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Neuroimage. 2019 Jan 1;184:964-980. doi: 10.1016/j.neuroimage.2018.09.076. Epub 2018 Sep 30.

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

Author information

1
ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium; Signal Processing Lab (LTS5), École polytechnique fédérale de Lausanne, Lausanne, Switzerland. Electronic address: gaetan.rensonnet@uclouvain.be.
2
Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
3
Signal Processing Lab (LTS5), École polytechnique fédérale de Lausanne, Lausanne, Switzerland.
4
Institute of Neuroscience, Université catholique de Louvain, Louvain-la-Neuve, Belgium; Department of Neurosurgery, Centre hospitalier universitaire Dinant Godinne, Université catholique de Louvain, Namur, Belgium.
5
ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium.
6
Signal Processing Lab (LTS5), École polytechnique fédérale de Lausanne, Lausanne, Switzerland; Radiology Department, Centre hospitalier universitaire vaudois and University of Lausanne, Lausanne, Switzerland.
7
ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium; Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.

Abstract

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.

KEYWORDS:

Diffusion-weighted magnetic resonance imaging; Microstructure fingerprinting; Monte Carlo simulations; Sparse optimization; Tissue microstructure

PMID:
30282007
PMCID:
PMC6230496
[Available on 2020-01-01]
DOI:
10.1016/j.neuroimage.2018.09.076

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