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
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Journal ArticleDOI
Multi-shell diffusion signal recovery from sparse measurements
Yogesh Rathi,Oleg V. Michailovich,Frederik Bernd Laun,Kawin Setsompop,Patricia Ellen Grant,Carl-Fredrik Westin +5 more
TL;DR: This work proposes a new method for the reconstruction of diffusion signals in the entire q-space from highly undersampled sets of MSDI data, thus reducing the scan time significantly and enforcing the reconstructed signal to have smooth spatial regularity in the brain, by minimizing the total variation (TV) norm.
Journal ArticleDOI
Microstructure Imaging of Crossing (MIX) White Matter Fibers from diffusion MRI
Hamza Farooq,Junqian Xu,Jung Who Nam,Daniel F. Keefe,Essa Yacoub,Tryphon T. Georgiou,Christophe Lenglet +6 more
TL;DR: A new and versatile optimization technique (MIX) is introduced, which enables microstructure imaging of crossing white matter fibers in fiber crossings using synthetic as well as ex- vivo and in-vivo brain data.
Journal ArticleDOI
DR-TAMAS: Diffeomorphic Registration for Tensor Accurate Alignment of Anatomical Structures.
M. Okan Irfanoglu,Amritha Nayak,Jeffrey Jenkins,Elizabeth B. Hutchinson,Neda Sadeghi,Cibu Thomas,Carlo Pierpaoli +6 more
TL;DR: The proposed DR-TAMAS (Diffeomorphic Registration for Tensor Accurate alignMent of Anatomical Structures), a novel framework for intersubject registration of Diffusion Tensor Imaging (DTI) data sets, has excellent overall performance and is equivalent to the best existing methods in WM.
Journal ArticleDOI
Denoising and fast diffusion imaging with physically constrained sparse dictionary learning.
TL;DR: This work aims to denoise DWI and reduce the number of required measurements, while maintaining data quality, using sparse dictionary learning constrained by the physical properties of the signal: symmetry and positivity.
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Microstructural organization of human insula is linked to its macrofunctional circuitry and predicts cognitive control.
Vinod Menon,Guillermo Gallardo,Guillermo Gallardo,Mark A. Pinsk,Van Dang Nguyen,Jing-Rebecca Li,Weidong Cai,Demian Wassermann +7 more
TL;DR: Quantitative modeling of multi-shell diffusion MRI data from 413 participants revealed that human insula microstructure differs significantly across subdivisions that serve distinct cognitive and affective functions, and insular microstructural features were linked to behavior and predicted individual differences in cognitive control ability.
References
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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
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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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Spin diffusion measurements : spin echoes in the presence of a time-dependent field gradient
E. O. Stejskal,J. E. Tanner +1 more
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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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.