Journal ArticleDOI
Parametric and non-parametric statistical analysis of DT-MRI data.
Sinisa Pajevic,Peter J. Basser +1 more
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TLDR
A Multivariate Normal Distribution is proposed as a parametric statistical model of diffusion tensor data when magnitude MR images contain no artifacts other than Johnson noise, and this model is evaluated using Monte Carlo simulations of DT-MRI experiments.About:
This article is published in Journal of Magnetic Resonance.The article was published on 2003-03-01. It has received 173 citations till now. The article focuses on the topics: Parametric statistics & Nonparametric statistics.read more
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White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI
TL;DR: The physics of DW-MRI is reviewed, currently preferred methodology is indicated, and the limits of interpretation of its results are explained, with a list of 'Do's and Don'ts' which define good practice in this expanding area of imaging neuroscience.
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The B-matrix must be rotated when correcting for subject motion in DTI data.
Alexander Leemans,Derek K. Jones +1 more
TL;DR: A systematic study to investigate the effect of neglecting to reorient the B‐matrix on DTI data during motion correction is presented and the consequences for diffusion fiber tractography are discussed.
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About "axial" and "radial" diffusivities.
TL;DR: Researchers should strongly discourage researchers from interpreting changes of the “axial’ and “radial” diffusivities on the basis of the underlying tissue structure, unless accompanied by a thorough investigation of their mathematical and geometrical properties in each dataset studied.
Journal ArticleDOI
The effect of filter size on VBM analyses of DT-MRI data.
TL;DR: The results suggest that, even with moderate smoothing, a large number of voxels within central white matter regions may have non-normally distributed residuals thus making valid statistical inferences with a parametric approach problematic in these areas.
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"Squashing peanuts and smashing pumpkins": how noise distorts diffusion-weighted MR data.
Derek K. Jones,Peter J. Basser +1 more
TL;DR: Several new artifacts that can be explained by considering how background noise affects the peanut‐shaped angular apparent diffusion coefficient (ADC) profile are reported, including an orientationally dependent deviation from Gaussian behavior of the ADC profile, an underestimation of indices of diffusion anisotropy, and a correlation between estimates of mean diffusivity and diffusion anIsotropy.
References
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Book
An introduction to the bootstrap
Bradley Efron,Robert Tibshirani +1 more
TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.
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Bootstrap Methods: Another Look at the Jackknife
TL;DR: In this article, the authors discuss the problem of estimating the sampling distribution of a pre-specified random variable R(X, F) on the basis of the observed data x.
Book
Data Reduction and Error Analysis for the Physical Sciences
TL;DR: In this paper, Monte Carlo techniques are used to fit dependent and independent variables least squares fit to a polynomial least-squares fit to an arbitrary function fitting composite peaks direct application of the maximum likelihood.
Journal ArticleDOI
Data Reduction and Error Analysis for the Physical Sciences.
TL;DR: Numerical methods matrices graphs and tables histograms and graphs computer routines in Pascal and Monte Carlo techniques dependent and independent variables least-squares fit to a polynomial least-square fit to an arbitrary function fitting composite peaks direct application of the maximum likelihood.
Book
Linear statistical inference and its applications
TL;DR: Algebra of Vectors and Matrices, Probability Theory, Tools and Techniques, and Continuous Probability Models.