A hybrid method for efficient and accurate simulations of diffusion compartment imaging signals

Rensonnet, Gaëtan; Jacobs, Damien; Macq, Benoı̂t; Taquet, Maxime

doi:10.1117/12.2207890

Cited by 3 publications

(8 citation statements)

References 12 publications

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“…The Microstructure Fingerprinting framework first precomputes a dictionary of DW-MRI fingerprints (Rensonnet et al, 2015 , 2019 ). Each fingerprint in the dictionary corresponds to a unique microstructural configuration, obtained by leveraging the physical accuracy of Monte Carlo simulations (Hall and Alexander, 2009 ; Rensonnet et al, 2015 ).…”

Section: Methodsmentioning

confidence: 99%

Fast multi-compartment Microstructure Fingerprinting in brain white matter

Dessain,

Fuchs,

Macq

et al. 2024

Front. Neurosci.

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We proposed two deep neural network based methods to accelerate the estimation of microstructural features of crossing fascicles in the white matter. Both methods focus on the acceleration of a multi-dictionary matching problem, which is at the heart of Microstructure Fingerprinting, an extension of Magnetic Resonance Fingerprinting to diffusion MRI. The first acceleration method uses efficient sparse optimization and a dedicated feed-forward neural network to circumvent the inherent combinatorial complexity of the fingerprinting estimation. The second acceleration method relies on a feed-forward neural network that uses a spherical harmonics representation of the DW-MRI signal as input. The first method exhibits a high interpretability while the second method achieves a greater speedup factor. The accuracy of the results and the speedup factors of several orders of magnitude obtained on in vivo brain data suggest the potential of our methods for a fast quantitative estimation of microstructural features in complex white matter configurations.

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Section: Methodsmentioning

confidence: 99%

Fast multi-compartment Microstructure Fingerprinting in brain white matter

Dessain,

Fuchs,

Macq

et al. 2024

Front. Neurosci.

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“…The dMRI protocol used for the phantom matched as closely as possible the protocol used in the in vivo acquisitions described below. Axons were modeled as straight, randomly-packed cylinders with diameters drawn from a gamma distribution with mean and variance fixed to 1.0μm and 0.6μm, respectively (Rensonnet et al, 2015 ). The cylinder packing density was interpreted as a fiber volume fraction (FVF).…”

Section: Methodsmentioning

confidence: 99%

“…A synthetic phantom based on Monte Carlo simulations of the dMRI signal (Hall and Alexander, 2009;Rensonnet et al, 2015Rensonnet et al, , 2018 was created to compare the microstructural metrics obtained to a known ground truth. The dMRI protocol used for the phantom matched as closely as possible the protocol used in the in vivo acquisitions described below.…”

Section: Experiments I: Synthetic Phantommentioning

confidence: 99%

Unraveling multi-fixel microstructure with tractography and angular weighting

et al. 2023

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Recent advances in MRI technology have enabled richer multi-shell sequences to be implemented in diffusion MRI, allowing the investigation of both the microscopic and macroscopic organization of the brain white matter and its complex network of neural fibers. The emergence of advanced diffusion models has enabled a more detailed analysis of brain microstructure by estimating the signal received from a voxel as the combination of responses from multiple fiber populations. However, disentangling the individual microstructural properties of different macroscopic white matter tracts where those pathways intersect remains a challenge. Several approaches have been developed to assign microstructural properties to macroscopic streamlines, but often present shortcomings. ROI-based heuristics rely on averages that are not tract-specific. Global methods solve a computationally-intensive global optimization but prevent the use of microstructural properties not included in the model and often require restrictive hypotheses. Other methods use atlases that might not be adequate in population studies where the shape of white matter tracts varies significantly between patients. We introduce UNRAVEL, a framework combining the microscopic and macroscopic scales to unravel multi-fixel microstructure by utilizing tractography. The framework includes commonly-used heuristics as well as a new algorithm, estimating the microstructure of a specific white matter tract with angular weighting. Our framework grants considerable freedom as the inputs required, a set of streamlines defining a tract and a multi-fixel diffusion model estimated in each voxel, can be defined by the user. We validate our approach on synthetic data and in vivo data, including a repeated scan of a subject and a population study of children with dyslexia. In each case, we compare the estimation of microstructural properties obtained with angular weighting to other commonly-used approaches. Our framework provides estimations of the microstructure at the streamline level, volumetric maps for visualization and mean microstructural values for the whole tract. The angular weighting algorithm shows increased accuracy, robustness to uncertainties in its inputs and maintains similar or better reproducibility compared to commonly-used analysis approaches. UNRAVEL will provide researchers with a flexible and open-source tool enabling them to study the microstructure of specific white matter pathways with their diffusion model of choice.

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“…Specifically, we followed the method described in Ref. where the exact intracellular signal

S_{in}

is obtained by the Multiple Correlation Function formalism and the extracellular signal

S_{ex}

is computed from fixed‐step Monte Carlo simulations of the random diffusive motion of water molecules in the extracellular space with perfectly elastic reflections at the membranes as described in Ref. .…”

Section: Methodsmentioning

confidence: 99%

“…18. Compared to Monte Carlo simulations performed in both the intra and extracellular compartments, this provides significant gains in precision at fixed computation time and equivalently, significant computational gains for a set precision (16).…”

Section: Signal Simulationsmentioning

confidence: 99%