Improved estimation of myelin water fractions with learned parameter distributions.

ABSTRACT:

Purpose

To improve estimation of myelin water fraction (MWF) in the brain from multi-echo gradient-echo imaging data.

Methods

A systematic sensitivity analysis was first conducted to characterize the conventional exponential models used for MWF estimation. A new estimation method was then proposed for improved estimation of MWF from practical gradient-echo imaging data. The proposed method uses an extended signal model that includes a finite impulse response filter to compensate for practical signal variations. This new model also enables the use of prelearned parameter distributions as well as low-rank signal structures to improve parameter estimation. The resulting parameter estimation problem was solved optimally in the Bayesian sense.

Results

Our sensitivity analy

SUBMITTER: Li Y 

PROVIDER: S-EPMC8530830 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature