Bayesian estimation of multicomponent relaxation parameters in magnetic resonance fingerprinting.

ABSTRACT:

Purpose

To estimate multiple components within a single voxel in magnetic resonance fingerprinting when the number and types of tissues comprising the voxel are not known a priori.

Theory

Multiple tissue components within a single voxel are potentially separable with magnetic resonance fingerprinting as a result of differences in signal evolutions of each component. The Bayesian framework for inverse problems provides a natural and flexible setting for solving this problem when the tissue composition per voxel is unknown. Assuming that only a few entries from the dictionary contribute to a mixed signal, sparsity-promoting priors can be placed upon the solution.

Methods

An iterative algorithm is applied to compute the maximum a posteriori estimator of the posterior pro

SUBMITTER: McGivney D 

PROVIDER: S-EPMC5876128 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature