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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Book ChapterDOI
Diffusion MRI Signal Augmentation: From Single Shell to Multi Shell with Deep Learning
TL;DR: A data-driven approach is presented that is able to augment single-shell signals to multi- shell signals based on Deep Neural Networks and Spherical Harmonics and performs equally well on both synthetic as well as real human brain data.
Journal ArticleDOI
A deep network for tissue microstructure estimation using modified LSTM units.
Chuyang Ye,Xiuli Li,Jingnan Chen +2 more
TL;DR: This work proposes a deep network structure that is motivated by the use of historical information in iterative optimization for tissue microstructure estimation, and such incorporation of historical Information has not been previously explored in the design of deep networks for microst structure estimation.
Journal ArticleDOI
Detection of aberrant hippocampal mossy fiber connections: Ex vivo mesoscale diffusion MRI and microtractography with histological validation in a patient with uncontrolled temporal lobe epilepsy
TL;DR: It is demonstrated that the ex vivo mesoscopic MRI of surgically excised hippocampi can bridge the explanatory and analytical gap between the macro‐ and microscopic scale, and suggests that there is indeed an aberrant connection between the DG and SM, supporting the sprouting hypothesis of a reverberant excitatory network.
Posted Content
Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement.
Ryutaro Tanno,Daniel E. Worrall,Enrico Kaden,Aurobrata Ghosh,Francesco Grussu,Alberto Bizzi,Stamatios N. Sotiropoulos,Antonio Criminisi,Daniel C. Alexander +8 more
TL;DR: Methods to characterise different components of uncertainty in medical image enhancement problems and demonstrate the ideas using diffusion MRI super-resolution to highlight three key benefits of uncertainty modelling for improving the safety of DL-based image enhancement systems.
Journal ArticleDOI
Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI
Vishwesh Nath,Kurt G. Schilling,Prasanna Parvathaneni,Colin B. Hansen,Allison E. Hainline,Yuankai Huo,Justin A. Blaber,Ilwoo Lyu,Vaibhav A. Janve,Yurui Gao,Iwona Stepniewska,Adam W. Anderson,Bennett A. Landman +12 more
TL;DR: This work highlights the ability of deep learning to capture linkages between ex-vivo ground truth data with feasible MRI sequences and results in intriguingly high reproducibility of orientation structure.
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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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
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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.