Mean apparent propagator (MAP) MRI: a novel diffusion imaging method for mapping tissue microstructure
Evren Özarslan,Cheng Guan Koay,Timothy M. Shepherd,Michal E. Komlosh,Michal E. Komlosh,M. Okan Irfanoglu,M. Okan Irfanoglu,Carlo Pierpaoli,Peter J. Basser +8 more
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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.read more
Citations
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Journal ArticleDOI
Laplacian-Regularized Mean Apparent Propagator-MRI in Evaluating Corticospinal Tract Injury in Patients with Brain Glioma.
Rifeng Jiang,Shaofan Jiang,Shiwei Song,Xiaoqiang Wei,Kaiji Deng,Zhongshuai Zhang,Yunjing Xue +6 more
TL;DR: MAPL-MRI is an effective approach for evaluating microstructural changes after CST injury and its sensitivity may improve when using the peritumoral CST features.
Journal ArticleDOI
Bayesian uncertainty quantification in linear models for diffusion MRI.
TL;DR: A probabilistic interpretation of linear least‐squares methods are used to recast popular dMRI models as Bayesian ones, which makes it possible to quantify the uncertainty of any derived quantity, and converts successful linear models for dMRI signal estimation to Probabilistic models, capable of accurate uncertainty quantification.
Journal ArticleDOI
Generalized diffusion spectrum magnetic resonance imaging (GDSI) for model-free reconstruction of the ensemble average propagator.
TL;DR: A generalized DSI approach is proposed that allows flexible reconstruction of the diffusion EAP and ODF from multi‐shell diffusion data and data acquired with other sampling patterns and less sensitive compared to the model‐based methods.
Journal ArticleDOI
A Multimodal Study of the Contributions of Conduction Velocity to the Auditory Evoked Neuromagnetic Response: Anomalies in Autism Spectrum Disorder
Timothy P.L. Roberts,Timothy P.L. Roberts,Luke Bloy,Matt Ku,Lisa Blaskey,Carissa R. Jackel,JC Edgar,JC Edgar,Jeffrey I. Berman,Jeffrey I. Berman +9 more
TL;DR: Findings indicate the dependence of electrophysiologic sensory response latency on underlying microstructure (white matter) and neurochemistry (synaptic activity) and the use of biologically based measures to stratify ASD according to their brain‐level “building blocks” as an alternative to their behavioral phenotype.
Proceedings ArticleDOI
Using 3D-SHORE and MAP-MRI to obtain both tractography and microstructural constrast from a clinical DMRI acquisition
TL;DR: This work shows, that by simply spreading the same number of samples over multiple b-values (i.e. multi-shell) they can accurately estimate both the WM directionality using 3D-SHORE and characterize the radially dependent diffusion microstructure measures using MAP-MRI.
References
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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
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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
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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.