A Bayesian Model for Highly Accelerated Phase-Contrast MRI

作者：Rich Adam; Potter Lee C; Jin Ning; Ash Joshua; Simonetti Orlando P; Ahmad Rizwan^*

来源：Magnetic Resonance in Medicine, 2016, 76(2): 689-701.

DOI：10.1002/mrm.25904

• flow imaging
• approximate message passing
• minimum mean squared error estimation
• cardiac MRI
• peak blood flow
• velocity
• Bayesian inference
• factor graph

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 encodings. A fast iterative recovery algorithm is introduced based on message passing. For validation, prospectively undersampled data are processed from a pulsatile flow phantom and five healthy volunteers.
Results: The proposed approach is in good agreement, quantified by peak velocity and stroke volume (SV), with reference data for acceleration rates R <= 10. For SV, Pearson r >= 0.99 for phantom imaging (n = 24) and r >= 0.96 for prospectively accelerated in vivo imaging (n = 10) for R <= 10.
Conclusion: The proposed approach enables accurate quantification of blood flow from highly undersampled data. The technique is extensible to four-dimensional flow imaging, where higher acceleration may be possible due to additional redundancy.

• 出版日期2016-8