A Bayesian model for highly accelerated phase-contrast MRI.

ABSTRACT:

Purpose

Phase-contrast magnetic resonance imaging is a noninvasive tool to assess cardiovascular disease by quantifying blood flow; however, low data acquisition efficiency limits the spatial and temporal resolutions, real-time application, and extensions to four-dimensional flow imaging in clinical settings. We propose a new data processing approach called Reconstructing Velocity Encoded MRI with Approximate message passing aLgorithms (ReVEAL) that accelerates the acquisition by exploiting data structure unique to phase-contrast magnetic resonance imaging.

Theory and methods

The proposed approach models physical correlations across space, time, and velocity encodings. The proposed Bayesian approach exploits the relationships in both magnitude and phase among velocity encodi

SUBMITTER: Rich A 

PROVIDER: S-EPMC4824680 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature