lower bound which dictates the smallest diameter visible by the scanner, and the difficulty in modeling properly the extra-axonal signal. In this thesis, we addressed the challenges at stake by focusing on the intra-axonal and extra-axonal compartments separately. Model performance was evaluated by comparing estimates with the ground-truth provided by Monte Carlo simulations. Model degeneracy was addressed by introducing Laplacian regularization, which was shown to provide better ADD estimates when considering the intra-axonal signal only. Sensitivity to small diameters was improved by using a richer diffusion protocol designed to maximize sensitivity to a set of diameters, which was shown to provide reconstruction of unimodal and bimodal distributions, with sensitivity to population specific changes. Regarding the extra-axonal space, using a mixture of higher order tensors improved reconstruction of the signal compared to standard models. The proposed model is flexible enough to adapt to a variety of simulated signals, outperforming current models of hindered diffusion. Combining the results for both compartments slightly improved estimates for some of the simulated signals. We highlighted the similarity between the intra-axonal and extra-axonal signals, which might be the main limiting factor when using single Pulsed Gradient Spin Echo sequences.

EPFL2019

Towards non-parametric reconstruction of axon diameter distributions using diffusion MRI and regularized discrete linear modeling

David Paul Roger Romascano

EPFL2019

Towards microstructure fingerprinting: Estimation of tissue properties from a dictionary of Monte Carlo diffusion MRI simulations

Gabriel Girard, Benoît Macq, Gaëtan Olivier D Rensonnet, Jean-Philippe Thiran

2018