Robust quantitative susceptibility mapping via approximate message passing with parameter estimation.

ABSTRACT:

Purpose

For quantitative susceptibility mapping (QSM), the lack of ground-truth in clinical settings makes it challenging to determine suitable parameters for the dipole inversion. We propose a probabilistic Bayesian approach for QSM with built-in parameter estimation, and incorporate the nonlinear formulation of the dipole inversion to achieve a robust recovery of the susceptibility maps.

Theory

From a Bayesian perspective, the image wavelet coefficients are approximately sparse and modeled by the Laplace distribution. The measurement noise is modeled by a Gaussian-mixture distribution with two components, where the second component is used to model the noise outliers. Through probabilistic inference, the susceptibility map and distribution parameters can be jointly recover

SUBMITTER: Huang S 

PROVIDER: S-EPMC10664815 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature