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Open AccessJournal ArticleDOI

Mean apparent propagator (MAP) MRI: a novel diffusion imaging method for mapping tissue microstructure

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TLDR
MAP-MRI represents a new comprehensive framework to model the three-dimensional q-space MR signal and transform it into diffusion propagators, and provides several novel, quantifiable parameters that capture previously obscured intrinsic features of nervous tissue microstructure.
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This article is published in NeuroImage.The article was published on 2013-09-01 and is currently open access. It has received 316 citations till now. The article focuses on the topics: Diffusion Anisotropy & Diffusion MRI.

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Citations
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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.
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Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited

TL;DR: The results indicate that, even with high-quality data, DWI tractography alone is unlikely to provide an anatomically accurate map of the brain connectome, and suggest that there is an inherent limitation in determining long-range anatomical projections based on voxel-averaged estimates of local fiber orientation obtained from DWI data that is likely to be overcome by improvements in data acquisition and analysis alone.
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Quantifying brain microstructure with diffusion MRI: Theory and parameter estimation

TL;DR: In this article, the authors review, systematize and discuss models of diffusion in neuronal tissue, by putting them into an overarching physical context of coarse-graining over an increasing diffusion length scale.
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Imaging brain microstructure with diffusion MRI: practicality and applications

TL;DR: The article summarizes the relevant aspects of brain microanatomy and the range of diffusion‐weighted MR measurements that provide to them and reviews the evolution of mathematical and computational models that relate the diffusion MR signal to brain tissue microstructure.
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Recognition of white matter bundles using local and global streamline-based registration and clustering.

TL;DR: The purpose of the proposed method, named RecoBundles, is to segment white matter bundles and make virtual dissection easier to perform and robust and adaptive to incomplete data and bundles with missing components.
References
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Journal ArticleDOI

Axcaliber: A method for measuring axon diameter distribution from diffusion MRI

TL;DR: A new NMR‐based approach that employs a model of water diffusion within “restricted” cylindrical axons to estimate their diameter distribution within a nerve bundle is proposed and validated by comparing the diameter distributions measured using the NMR and histological techniques on sciatic and optic nerve tissue specimens.
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Characterization of anisotropy in high angular resolution diffusion-weighted MRI†

TL;DR: In this representation, it is shown that isotropic diffusion, anisotropic diffusion from a single fiber, and anisotrop diffusion from multiple fiber directions fall into distinct and separable channels and can be determined for any voxel without any prior information by a spherical harmonic transform.
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Detection and modeling of non-Gaussian apparent diffusion coefficient profiles in human brain data.

TL;DR: This work details the observation of non‐Gaussian apparent diffusion coefficient (ADC) profiles in multi‐direction, diffusion‐weighted MR data acquired with easily achievable imaging parameters, and uses it to show that non‐ Gaussian profiles arise consistently in various regions of the human brain.
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Generalized diffusion tensor imaging and analytical relationships between diffusion tensor imaging and high angular resolution diffusion imaging

TL;DR: The inadequacy of the traditional rank‐2 tensor model is demonstrated with simulations, and the method is applied to excised rat brain data collected in a spin‐echo HARDI experiment.
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Measurement of fiber orientation distributions using high angular resolution diffusion imaging.

TL;DR: The new method addresses the problem of partial volume averaging in diffusion tensor imaging and provides a basis for more reliable estimates of fiber orientation and fractional anisotropy.
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