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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.
About
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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Journal ArticleDOI

Direct and specific assessment of axonal injury and spinal cord microenvironments using diffusion correlation imaging

TL;DR: The ability to selectively image microstructural changes following axonal injury in the spinal cord can be useful in clinical and research applications by enabling specific detection and increased sensitivity to injury-inducedmicrostructural alterations.
Journal ArticleDOI

White Matter Integrity, Duration of Untreated Psychosis, and Antipsychotic Treatment Response in Medication-Naïve First Episode Psychosis Patients

TL;DR: Empirical support is provided to the idea the DUP may have fundamental pathogenic effects on the natural history of psychosis, suggest a biological mechanism underlying this phenomenon, and underscore the importance of early intervention efforts in this disabling neuropsychiatric syndrome.
Book ChapterDOI

A Unifying Framework for Spatial and Temporal Diffusion in Diffusion MRI

TL;DR: This work proposes a novel framework to simultaneously represent the diffusion-weighted MRI (dMRI) signal over diffusion times, gradient strengths and gradient directions, and shows that the method is robust to noise, and can accurately describe the restricted spatio-temporal signal decay originating from tissue models such as cylindrical pores.
Journal ArticleDOI

Optimal DSI reconstruction parameter recommendations: Better ODFs and better connectivity

TL;DR: It is shown that the parameters in the reconstruction have huge impact on the reconstruction quality and that, while there is no consensus about what they should be, the parameters the authors most often see in the literature are not optimal.
Journal ArticleDOI

Mesoscale diffusion magnetic resonance imaging of the ex vivo human hippocampus.

TL;DR: Using ex vivo samples, surgically excised from patients with intractable epilepsy, it is found that shorter diffusion times were advantageous at the mesoscale, providing a compromise between mean diffusivity and fractional anisotropy measurements.
References
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Book

Table of Integrals, Series, and Products

TL;DR: Combinations involving trigonometric and hyperbolic functions and power 5 Indefinite Integrals of Special Functions 6 Definite Integral Integral Functions 7.Associated Legendre Functions 8 Special Functions 9 Hypergeometric Functions 10 Vector Field Theory 11 Algebraic Inequalities 12 Integral Inequality 13 Matrices and related results 14 Determinants 15 Norms 16 Ordinary differential equations 17 Fourier, Laplace, and Mellin Transforms 18 The z-transform
Book

Compressed sensing

TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.

A table of integrals

TL;DR: Basic Forms x n dx = 1 n + 1 x n+1 (1) 1 x dx = ln |x| (2) udv = uv − vdu (3) 1 ax + bdx = 1 a ln|ax + b| (4) Integrals of Rational Functions
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.
Book

Solving least squares problems

TL;DR: Since the lm function provides a lot of features it is rather complicated so it is going to instead use the function lsfit as a model, which computes only the coefficient estimates and the residuals.
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