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Journal ArticleDOI

Estimation of relaxation time distributions in magnetic resonance imaging

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TLDR
In this paper, the authors developed a new technique to estimate the integral of the distribution of T2 relaxation time without imposing any constraint other than the monotonicity of the underlying cumulative relaxation time distribution.
Abstract
Magnetic resonance imaging techniques can be used to measure some biophysical properties of tissue. In this context, the T2 relaxation time is an important parameter for soft-tissue contrast. The authors develop a new technique to estimate the integral of the distribution of T2 relaxation time without imposing any constraint other than the monotonicity of the underlying cumulative relaxation time distribution. They explore the properties of the estimation and its applications for the analysis of breast tissue data. As they show, an extension of linear discriminant analysis is found to distinguish well between two classes of breast tissue. Estimation de la loi du temps de decontraction en imagerie par resonance magnetique Les techniques d'imagerie par resonance magnetique permettent de mesurer certaines proprietes biophysiques des tissus. Dans ce contexte, le temps de decontraction T2 est un parametre important pour l'identification des tissus mous. Les auteurs proposent une nouvelle technique d'estimation de l'integrate de la loi du temps de decontraction T2 sans imposer d'autres contraintes que la monotonicite de la fonction de repartition de la variable sous-jacente. Ils explorent les proprietes de l'estimateur et montrent son utilite dans l'analyse de tissus mammaires. Comme ils le font valoir, une generalisation de l'analyse discriminante lineaire permet de distinguer nettement entre deux types de tissus mammaires.

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Citations
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Journal ArticleDOI

Modelling and Estimation of Multicomponent $T_{2}$ Distributions

TL;DR: A parsimonious parametric and continuous model based on a mixture of inverse-gamma distributions is introduced, which supports the notion that T2 spread is difficult, if not infeasible, to estimate from relaxometry data acquired with a typical clinical paradigm.
Dissertation

Advances in magnetic resonance imaging using statistical signal processing

Kelvin Layton
TL;DR: The present work examines the estimation of transverse relaxation rates for two cases and adopts a statistical signal processing framework, applied to the advancement of two emerging MRI technologies: quantitative MRI and nonlinear spatial encoding.
References
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Book

An introduction to the bootstrap

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.
Book

A practical guide to splines

Carl de Boor
TL;DR: This book presents those parts of the theory which are especially useful in calculations and stresses the representation of splines as linear combinations of B-splines as well as specific approximation methods, interpolation, smoothing and least-squares approximation, the solution of an ordinary differential equation by collocation, curve fitting, and surface fitting.
Journal ArticleDOI

A constrained regularization method for inverting data represented by linear algebraic or integral equations

TL;DR: CONTIN as discussed by the authors is a portable Fortran IV package for inverting noisy linear operator equations, which can be used for the analysis of data from a wide variety of experiments, including photon correlation spectroscopy, multicomponent spectra, and Fourier-Bessel, Fourier and Laplace transforms.
BookDOI

Nonparametric regression and generalized linear models

TL;DR: In this paper, the authors propose that having more aspects to know and understand will lead to becoming a more precious person, and becoming more precious can be situated with the presentation of how your knowledge much.