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Open AccessJournal ArticleDOI

Design of multishell sampling schemes with uniform coverage in diffusion MRI

TLDR
In diffusion MRI, a technique known as diffusion spectrum imaging reconstructs the propagator with a discrete Fourier transform, from a Cartesian sampling of the diffusion signal, providing high angular resolution diffusion imaging.
Abstract
PURPOSE: In diffusion MRI, a technique known as diffusion spectrum imaging reconstructs the propagator with a discrete Fourier transform, from a Cartesian sampling of the diffusion signal. Alternatively, it is possible to directly reconstruct the orientation distribution function in q-ball imaging, providing so-called high angular resolution diffusion imaging. In between these two techniques, acquisitions on several spheres in q-space offer an interesting trade-off between the angular resolution and the radial information gathered in diffusion MRI. A careful design is central in the success of multishell acquisition and reconstruction techniques. METHODS: The design of acquisition in multishell is still an open and active field of research, however. In this work, we provide a general method to design multishell acquisition with uniform angular coverage. This method is based on a generalization of electrostatic repulsion to multishell. RESULTS: We evaluate the impact of our method using simulations, on the angular resolution in one and two bundles of fiber configurations. Compared to more commonly used radial sampling, we show that our method improves the angular resolution, as well as fiber crossing discrimination. DISCUSSION: We propose a novel method to design sampling schemes with optimal angular coverage and show the positive impact on angular resolution in diffusion MRI.

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Citations
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Journal ArticleDOI

Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data

TL;DR: The aim of this study is to incorporate support for multi-shell data into the CSD approach as well as to exploit the unique b-value dependencies of the different tissue types to estimate a multi-tissue ODF.
Journal ArticleDOI

Dipy, a library for the analysis of diffusion MRI data

TL;DR: Dipy aims to provide transparent implementations for all the different steps of dMRI analysis with a uniform programming interface, and has implemented classical signal reconstruction techniques, such as the diffusion tensor model and deterministic fiber tractography.
Journal ArticleDOI

Multi-compartment microscopic diffusion imaging.

TL;DR: The normative values of the new biomarkers for a large cohort of healthy young adults are established, which may then support clinical diagnostics in patients, and it is shown that the microscopic diffusion indices offer direct sensitivity to pathological tissue alterations.
Journal ArticleDOI

Quantitative mapping of the per-axon diffusion coefficients in brain white matter

TL;DR: This article presents a simple method for estimating the effective diffusion coefficients parallel and perpendicular to the axons unconfounded by the intravoxel fiber orientation distribution, the per‐axon or microscopic diffusion coefficients.
References
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Journal ArticleDOI

Spin diffusion measurements : spin echoes in the presence of a time-dependent field gradient

TL;DR: In this article, a derivation of the effect of a time-dependent magnetic field gradient on the spin-echo experiment, particularly in the presence of spin diffusion, is given.
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Estimation of the Effective Self-Diffusion Tensor from the NMR Spin Echo

TL;DR: The diagonal and off-diagonal elements of the effective self-diffusion tensor, Deff, are related to the echo intensity in an NMR spin-echo experiment.
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Q‐ball imaging

TL;DR: This work has shown that it is possible to resolve intravoxel fiber crossing using mixture model decomposition of the high angular resolution diffusion imaging (HARDI) signal, but mixture modeling requires a model of the underlying diffusion process.
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Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging.

TL;DR: An algorithm is presented that minimizes the bias inherent in making measurements with a fixed set of gradient vector directions by spreading out measurements in 3‐dimensional gradient vector space and this results in reduced scan times, increased precision, or improved resolution in diffusion tensor images.
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