Publication

Deriving Brain Myelin Water Fraction Maps from Relaxometry: a Data-Driven Approach

Jean-Philippe Thiran, Jonas Richiardi
2019
Posters

Abstract

Currently, one of the gold-standard methods to obtain brain myelin water fraction (MWF) maps is the multi-echo spin-echo sequence. To overcome some of its limitations (e.g. long acquisition times), a data-driven approach for deriving MWF maps is proposed here. A general linear model (GLM) and a conditional generative adversarial network (cGAN) were trained to learn the reference MWF from T1 and T2 maps acquired in a healthy cohort. While GLM-derived maps exhibited MWF overestimation, especially in WM tissue, the cGAN yielded images in agreement with the reference. The proposed methods were preliminarily tested in patients and revealed myelin degradation in expected areas.

Official source

https://infoscience.epfl.ch/entities/publication/5ac73a9c-5a16-4dbb-b69e-ed250ed0bbb3

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Jean-Philippe Thiran, Jonas Richiardi
2019
Posters

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/5ac73a9c-5a16-4dbb-b69e-ed250ed0bbb3

About this result

Related concepts (23)

Generalized linear model

In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value. Generalized linear models were formulated by John Nelder and Robert Wedderburn as a way of unifying various other statistical models, including linear regression, logistic regression and Poisson regression.

General linear model

The general linear model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that sense it is not a separate statistical linear model. The various multiple linear regression models may be compactly written as where Y is a matrix with series of multivariate measurements (each column being a set of measurements on one of the dependent variables), X is a matrix of observations on independent variables that might be a design matrix (each column being a set of observations on one of the independent variables), B is a matrix containing parameters that are usually to be estimated and U is a matrix containing errors (noise).

Linear model

In statistics, the term linear model is used in different ways according to the context. The most common occurrence is in connection with regression models and the term is often taken as synonymous with linear regression model. However, the term is also used in time series analysis with a different meaning. In each case, the designation "linear" is used to identify a subclass of models for which substantial reduction in the complexity of the related statistical theory is possible.

Related publications (1)

Deriving Brain Myelin Water Fraction Maps from Relaxometry: a Data-Driven Approach

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